mirror of
https://github.com/EyeTrackVR/EyeTrackVR.git
synced 2025-11-04 14:39:42 +08:00
1866 lines
85 KiB
Python
1866 lines
85 KiB
Python
import math
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import os
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import timeit
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from functools import lru_cache
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from logging import Formatter, INFO, StreamHandler,FileHandler, getLogger
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import cv2
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import numpy as np
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from numpy.linalg import _umath_linalg
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from EyeTrackApp.utils.img_utils import safe_crop
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from EyeTrackApp.utils.misc_utils import clamp
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from EyeTrackApp.utils.time_utils import FPSResult, TimeitResult, format_time
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# from line_profiler_pycharm import profile
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this_file_basename = os.path.basename(__file__)
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this_file_name = this_file_basename.replace(".py", "")
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alg_ver = "230314-1" # Do not change it.
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##############################
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# These can be changed
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old_mode = False
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save_logfile = False # This setting is disabled when imshow_enable or save_video is true
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imshow_enable = False
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save_video = False
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loop_num = 1 if imshow_enable or save_video else 100
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input_video_path = "Pro_demo2.mp4"
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output_video_path = f'./{this_file_name}_{alg_ver}_new.mp4' if not old_mode else f'./{this_file_name}_{alg_ver}_old.mp4'
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logfilename = f'./{this_file_name}_{alg_ver}_new.log' if not old_mode else f'./{this_file_name}_old.log'
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print_enable = False # I don't recommend changing to True.
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# RANSAC
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thresh_add = 10
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# calc_print_enable = True
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skip_autoradius = False
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skip_blink_detect = False
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##############################
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##############################
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# Do not change these.
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imsave_flg = imshow_enable or save_video
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# cache param
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lru_maxsize_vvs = 16
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lru_maxsize_vs = 64
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lru_maxsize_s = 128
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# CV param
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default_radius = 20
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auto_radius_range = (default_radius - 10, default_radius + 10) # (10,30)
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auto_radius_step = 1
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blink_init_frames = 60 * 3 # 60fps*3sec,Number of blink statistical frames
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# step==(x,y)
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default_step = (5, 5) # bigger the steps,lower the processing time! ofc acc also takes an impact
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logger = getLogger(__name__)
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logger.setLevel(INFO)
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formatter = Formatter('%(message)s')
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handler = StreamHandler()
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handler.setLevel(INFO)
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handler.setFormatter(formatter)
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logger.addHandler(handler)
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if save_logfile and not imsave_flg:
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handler = FileHandler(logfilename, encoding="utf8", mode="w")
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handler.setLevel(INFO)
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handler.setFormatter(formatter)
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logger.addHandler(handler)
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else:
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save_logfile = False
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video_wr = cv2.VideoWriter(output_video_path, cv2.VideoWriter_fourcc(*"x264"), 60.0, (200, 150)) if save_video else None
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##############################
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class AutoRadiusCalc(object):
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def __init__(self):
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self.response_list = []
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self.radius_cand_list = []
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self.adj_comp_flag = False
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self.radius_middle_index = None
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self.left_item = None
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self.right_item = None
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self.left_index = None
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self.right_index = None
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def get_radius(self):
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prev_res_len = len(self.response_list)
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# adjustment of radius
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if prev_res_len == 1:
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# len==1==response_list==[default_radius]
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self.adj_comp_flag = False
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return auto_radius_range[0]
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elif prev_res_len == 2:
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# len==2==response_list==[default_radius, auto_radius_range[0]]
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self.adj_comp_flag = False
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return auto_radius_range[1]
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elif prev_res_len == 3:
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# len==3==response_list==[default_radius,auto_radius_range[0],auto_radius_range[1]]
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if self.response_list[1][1] < self.response_list[2][1]:
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self.left_item = self.response_list[1]
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self.right_item = self.response_list[0]
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else:
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self.left_item = self.response_list[0]
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self.right_item = self.response_list[2]
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self.radius_cand_list = [i for i in range(self.left_item[0], self.right_item[0] + auto_radius_step, auto_radius_step)]
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self.left_index = 0
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self.right_index = len(self.radius_cand_list) - 1
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self.radius_middle_index = (self.left_index + self.right_index) // 2
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self.adj_comp_flag = False
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return self.radius_cand_list[self.radius_middle_index]
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else:
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if self.left_index <= self.right_index and self.left_index != self.radius_middle_index:
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if (self.left_item[1] + self.response_list[-1][1]) < (self.right_item[1] + self.response_list[-1][1]):
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self.right_item = self.response_list[-1]
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self.right_index = self.radius_middle_index - 1
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self.radius_middle_index = (self.left_index + self.right_index) // 2
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self.adj_comp_flag = False
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return self.radius_cand_list[self.radius_middle_index]
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if (self.left_item[1] + self.response_list[-1][1]) > (self.right_item[1] + self.response_list[-1][1]):
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self.left_item = self.response_list[-1]
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self.left_index = self.radius_middle_index + 1
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self.radius_middle_index = (self.left_index + self.right_index) // 2
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self.adj_comp_flag = False
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return self.radius_cand_list[self.radius_middle_index]
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self.adj_comp_flag = True
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return self.radius_cand_list[self.radius_middle_index]
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def get_radius_base(self):
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"""
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Use it when the new version doesn't work well.
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:return:
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"""
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prev_res_len = len(self.response_list)
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# adjustment of radius
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if prev_res_len == 1:
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# len==1==response_list==[default_radius]
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self.adj_comp_flag = False
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return auto_radius_range[0]
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elif prev_res_len == 2:
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# len==2==response_list==[default_radius, auto_radius_range[0]]
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self.adj_comp_flag = False
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return auto_radius_range[1]
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elif prev_res_len == 3:
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# len==3==response_list==[default_radius,auto_radius_range[0],auto_radius_range[1]]
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sort_res = sorted(self.response_list, key=lambda x: x[1])[0]
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# Extract the radius with the lowest response value
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if sort_res[0] == default_radius:
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# If the default value is best, change now_mode to init after setting radius to the default value.
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self.adj_comp_flag = True
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return default_radius
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elif sort_res[0] == auto_radius_range[0]:
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self.radius_cand_list = [i for i in range(auto_radius_range[0], default_radius, auto_radius_step)][1:]
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self.adj_comp_flag = False
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return self.radius_cand_list.pop()
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else:
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self.radius_cand_list = [i for i in range(default_radius, auto_radius_range[1], auto_radius_step)][1:]
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self.adj_comp_flag = False
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return self.radius_cand_list.pop()
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else:
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# Try the contents of the radius_cand_list in order until the radius_cand_list runs out
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# Better make it a binary search.
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if len(self.radius_cand_list) == 0:
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sort_res = sorted(self.response_list, key=lambda x: x[1])[0]
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self.adj_comp_flag = True
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return sort_res[0]
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else:
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self.adj_comp_flag = False
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return self.radius_cand_list.pop()
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def add_response(self, radius, response):
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self.response_list.append((radius, response))
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return None
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class BlinkDetector(object):
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def __init__(self):
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self.response_list = []
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self.response_max = None
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self.enable_detect_flg = False
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self.quartile_1 = None
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def calc_thresh(self):
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# Calculate response_max by computing interquartile range, IQR
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# self.response_listo = np.array(self.response_listo)
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# 25%,75%
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# This value may need to be adjusted depending on the environment.
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# quartile_1, quartile_3 = np.percentile(self.response_listo, [25, 75])
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# iqr = quartile_3 - quartile_1
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# self.response_maxo = quartile_3 + (iqr * 1.5)
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# quartile_1, quartile_3 = np.percentile(self.response_list, [25, 75])
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# or
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quartile_1, quartile_3 = np.percentile(np.array(self.response_list), [25, 75])
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self.quartile_1 = quartile_1
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iqr = quartile_3 - quartile_1
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# response_min = quartile_1 - (iqr * 1.5)
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self.response_max = float(quartile_3 + (iqr * 1.5))
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# or
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# self.response_max = quartile_3 + (iqr * 1.5)
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self.enable_detect_flg = True
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return None
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def detect(self, now_response):
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return now_response > self.response_max
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def add_response(self, response):
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self.response_list.append(response)
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return None
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def response_len(self):
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return len(self.response_list)
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def ellipse_model(data, y, f):
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"""
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There is no need to make this process a function, since making the process a function will slow it down a little by calling it.
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The results may be slightly different from the lambda version due to calculation errors derived from float types, but the calculation results are virtually the same.
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a = 1.0,b = P[0],c = P[1],d = P[2],e = P[3],f = P[4]
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:param data:
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:param y: np.c_[d, e, a, c, b]
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:param f: f == P[4, 0]
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:return: this_return == np.array([ellipse_model(x, y) for (x, y) in data ])
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"""
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return data.dot(y) + f
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def fit_rotated_ellipse_ransac_old(data: np.ndarray, rng: np.random.Generator, iter=100, sample_num=10,
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offset=80): # before changing these values, please read up on the ransac algorithm
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# However if you want to change any value just know that higher iterations will make processing frames slower
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effective_sample = None
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# The array contents do not change during the loop, so only one call is needed.
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# They say len is faster than shape.
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# Reference url: https://stackoverflow.com/questions/35547853/what-is-faster-python3s-len-or-numpys-shape
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len_data = len(data)
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if len_data < sample_num:
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return None
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# Type of calculation result
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ret_dtype = np.float64
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# Sorts a random number array of size (iter,len_data). After sorting, returns the index of sample_num random numbers before sorting.
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# If the array size is less than about 100, this is faster than rng.choice.
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rng_sample = rng.random((iter, len_data)).argsort()[:, :sample_num]
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# or
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# I don't see any advantage to doing this.
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# rng_sample = np.asarray(rng.random((iter, len_data)).argsort()[:, :sample_num], dtype=np.int32)
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# I don't think it looks beautiful.
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# x,y,x**2,y**2,x*y,1,-1*x**2
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datamod = np.concatenate(
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[data, data ** 2, (data[:, 0] * data[:, 1])[:, np.newaxis], np.ones((len_data, 1), dtype=ret_dtype),
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(-1 * data[:, 0] ** 2)[:, np.newaxis]], axis=1,
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dtype=ret_dtype)
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datamod_slim = np.array(datamod[:, :5], dtype=ret_dtype)
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datamod_rng = datamod[rng_sample]
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datamod_rng6 = datamod_rng[:, :, 6]
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datamod_rng_swap = datamod_rng[:, :, [4, 3, 0, 1, 5]]
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datamod_rng_swap_trans = datamod_rng_swap.transpose((0, 2, 1))
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# These two lines are one of the bottlenecks
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datamod_rng_5x5 = np.matmul(datamod_rng_swap_trans, datamod_rng_swap)
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datamod_rng_p5smp = np.matmul(np.linalg.inv(datamod_rng_5x5), datamod_rng_swap_trans)
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datamod_rng_p = np.matmul(datamod_rng_p5smp, datamod_rng6[:, :, np.newaxis]).reshape((-1, 5))
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# I don't think it looks beautiful.
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ellipse_y_arr = np.asarray(
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[datamod_rng_p[:, 2], datamod_rng_p[:, 3], np.ones(len(datamod_rng_p)), datamod_rng_p[:, 1], datamod_rng_p[:, 0]], dtype=ret_dtype)
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ellipse_data_arr = ellipse_model(datamod_slim, ellipse_y_arr, np.asarray(datamod_rng_p[:, 4])).transpose((1, 0))
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ellipse_data_abs = np.abs(ellipse_data_arr)
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ellipse_data_index = np.argmax(np.sum(ellipse_data_abs < offset, axis=1), axis=0)
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effective_data_arr = ellipse_data_arr[ellipse_data_index]
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effective_sample_p_arr = datamod_rng_p[ellipse_data_index]
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return fit_rotated_ellipse_old(effective_data_arr, effective_sample_p_arr)
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# @profile
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def fit_rotated_ellipse_old(data, P):
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a = 1.0
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b = P[0]
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c = P[1]
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d = P[2]
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e = P[3]
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f = P[4]
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# The cost of trigonometric functions is high.
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theta = 0.5 * np.arctan(b / (a - c), dtype=np.float64)
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theta_sin = np.sin(theta, dtype=np.float64)
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theta_cos = np.cos(theta, dtype=np.float64)
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tc2 = theta_cos ** 2
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ts2 = theta_sin ** 2
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b_tcs = b * theta_cos * theta_sin
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# Do the calculation only once
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cxy = b ** 2 - 4 * a * c
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cx = (2 * c * d - b * e) / cxy
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cy = (2 * a * e - b * d) / cxy
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# I just want to clear things up around here.
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cu = a * cx ** 2 + b * cx * cy + c * cy ** 2 - f
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cu_r = np.array([(a * tc2 + b_tcs + c * ts2), (a * ts2 - b_tcs + c * tc2)])
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wh = np.sqrt(cu / cu_r)
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w, h = wh[0], wh[1]
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error_sum = np.sum(data)
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# print("fitting error = %.3f" % (error_sum))
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return (cx, cy, w, h, theta)
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@lru_cache(maxsize=lru_maxsize_vs)
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def get_ransac_empty_array_lendata(len_data,iter_num, sample_num):
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# Function to reduce array allocation by providing an empty array first and recycling it with lru
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use_dtype=np.float64
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datamod = np.empty((len_data, 7), dtype=use_dtype) # np.empty((len(data), 7), dtype=ret_dtype)
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datamod[:, 5] = 1
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datamod_b=datamod[:, :5]#.T
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random_index_init_arr = np.empty((iter_num, len_data), dtype=np.uint16)
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random_index_init_arr[:, :] = np.arange(len_data, dtype=np.uint16)
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random_index = np.empty((iter_num, len_data), dtype=np.uint16)
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random_index_samplenum=random_index[:, :sample_num]
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ellipse_data_arr=np.empty((iter_num,len_data),dtype=use_dtype)
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th_abs=np.empty((iter_num,len_data),dtype=use_dtype)
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dm_data_view=datamod[:, :2]# = data
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dm_p2_view=datamod[:, 2:4]# = data * data
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dm_mul_view=datamod[:, 4]# = data[:, 0] * data[:, 1]
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dm_neg_view=datamod[:, 6]# = -datamod[:, 2]
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# return datamod,random_index_init_arr,random_index,ellipse_data_arr,th_abs
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return datamod,datamod_b,dm_data_view,dm_p2_view,dm_mul_view,dm_neg_view, random_index_init_arr, random_index,random_index_samplenum, ellipse_data_arr, th_abs
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@lru_cache(maxsize=lru_maxsize_s)
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def get_ransac_empty_array_iternum_samplenum(iter_num, sample_num,len_data):
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# Function to reduce array allocation by providing an empty array first and recycling it with lru
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use_dtype=np.float64
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datamod_rng=np.empty((iter_num,sample_num,7),dtype=use_dtype)
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datamod_rng_swap = np.empty((iter_num, sample_num, 5), dtype=use_dtype)
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datamod_rng_swap_trans=datamod_rng_swap.transpose((0,2,1))
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# datamod_rng_swap_trans = np.empty((iter_num, 5,sample_num), dtype=use_dtype)
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datamod_rng_5x5= np.empty((iter_num, 5,5), dtype=use_dtype)
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datamod_rng_p5smp = np.empty((iter_num, 5,sample_num), dtype=use_dtype)
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datamod_rng_p=np.empty((iter_num,5),dtype=use_dtype)
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datamod_rng_p_npaxis=datamod_rng_p[:,:,np.newaxis]
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ellipse_y_arr=np.empty((iter_num,5),dtype=use_dtype)
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ellipse_y_arr[:, 2] = 1
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swap_index=np.array([4, 3, 0, 1, 5])
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dm_brod=np.broadcast_to(datamod_rng_p[:, 4, np.newaxis], (iter_num, len_data))
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dm_rng_six=datamod_rng[:, :, 6, np.newaxis]
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dm_rng_p_24_view= datamod_rng_p[:, 2:4]
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dm_rng_p_10_view= datamod_rng_p[:, 1::-1]
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el_y_arr_2_view=ellipse_y_arr[:, :2]
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el_y_arr_3_view=ellipse_y_arr[:, 3:]
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return datamod_rng,datamod_rng_swap,datamod_rng_swap_trans,datamod_rng_5x5,datamod_rng_p5smp,datamod_rng_p,datamod_rng_p_npaxis,ellipse_y_arr,swap_index,dm_brod,dm_rng_six,dm_rng_p_24_view,dm_rng_p_10_view,el_y_arr_2_view,el_y_arr_3_view
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# @profile
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def fit_rotated_ellipse_ransac(data: np.ndarray, sfc: np.random.Generator, iter_num=100, sample_num=10, offset=80 # 80.0, 10, 80
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): # before changing these values, please read up on the ransac algorithm
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# However if you want to change any value just know that higher iterations will make processing frames slower
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# The array contents do not change during the loop, so only one call is needed.
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# They say len is faster than shape.
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# Reference url: https://stackoverflow.com/questions/35547853/what-is-faster-python3s-len-or-numpys-shape
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len_data = len(data)
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if len_data < sample_num:
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return None
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# Type of calculation result
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# ret_dtype = np.float64
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# todo:create view
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datamod_rng, datamod_rng_swap, datamod_rng_swap_trans, datamod_rng_5x5, datamod_rng_p5smp, datamod_rng_p,datamod_rng_p_npaxis, ellipse_y_arr,swap_index,dm_brod,dm_rng_six,dm_rng_p_24_view,dm_rng_p_10_view,el_y_arr_2_view,el_y_arr_3_view=get_ransac_empty_array_iternum_samplenum(iter_num,sample_num,len_data)
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datamod,datamod_b,dm_data_view,dm_p2_view,dm_mul_view,dm_neg_view,random_index_init_arr,random_index,random_index_samplenum,ellipse_data_arr,th_abs=get_ransac_empty_array_lendata(len_data,iter_num,sample_num)
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# Sorts a random number array of size (iter,len_data). After sorting, returns the index of sample_num random numbers before sorting.
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# If the array size is less than about 100, this is faster than rng.choice.
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# rng_sample = rng.random((iter_num, len_data),dtype=np.float32).argsort()[:, :sample_num]#out=
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# or
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# I don't see any advantage to doing this.
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# rng_sample = np.asarray(rng.random((iter, len_data)).argsort()[:, :sample_num], dtype=np.int32)
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# I don't think it looks beautiful.
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# x,y,x**2,y**2,x*y,1,-1*x**2
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# datamod = np.concatenate(
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# [data, data ** 2, (data[:, 0] * data[:, 1])[:, np.newaxis], np.ones((len_data, 1), dtype=ret_dtype),
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# (-1 * data[:, 0] ** 2)[:, np.newaxis]], axis=1,
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# dtype=ret_dtype)
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# I don't think it looks beautiful.
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# x,y,x**2,y**2,x*y,1,-1*x**2
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# data_squared = np.square(data)
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# datamod = np.empty((len(data), 7), dtype=ret_dtype) # np.empty((len(data), 7), dtype=ret_dtype)
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# datamod[:, :2] = data#[:]
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# # datamod[:, 2:4] = np.square(data) # or data**2
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# # np.square(data,out=datamod[:, 2:4])#casting,dtype
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# datamod[:, 2:4] = data * data
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# datamod[:, 4] = data[:, 0] * data[:, 1]
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# # datamod[:, 4]=data.prod(axis=1,dtype=np.float64)
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# # datamod[:, 5] = 1
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# datamod[:, 6] = -datamod[:, 2] # -1 * data[:, 0] ** 2#
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dm_data_view[:, :] = data#[:]
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dm_p2_view[:,:] = data * data
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dm_mul_view[:] = data[:, 0] * data[:, 1]
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dm_neg_view[:] = -dm_p2_view[:,0] # -1 * data[:, 0] ** 2#
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# datamod_slim = np.array(datamod[:, :5], dtype=ret_dtype)
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#
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# datamod_rng = datamod[rng_sample]
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# datamod_rng = rng.choice(datamod, (iter, sample_num), replace=True, shuffle=False)
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# datamod_rng = datamod[rng.choice(len_data, (iter,len_data),shuffle=False)[:,:sample_num]]
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# random_index = np.empty((iter_num, len_data), dtype=np.uint16)
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# random_index[:, :] = np.arange(len_data, dtype=np.uint16)
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# random_index_bro = np.broadcast_to(np.arange(len_data, dtype=np.uint16), (iter_num, len_data))
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# or
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# random_index = np.zeros(100,dtype=np.uint16).reshape((-1,1))+ np.arange(len_data, dtype=np.uint16).reshape((1, len_data))
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# sfc.permuted(random_index, axis=1, out=random_index)
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sfc.permuted(random_index_init_arr, axis=1, out=random_index)
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# random_index = sfc.permuted(np.broadcast_to(np.arange(len_data, dtype=np.uint16), (iter_num, len_data)),axis=1)
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# datamod_rng = datamod[random_index[:,:sample_num]]#take
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# np.take replaces a[ind,:] and is 3-4 times faster, https://gist.github.com/rossant/4645217
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# datamod_rng = np.take(datamod,random_index[:,:sample_num],axis=0)
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# datamod_rng = datamod.take(random_index[:, :sample_num], axis=0, mode="clip")# iter_num,sample_num,7
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# datamod.take(random_index[:, :sample_num], axis=0, mode="clip",out=datamod_rng) # iter_num,sample_num,7
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datamod.take(random_index_samplenum, axis=0, mode="clip", out=datamod_rng)
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# datamod_rng = datamod[rng_sample]#out=
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# datamod_rng_swap = datamod_rng[:, :, [4, 3, 0, 1, 5]] # out= # iter_num,sample_num,5
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# datamod_rng_swap[:,:,:] = datamod_rng[:, :, [4, 3, 0, 1, 5]] # out= # iter_num,sample_num,
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datamod_rng.take(swap_index, axis=2,mode="clip",out=datamod_rng_swap)
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# or
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# datamod_rng_swap = np.take(datamod_rng,[4, 3, 0, 1, 5],axis=2)
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# datamod_rng_swap_trans = datamod_rng_swap.transpose((0, 2, 1)) # out=
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# datamod_rng_swap_trans[:,:,:] = datamod_rng_swap.transpose((0, 2, 1))#.copy() # out=
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#
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# # These two lines are one of the bottlenecks
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# datamod_rng_5x5 = np.matmul(datamod_rng_swap_trans, datamod_rng_swap)
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# datamod_rng_p5smp = np.matmul(np.linalg.inv(datamod_rng_5x5), datamod_rng_swap_trans)
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# datamod_rng_p5smp = np.matmul(np.linalg.inv(np.matmul(datamod_rng_swap_trans, datamod_rng_swap)), datamod_rng_swap_trans)
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np.matmul(datamod_rng_swap_trans, datamod_rng_swap,out=datamod_rng_5x5)
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# I want to use cv2.mulTransposed, but for some reason the results are different and it can only use 1-channel arrays.
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# np.linalg.inv(datamod_rng_5x5)
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# datamod_rng_5x5[:,:,:]=np.linalg.inv(datamod_rng_5x5)
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# _umath_linalg.inv(datamod_rng_5x5,out=datamod_rng_5x5)# check error
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# https://github.com/bogovicj/JaneliaMLCourse/issues/1
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# solve is slow
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# np.linalg.solve(np.matmul(datamod_rng_swap_trans, datamod_rng_swap), datamod_rng_swap_trans)
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_umath_linalg.inv(datamod_rng_5x5, signature='d->d', extobj=np.linalg.linalg.get_linalg_error_extobj(np.linalg.linalg._raise_linalgerror_singular),out=datamod_rng_5x5)
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np.matmul(datamod_rng_5x5, datamod_rng_swap_trans,out=datamod_rng_p5smp)
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# global ein_path
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# if ein_path is None:
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# ein_path = np.einsum_path("ijk,ijl->ikl", datamod_rng_swap, datamod_rng_swap, optimize='optimal')[0]#'optimal','greedy'
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# datamod_rng_p5smp = np.matmul(np.linalg.inv(np.einsum("ijk,ijl->ikl", datamod_rng_swap, datamod_rng_swap,casting="no",optimize=ein_path)), datamod_rng_swap_trans)
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# # np.einsum('ijk,ilk->ijl', dataswap_trans, dataswap)
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#
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# datamod_rng_p = np.matmul(datamod_rng_p5smp, datamod_rng[:, :, 6, np.newaxis]).reshape((-1, 5)) # out= # iter_num,5
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# datamod_rng_p[:,:]=np.matmul(datamod_rng_p5smp, datamod_rng[:, :, 6, np.newaxis]).reshape((-1, 5)) # out= # iter_num,5
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np.matmul(datamod_rng_p5smp, dm_rng_six,out=datamod_rng_p_npaxis)
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#
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# # I don't think it looks beautiful.
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# ellipse_y_arr = np.asarray(
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# [datamod_rng_p[:, 2], datamod_rng_p[:, 3], np.ones(iter_num,dtype=ret_dtype), datamod_rng_p[:, 1], datamod_rng_p[:, 0]], dtype=ret_dtype)
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# ellipse_y_arr = np.empty((iter_num, 5), dtype=ret_dtype)
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# ellipse_y_arr[0,:]=datamod_rng_p[:, 2]
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# ellipse_y_arr[1, :]=datamod_rng_p[:, 3]
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# ellipse_y_arr[:, :2] = datamod_rng_p[:, 2:4]
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# # ellipse_y_arr[:, 2] = 1
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# ellipse_y_arr[:, 3:] = datamod_rng_p[:, 1::-1]
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el_y_arr_2_view[:,:]= dm_rng_p_24_view#datamod_rng_p[:, 2:4]
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el_y_arr_3_view[:,:] = dm_rng_p_10_view#datamod_rng_p[:, 1::-1]
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# ellipse_y_arr[:,3:]=datamod_rng_p[:,1]
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# ellipse_y_arr[4,:]=datamod_rng_p[:,0]
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# ellipse_data_arr = np.asarray(ellipse_model(datamod[:, :5], ellipse_y_arr.T, np.asarray(datamod_rng_p[:, 4])).transpose((1, 0)))
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# ellipse_data_arr = datamod[:, :5].dot(ellipse_y_arr.T)+np.asarray(datamod_rng_p[:, 4])
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# ellipse_data_arr = np.matmul(ellipse_y_arr, datamod[:, :5].T) + np.asarray(datamod_rng_p[:, 4, np.newaxis])# iter_num,len_data
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# np.matmul(ellipse_y_arr, datamod_t,out=ellipse_data_arr)
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# ellipse_y_arr.dot(datamod[:, :5].T, out=ellipse_data_arr)
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# ellipse_data_arr+=datamod_rng_p[:, 4, np.newaxis]#np.asarray(datamod_rng_p[:, 4, np.newaxis])
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# cv2.gemm is slower and for some reason the src3 argument for addition is not available
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cv2.gemm(ellipse_y_arr,datamod_b,1.0,dm_brod,1.0,dst=ellipse_data_arr,flags=cv2.GEMM_2_T)
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# ellipse_data_arr[:,:]=scipy.linalg.blas.dgemm(alpha=1.0, a=ellipse_y_arr, b=datamod_b, beta=1.0, c=np.broadcast_to(datamod_rng_p[:, 4, np.newaxis], (iter_num, len_data)),trans_b=True, overwrite_c=False)
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|
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# ellipse_data_arr=ellipse_y_arr.dot(datamod[:, :5].T) + np.asarray(datamod_rng_p[:, 4, np.newaxis])
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# ellipse_data_arr =ellipse_data_arr.transpose((1, 0))
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# ellipse_data_arr = np.einsum("ij,kj->ki",np.asarray(datamod[:, :5]),ellipse_y_arr)+np.asarray(datamod_rng_p[:, 4,np.newaxis])
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|
|
# Q, R = np.linalg.qr(datamod_rng_swap_trans)
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|
# # datarng_T = datamod_rng.transpose((0, 2, 1))
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# Qtb = Q @ datamod_rng[:, :, 6].reshape((iter_num,5,5))
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# p = np.linalg.solve(R, Qtb)
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|
# Q, R = np.linalg.qr(datamod_rng_swap)#, mode='raw')
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# Qtb = Q.transpose((0, 2, 1)) @ datamod_rng[:, :, 6, np.newaxis]
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# p = np.linalg.solve(R, Qtb.reshape((-1, 5)))
|
|
# hoge=datamod_rng_swap.transpose((0, 2, 1))@datamod_rng_swap
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# inv_h=np.linalg.inv(hoge)
|
|
# D, U = np.linalg.eigh(hoge)
|
|
# Ap = (U * np.sqrt(D)).T
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|
|
|
# smp=np.linalg.solve(R, Q.transpose((0, 2, 1)))
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|
# smp= np.matmul(np.linalg.inv(R), Q.transpose((0, 2, 1)))
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# datamod_rng_p=np.matmul(smp, datamod_rng[:, :,6, np.newaxis]).reshape((-1, 5))
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|
# ellipse_y_arr = np.asarray([datamod_rng_p[:,2], datamod_rng_p[:,3], np.ones(iter_num), datamod_rng_p[:,1], datamod_rng_p[:,0]], dtype=ret_dtype)
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|
# ellipse_data_arr = datamod[:, :5].dot(ellipse_y_arr) + datamod_rng_p[:,4]
|
|
# ellipse_data_arr = ellipse_data_arr.transpose((1, 0))
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|
|
|
# ellipse_data_abs = np.abs(ellipse_data_arr)
|
|
# ellipse_data_abs = cv2.absdiff(ellipse_data_arr, 0)
|
|
# ellipse_data_index = np.argmax(np.sum(ellipse_data_abs < offset, axis=1), axis=0)
|
|
# ellipse_data_index = np.argmax(cv2.reduce(cv2.threshold(ellipse_data_abs, offset, 1, cv2.THRESH_BINARY_INV)[1], 1, cv2.REDUCE_SUM).reshape(-1), axis=0)
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# ellipse_data_index = cv2.reduceArgMax(cv2.reduce((ellipse_data_abs < offset)*1.0, 1, cv2.REDUCE_SUM),axis=0)[0,0]
|
|
# ellipse_data_index = cv2.reduceArgMax(cv2.reduce(cv2.threshold(ellipse_data_abs, offset, 1, cv2.THRESH_BINARY_INV)[1], 1, cv2.REDUCE_SUM),axis=0)[0,0]
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# ellipse_data_index = np.einsum("ij->i", cv2.threshold(ellipse_data_abs, offset, 1, cv2.THRESH_BINARY_INV)[1]).argmax()
|
|
# ellipse_data_index = \
|
|
# cv2.minMaxLoc(cv2.reduce(cv2.threshold(np.abs(ellipse_data_arr), offset, 1, cv2.THRESH_BINARY_INV)[1], 1, cv2.REDUCE_SUM))[3][1]
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np.abs(ellipse_data_arr,out=th_abs)
|
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cv2.threshold(th_abs, offset, 1.0, cv2.THRESH_BINARY_INV,dst=th_abs)#[1]
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|
ellipse_data_index = \
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cv2.minMaxLoc(cv2.reduce(th_abs, 1, cv2.REDUCE_SUM))[3][1]
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# ellipse_data_index = np.linalg.norm(cv2.threshold(ellipse_data_abs, offset, 1, cv2.THRESH_BINARY_INV)[1],ord=0,axis=1).argmax()
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|
# if ellipse_data_index!=a:
|
|
# print()
|
|
# effective_data_arr = ellipse_data_arr[ellipse_data_index]
|
|
# error_num = ellipse_data_arr[ellipse_data_index].sum()
|
|
error_num = cv2.sumElems(ellipse_data_arr[ellipse_data_index])[0]
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|
effective_sample_p_arr = datamod_rng_p[ellipse_data_index].tolist()
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|
|
|
# if fit_rotated_ellipse(effective_data_arr.sum(), effective_sample_p_arr)!= fit_rotated_ellipse_base(effective_data_arr, effective_sample_p_arr):
|
|
# print()
|
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return fit_rotated_ellipse(error_num, effective_sample_p_arr)
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|
|
|
|
# @profile
|
|
def fit_rotated_ellipse(data, P):
|
|
# a = 1.0
|
|
# # b, c, d, e, f = P
|
|
# b, c, d, e, f = P[0], P[1], P[2], P[3], P[4]
|
|
# # b = P[0]
|
|
# # c = P[1]
|
|
# # d = P[2]
|
|
# # e = P[3]
|
|
# # f = P[4]
|
|
# # The cost of trigonometric functions is high.
|
|
# theta = 0.5 * math.atan2(b, a-c)# math.atan(b / (a - c))# #np.arctan(b / (a - c), dtype=np.float64)
|
|
# # theta_sin = np.sin(theta, dtype=np.float64)
|
|
# # theta_cos = np.cos(theta, dtype=np.float64)
|
|
# theta_sin, theta_cos = math.sin(theta), math.cos(theta)
|
|
# tc2 = theta_cos ** 2
|
|
# ts2 = theta_sin ** 2
|
|
# b_tcs = b * theta_cos * theta_sin
|
|
#
|
|
# # Do the calculation only once
|
|
# cxy = b ** 2 - 4 * a * c
|
|
# cx = (2 * c * d - b * e) / cxy
|
|
# cy = (2 * a * e - b * d) / cxy
|
|
#
|
|
# # I just want to clear things up around here.
|
|
# cu = a * cx ** 2 + b * cx * cy + c * cy ** 2 - f
|
|
# # cu_r = np.array([(a * tc2 + b_tcs + c * ts2), (a * ts2 - b_tcs + c * tc2)])
|
|
# # wh = np.sqrt(cu / cu_r)
|
|
#
|
|
# w = math.sqrt(cu/(a * tc2 + b_tcs + c * ts2))
|
|
# h = math.sqrt(cu/(a * ts2 - b_tcs + c * tc2))
|
|
|
|
a = 1.0
|
|
# b, c, d, e, f = P[0], P[1], P[2], P[3], P[4]
|
|
b, c, d, e = P[0], P[1], P[2], P[3]
|
|
theta = 0.5 * math.atan(b / (a - c)) # math.atan2(b, a - c)
|
|
theta_sin, theta_cos = math.sin(theta), math.cos(theta)
|
|
tc2 = theta_cos * theta_cos
|
|
ts2 = theta_sin * theta_sin
|
|
b_tcs = b * theta_cos * theta_sin
|
|
cxy = b * b - 4 * a * c
|
|
cx = (2 * c * d - b * e) / cxy
|
|
cy = (2 * a * e - b * d) / cxy
|
|
# cu = a * cx * cx + b * cx * cy + c * cy * cy - P[4]#f
|
|
cu = c * cy * cy + cx * (a * cx + b * cy) - P[4]
|
|
# here: https://stackoverflow.com/questions/327002/which-is-faster-in-python-x-5-or-math-sqrtx
|
|
# and : https://gist.github.com/zed/783011
|
|
w = math.sqrt(cu / (a * tc2 + b_tcs + c * ts2))
|
|
h = math.sqrt(cu / (a * ts2 - b_tcs + c * tc2))
|
|
|
|
# error_sum = data.sum()#sum(data)#np.sum(data)
|
|
# error_sum = data[0] + data[1] + data[2] + data[3] + data[4] + data[5] + data[6] + data[7] + data[8] + data[9] + data[10]
|
|
error_sum = data # sum(data)
|
|
# print("fitting error = %.3f" % (error_sum))
|
|
|
|
# cxy2 = P[0] * P[0] - 4 * a * P[1]
|
|
# theta2 = 0.5 * math.atan(P[0] / (a - P[1]))
|
|
# theta_sin2, theta_cos2 = math.sin(theta2), math.cos(theta2)
|
|
# tc22 = theta_cos2 * theta_cos2
|
|
# ts22 = theta_sin2 * theta_sin2
|
|
# b_tcs2 = P[0] * theta_cos2 * theta_sin2
|
|
# cx2 = (2 * P[1] * P[2] - P[0] * P[3]) / cxy2
|
|
# cy2 = (2 * a * P[3] - P[0] * P[2]) / cxy2
|
|
# cu2 = P[1] * cy2 * cy2 + cx2 * (a * cx2 + P[0] * cy2) - P[4]
|
|
# w2 = math.sqrt(cu2 / (a * tc22 + b_tcs2 + P[1] * ts22))
|
|
# h2 = math.sqrt(cu2 / (a * ts22 - b_tcs2 + P[1] * tc22))
|
|
|
|
return cx, cy, w, h, theta
|
|
|
|
|
|
class CvParameters_old:
|
|
# It may be a little slower because a dict named "self" is read for each function call.
|
|
def __init__(self, radius, step):
|
|
# self.prev_radius=radius
|
|
self._radius = radius
|
|
self.pad = 2 * radius
|
|
# self.prev_step=step
|
|
self._step = step
|
|
self._hsf = HaarSurroundFeature_old(radius)
|
|
|
|
def get_rpsh(self):
|
|
return self._radius, self.pad, self._step, self._hsf
|
|
# Essentially, the following would be preferable, but it would take twice as long to call.
|
|
# return self.radius, self.pad, self.step, self.hsf
|
|
|
|
@property
|
|
def radius(self):
|
|
return self._radius
|
|
|
|
@radius.setter
|
|
def radius(self, now_radius):
|
|
# self.prev_radius=self._radius
|
|
self._radius = now_radius
|
|
self.pad = 2 * now_radius
|
|
self.hsf = now_radius
|
|
|
|
@property
|
|
def step(self):
|
|
return self._step
|
|
|
|
@step.setter
|
|
def step(self, now_step):
|
|
# self.prev_step=self.step
|
|
self._step = now_step
|
|
|
|
@property
|
|
def hsf(self):
|
|
return self._hsf
|
|
|
|
@hsf.setter
|
|
def hsf(self, now_radius):
|
|
self._hsf = HaarSurroundFeature_old(now_radius)
|
|
|
|
|
|
class HaarSurroundFeature_old:
|
|
|
|
def __init__(self, r_inner, r_outer=None, val=None):
|
|
if r_outer is None:
|
|
r_outer = r_inner * 3
|
|
# print(r_outer)
|
|
r_inner2 = r_inner * r_inner
|
|
count_inner = r_inner2
|
|
count_outer = r_outer * r_outer - r_inner2
|
|
|
|
if val is None:
|
|
val_inner = 1.0 / r_inner2
|
|
val_outer = -val_inner * count_inner / count_outer
|
|
|
|
else:
|
|
val_inner = val[0]
|
|
val_outer = val[1]
|
|
|
|
self.val_in = np.array(val_inner, dtype=np.float64)
|
|
self.val_out = np.array(val_outer, dtype=np.float64)
|
|
self.r_in = r_inner
|
|
self.r_out = r_outer
|
|
|
|
def get_kernel(self):
|
|
# Defined here, but not yet used?
|
|
# Create a kernel filled with the value of self.val_out
|
|
kernel = np.ones(shape=(2 * self.r_out - 1, 2 * self.r_out - 1), dtype=np.float64) * self.val_out
|
|
|
|
# Set the values of the inner area of the kernel using array slicing
|
|
start = (self.r_out - self.r_in)
|
|
end = (self.r_out + self.r_in - 1)
|
|
kernel[start:end, start:end] = self.val_in
|
|
|
|
return kernel
|
|
|
|
@lru_cache(maxsize=lru_maxsize_vvs)
|
|
def get_hsf_empty_array_old(len_syx, frameint_x, frame_int_dtype, fcshape):
|
|
# Function to reduce array allocation by providing an empty array first and recycling it with lru
|
|
inner_sum = np.empty(len_syx, dtype=frame_int_dtype)
|
|
outer_sum = np.empty(len_syx, dtype=frame_int_dtype)
|
|
p_temp = np.empty((len_syx[0], frameint_x), dtype=frame_int_dtype)
|
|
p00 = np.empty(len_syx, dtype=frame_int_dtype)
|
|
p11 = np.empty(len_syx, dtype=frame_int_dtype)
|
|
p01 = np.empty(len_syx, dtype=frame_int_dtype)
|
|
p10 = np.empty(len_syx, dtype=frame_int_dtype)
|
|
response_list = np.empty(len_syx, dtype=np.float64)
|
|
frame_conv = np.zeros(shape=fcshape[0], dtype=np.uint8)
|
|
frame_conv_stride = frame_conv[::fcshape[1], ::fcshape[2]]
|
|
return (inner_sum, outer_sum), p_temp, (p00, p11, p01, p10), response_list, (frame_conv, frame_conv_stride)
|
|
|
|
|
|
@lru_cache(maxsize=lru_maxsize_vs)
|
|
def frameint_get_xy_step_old(imageshape, xysteps, pad, start_offset=None, end_offset=None):
|
|
"""
|
|
:param imageshape: (height(row),width(col)). row==y,cal==x
|
|
:param xysteps: (x,y)
|
|
:param pad: int
|
|
:param start_offset: (x,y) or None
|
|
:param end_offset: (x,y) or None
|
|
:return: xy_np:tuple(x,y)
|
|
"""
|
|
row, col = imageshape
|
|
row -= 1
|
|
col -= 1
|
|
x_step, y_step = xysteps
|
|
|
|
# This is not beautiful.
|
|
start_pad_x = start_pad_y = end_pad_x = end_pad_y = pad
|
|
|
|
if start_offset is not None:
|
|
start_pad_x += start_offset[0]
|
|
start_pad_y += start_offset[1]
|
|
if end_offset is not None:
|
|
end_pad_x += end_offset[0]
|
|
end_pad_y += end_offset[1]
|
|
y_np = np.arange(start_pad_y, row - end_pad_y, y_step)
|
|
x_np = np.arange(start_pad_x, col - end_pad_x, x_step)
|
|
|
|
xy_np = (x_np, y_np)
|
|
|
|
return xy_np
|
|
|
|
|
|
# @profile
|
|
def conv_int_old(frame_int, kernel, xy_step, padding, xy_steps_list):
|
|
"""
|
|
:param frame_int:
|
|
:param kernel: hsf
|
|
:param step: (x,y)
|
|
:param padding: int
|
|
:return:
|
|
"""
|
|
row, col = frame_int.shape
|
|
row -= 1
|
|
col -= 1
|
|
x_step, y_step = xy_step
|
|
# padding2 = 2 * padding
|
|
f_shape = row - 2 * padding, col - 2 * padding
|
|
r_in = kernel.r_in
|
|
|
|
len_sx, len_sy = len(xy_steps_list[0]), len(xy_steps_list[1])
|
|
inout_sum, p_temp, p_list, response_list, frameconvlist = get_hsf_empty_array_old((len_sy, len_sx), col + 1,
|
|
frame_int.dtype, (f_shape, y_step, x_step))
|
|
inner_sum, outer_sum = inout_sum
|
|
p00, p11, p01, p10 = p_list
|
|
frame_conv, frame_conv_stride = frameconvlist
|
|
|
|
y_rin_m = xy_steps_list[1] - r_in
|
|
x_rin_m = xy_steps_list[0] - r_in
|
|
y_rin_p = xy_steps_list[1] + r_in
|
|
x_rin_p = xy_steps_list[0] + r_in
|
|
# xx==(y,x),m==MINUS,p==PLUS, ex: mm==(y-,x-)
|
|
inarr_mm = frame_int[y_rin_m[0]:y_rin_m[-1] + 1:y_step, x_rin_m[0]:x_rin_m[-1] + 1:x_step]
|
|
inarr_mp = frame_int[y_rin_m[0]:y_rin_m[-1] + 1:y_step, x_rin_p[0]:x_rin_p[-1] + 1:x_step]
|
|
inarr_pm = frame_int[y_rin_p[0]:y_rin_p[-1] + 1:y_step, x_rin_m[0]:x_rin_m[-1] + 1:x_step]
|
|
inarr_pp = frame_int[y_rin_p[0]:y_rin_p[-1] + 1:y_step, x_rin_p[0]:x_rin_p[-1] + 1:x_step]
|
|
|
|
# == inarr_mm + inarr_pp - inarr_mp - inarr_pm
|
|
inner_sum[:, :] = inarr_mm
|
|
inner_sum += inarr_pp
|
|
inner_sum -= inarr_mp
|
|
inner_sum -= inarr_pm
|
|
|
|
# Bottleneck here, I want to make it smarter. Someone do it.
|
|
# (y,x)
|
|
# p00=max(y_ro_m,0),max(x_ro_m,0)
|
|
# p11=min(y_ro_p,ylim),min(x_ro_p,xlim)
|
|
# p01=max(y_ro_m,0),min(x_ro_p,xlim)
|
|
# p10=min(y_ro_p,ylim),max(x_ro_m,0)
|
|
y_ro_m = xy_steps_list[1] - kernel.r_out
|
|
x_ro_m = xy_steps_list[0] - kernel.r_out
|
|
y_ro_p = xy_steps_list[1] + kernel.r_out
|
|
x_ro_p = xy_steps_list[0] + kernel.r_out
|
|
# p00 calc
|
|
np.take(frame_int, y_ro_m, axis=0, mode="clip", out=p_temp)
|
|
np.take(p_temp, x_ro_m, axis=1, mode="clip", out=p00)
|
|
# p01 calc
|
|
np.take(p_temp, x_ro_p, axis=1, mode="clip", out=p01)
|
|
# p11 calc
|
|
np.take(frame_int, y_ro_p, axis=0, mode="clip", out=p_temp)
|
|
np.take(p_temp, x_ro_p, axis=1, mode="clip", out=p11)
|
|
# p10 calc
|
|
np.take(p_temp, x_ro_m, axis=1, mode="clip", out=p10)
|
|
# the point is this
|
|
# p00=np.take(np.take(frame_int, y_ro_m, axis=0, mode="clip"), x_ro_m, axis=1, mode="clip")
|
|
# p11=np.take(np.take(frame_int, y_ro_p, axis=0, mode="clip"), x_ro_p, axis=1, mode="clip")
|
|
# p01=np.take(np.take(frame_int, y_ro_m, axis=0, mode="clip"), x_ro_p, axis=1, mode="clip")
|
|
# p10=np.take(np.take(frame_int, y_ro_p, axis=0, mode="clip"), x_ro_m, axis=1, mode="clip")
|
|
|
|
outer_sum[:, :] = p00 + p11 - p01 - p10 - inner_sum
|
|
|
|
np.multiply(kernel.val_in, inner_sum, dtype=np.float64, out=response_list)
|
|
response_list += kernel.val_out * outer_sum
|
|
|
|
# min_response, max_val, min_loc, max_loc = cv2.minMaxLoc(response_list)
|
|
min_response, _, min_loc, _ = cv2.minMaxLoc(response_list)
|
|
|
|
center = ((xy_steps_list[0][min_loc[0]] - padding), (xy_steps_list[1][min_loc[1]] - padding))
|
|
|
|
frame_conv_stride[:, :] = response_list
|
|
# or
|
|
# frame_conv_stride[:, :] = response_list.astype(np.uint8)
|
|
|
|
return frame_conv, min_response, center
|
|
|
|
|
|
class CvParameters_new:
|
|
# It may be a little slower because a dict named "self" is read for each function call.
|
|
def __init__(self, radius, step):
|
|
# self.prev_radius=radius
|
|
self._radius = radius
|
|
self.pad = 2 * radius
|
|
# self.prev_step=step
|
|
self._step = step
|
|
self._hsf = HaarSurroundFeature_new(radius)
|
|
|
|
def get_rpsh(self):
|
|
return self._radius, self.pad, self._step, self._hsf
|
|
# Essentially, the following would be preferable, but it would take twice as long to call.
|
|
# return self.radius, self.pad, self.step, self.hsf
|
|
|
|
@property
|
|
def radius(self):
|
|
return self._radius
|
|
|
|
@radius.setter
|
|
def radius(self, now_radius):
|
|
# self.prev_radius=self._radius
|
|
self._radius = now_radius
|
|
self.pad = 2 * now_radius
|
|
self.hsf = now_radius
|
|
|
|
@property
|
|
def step(self):
|
|
return self._step
|
|
|
|
@step.setter
|
|
def step(self, now_step):
|
|
# self.prev_step=self.step
|
|
self._step = now_step
|
|
|
|
@property
|
|
def hsf(self):
|
|
return self._hsf
|
|
|
|
@hsf.setter
|
|
def hsf(self, now_radius):
|
|
self._hsf = HaarSurroundFeature_new(now_radius)
|
|
|
|
|
|
class HaarSurroundFeature_new:
|
|
|
|
def __init__(self, r_inner, r_outer=None, val=None):
|
|
if r_outer is None:
|
|
r_outer = r_inner * 3
|
|
# print(r_outer)
|
|
r_inner2 = r_inner * r_inner
|
|
count_inner = r_inner2
|
|
count_outer = r_outer * r_outer - r_inner2
|
|
|
|
if val is None:
|
|
val_inner = 1.0 / r_inner2
|
|
val_outer = -val_inner * count_inner / count_outer
|
|
|
|
else:
|
|
val_inner = val[0]
|
|
val_outer = val[1]
|
|
|
|
self.val_in = float(val_inner)#np.array(val_inner, dtype=np.float64)
|
|
self.val_out = float(val_outer)#np.array(val_outer, dtype=np.float64)
|
|
self.r_in = r_inner
|
|
self.r_out = r_outer
|
|
|
|
def get_kernel(self):
|
|
# Defined here, but not yet used?
|
|
# Create a kernel filled with the value of self.val_out
|
|
kernel = np.ones(shape=(2 * self.r_out - 1, 2 * self.r_out - 1), dtype=np.float64) * self.val_out
|
|
|
|
# Set the values of the inner area of the kernel using array slicing
|
|
start = (self.r_out - self.r_in)
|
|
end = (self.r_out + self.r_in - 1)
|
|
kernel[start:end, start:end] = self.val_in
|
|
|
|
return kernel
|
|
|
|
@lru_cache(maxsize=lru_maxsize_vvs)
|
|
def get_hsf_empty_array(len_sx,len_sy, frameint_x, frame_int_dtype, fcshape):
|
|
# def get_hsf_empty_array(len_syx, frameint_x, frame_int_dtype, fcshape):
|
|
# Function to reduce array allocation by providing an empty array first and recycling it with lru
|
|
len_syx=(len_sy,len_sx)
|
|
inner_sum = np.empty(len_syx, dtype=frame_int_dtype)
|
|
|
|
# in_p_temp = np.empty((len_syx[0], frameint_x), dtype=frame_int_dtype)
|
|
# in_p00 = np.empty(len_syx, dtype=frame_int_dtype)
|
|
# in_p11 = np.empty(len_syx, dtype=frame_int_dtype)
|
|
# in_p01 = np.empty(len_syx, dtype=frame_int_dtype)
|
|
# in_p10 = np.empty(len_syx, dtype=frame_int_dtype)
|
|
|
|
# inner_sum_temp = np.empty((*len_syx,4), dtype=frame_int_dtype)
|
|
outer_sum = np.empty(len_syx, dtype=frame_int_dtype)
|
|
# outer_sum_temp = np.empty((*len_syx,5), dtype=frame_int_dtype)
|
|
p_temp = np.empty((len_sy, frameint_x), dtype=frame_int_dtype)
|
|
p00 = np.empty(len_syx, dtype=frame_int_dtype)
|
|
p11 = np.empty(len_syx, dtype=frame_int_dtype)
|
|
p01 = np.empty(len_syx, dtype=frame_int_dtype)
|
|
p10 = np.empty(len_syx, dtype=frame_int_dtype)
|
|
response_list = np.empty(len_syx, dtype=np.float64)# or np.int32
|
|
frame_conv = np.zeros(shape=fcshape[0], dtype=np.uint8)# or np.float64
|
|
frame_conv_stride = frame_conv[::fcshape[1], ::fcshape[2]]
|
|
return inner_sum, outer_sum, p_temp, p00, p11, p01, p10, response_list, frame_conv, frame_conv_stride
|
|
# return inner_sum,in_p_temp,in_p00,in_p11,in_p01,in_p10, outer_sum, p_temp, p00, p11, p01, p10, response_list, frame_conv, frame_conv_stride
|
|
|
|
|
|
@lru_cache(maxsize=lru_maxsize_vvs)
|
|
def get_hsf_inout_index(padding, x_step, y_step, col, row, r_in, r_out):#,val_in,val_out):
|
|
# y_steps,x_steps=np.ogrid[padding:y_step * len_sy + padding:y_step, padding:x_step * len_sx + padding:x_step]
|
|
y_steps_arr = np.arange(padding, row - padding, y_step,dtype=np.int16)
|
|
x_steps_arr = np.arange(padding, col - padding, x_step,dtype=np.int16)
|
|
len_sx, len_sy = len(x_steps_arr), len(y_steps_arr)
|
|
|
|
# y_steps_arr = np.arange(padding, row - padding, y_step)
|
|
# x_steps_arr = np.arange(padding, col - padding, x_step)
|
|
# len_sx, len_sy = len(x_steps_arr), len(y_steps_arr)
|
|
|
|
# inarr_m = frame_int[y_steps[0]-r_in:y_steps[-1]-r_in+1:y_step]
|
|
# inarr_p = frame_int[y_steps[0]+r_in:y_steps[-1]+r_in+1:y_step]
|
|
# inarr_mm = inarr_m[:, x_steps[0]-r_in:x_steps[-1]-r_in+1:x_step]
|
|
# inarr_mp = inarr_m[:, x_steps[0]+r_in:x_steps[-1]+r_in+1:x_step]
|
|
# inarr_pm = inarr_p[:, x_steps[0]-r_in:x_steps[-1]-r_in+1:x_step]
|
|
# inarr_pp = inarr_p[:, x_steps[0]+r_in:x_steps[-1]+r_in+1:x_step]
|
|
|
|
# inarr_ym = np.arange(y_steps_arr[0]-r_in,y_steps_arr[-1]-r_in+1,y_step).reshape(-1,1)#np.ogrid[padding-r_in:y_steps[-1]-r_in+1:y_step],frame_int[y_steps[0]-r_in:y_steps[-1]-r_in+1:y_step]
|
|
# inarr_yp = np.arange(y_steps_arr[0]+r_in,y_steps_arr[-1]+r_in+1,y_step).reshape(-1,1)#np.ogrid[padding+r_in:y_steps[-1]+r_in+1:y_step],frame_int[y_steps[0]+r_in:y_steps[-1]+r_in+1:y_step]
|
|
# inarr_mm_index = (inarr_ym,np.arange(x_steps_arr[0]-r_in,x_steps_arr[-1]-r_in+1,x_step).reshape(1,-1))#inarr_m[:, x_steps[0]-r_in:x_steps[-1]-r_in+1:x_step]
|
|
# inarr_mp_index = (inarr_ym,np.arange(x_steps_arr[0]+r_in,x_steps_arr[-1]+r_in+1,x_step).reshape(1,-1))#inarr_m[:, x_steps[0]+r_in:x_steps[-1]+r_in+1:x_step]
|
|
# inarr_pm_index = (inarr_yp,inarr_mm_index[1].copy())#inarr_p[:, x_steps[0]-r_in:x_steps[-1]-r_in+1:x_step]
|
|
# inarr_pp_index = (inarr_yp,inarr_mp_index[1].copy())#inarr_p[:, x_steps[0]+r_in:x_steps[-1]+r_in+1:x_step]
|
|
#
|
|
# y_ro_m = y_steps_arr - r_out
|
|
# x_ro_m = x_steps_arr - r_out
|
|
# y_ro_p = y_steps_arr + r_out
|
|
# x_ro_p = x_steps_arr + r_out
|
|
#
|
|
# return x_steps_arr,y_steps_arr,len_sx,len_sy,inarr_mm_index,inarr_mp_index,inarr_pm_index,inarr_pp_index,y_ro_m,x_ro_m,y_ro_p,x_ro_p
|
|
|
|
# y_rin_m_f = y_steps_arr[0] - r_in
|
|
# y_rin_m_e = y_steps_arr[-1] - r_in + 1
|
|
# y_rin_p_f = y_steps_arr[0] + r_in
|
|
# y_rin_p_e = y_steps_arr[-1] + r_in + 1
|
|
#
|
|
# x_rin_m_f = x_steps_arr[0] - r_in
|
|
# x_rin_m_e = x_steps_arr[-1] - r_in + 1
|
|
# x_rin_p_f = x_steps_arr[0] + r_in
|
|
# x_rin_p_e = x_steps_arr[-1] + r_in + 1
|
|
|
|
# y_rin_m_f = padding - r_in
|
|
# y_rin_m_e = y_steps_arr[-1] - r_in + 1
|
|
# y_rin_p_f = padding + r_in
|
|
# y_rin_p_e = y_steps_arr[-1] + r_in + 1
|
|
#
|
|
# x_rin_m_f = padding - r_in
|
|
# x_rin_m_e = x_steps_arr[-1] - r_in + 1
|
|
# x_rin_p_f = padding + r_in
|
|
# x_rin_p_e = x_steps_arr[-1] + r_in + 1
|
|
|
|
y_end=padding+(y_step*(len_sy-1))
|
|
x_end=padding+(x_step*(len_sx-1))
|
|
y_rin_m_f = padding - r_in
|
|
y_rin_m_e = y_end - r_in + 1
|
|
y_rin_p_f = padding + r_in
|
|
y_rin_p_e = y_end + r_in + 1
|
|
|
|
x_rin_m_f = padding - r_in
|
|
x_rin_m_e = x_end - r_in + 1
|
|
x_rin_p_f = padding + r_in
|
|
x_rin_p_e = x_end + r_in + 1
|
|
|
|
y_rin_m=slice(y_rin_m_f,y_rin_m_e,y_step)
|
|
y_rin_p=slice(y_rin_p_f,y_rin_p_e,y_step)
|
|
x_rin_m=slice(x_rin_m_f,x_rin_m_e,x_step)
|
|
x_rin_p=slice(x_rin_p_f,x_rin_p_e,x_step)
|
|
|
|
# y_rin_m=np.arange(padding-r_in,y_end-r_in+1,y_step,dtype=np.int16)
|
|
# y_rin_p=np.arange(padding+r_in,y_end+r_in+1,y_step,dtype=np.int16)
|
|
# x_rin_m=np.arange(padding-r_in,x_end-r_in+1,x_step,dtype=np.int16)
|
|
# x_rin_p=np.arange(padding+r_in,x_end+r_in+1,x_step,dtype=np.int16)
|
|
|
|
# y_ro_m = y_steps_arr - r_out
|
|
# x_ro_m = x_steps_arr - r_out
|
|
# y_ro_p = y_steps_arr + r_out
|
|
# x_ro_p = x_steps_arr + r_out
|
|
|
|
# y_ro_m = slice(max(0,y_steps_arr[0]-r_out),max(0,y_steps_arr[-1]-r_out),y_step)#,y_steps_arr - r_out
|
|
# x_ro_m = slice(max(0,x_steps_arr[0]-r_out),max(0,x_steps_arr[-1]-r_out),x_step)#x_steps_arr - r_out
|
|
# y_ro_p = slice(min(row,y_steps_arr[0]+r_out),min(row,y_steps_arr[-1]+r_out),y_step)#y_steps_arr + r_out
|
|
# x_ro_p = slice(min(col,x_steps_arr[0]+r_out),min(col,x_steps_arr[-1]+r_out),x_step)#x_steps_arr + r_out
|
|
|
|
# y_ro_m = np.clip(y_steps_arr - r_out,0,y_steps_arr[-1])#[:,np.newaxis]
|
|
# x_ro_m = np.clip(x_steps_arr - r_out,0,x_steps_arr[-1])#[np.newaxis,:]
|
|
# y_ro_p = np.clip(y_steps_arr + r_out,0,row)#[:,np.newaxis]
|
|
# x_ro_p = np.clip(x_steps_arr + r_out,0,col)#[np.newaxis,:]
|
|
|
|
y_ro_m = np.maximum(y_steps_arr - r_out,0)#[:,np.newaxis]
|
|
x_ro_m = np.maximum(x_steps_arr - r_out,0)#[np.newaxis,:]
|
|
y_ro_p = np.minimum(row,y_steps_arr + r_out)#[:,np.newaxis]
|
|
x_ro_p = np.minimum(col,x_steps_arr + r_out)#[np.newaxis,:]
|
|
|
|
# return x_steps_arr, y_steps_arr, len_sx, len_sy, y_rin_m_f, y_rin_m_e, y_rin_p_f, y_rin_p_e, x_rin_m_f, x_rin_m_e, x_rin_p_f, x_rin_p_e, y_ro_m, x_ro_m, y_ro_p, x_ro_p
|
|
# return len_sx, len_sy, y_rin_m, y_rin_p, x_rin_m, x_rin_p, y_ro_m, x_ro_m, y_ro_p, x_ro_p,val_in,val_out,(row - 2 * padding, col - 2 * padding)
|
|
return len_sx, len_sy, y_rin_m, y_rin_p, x_rin_m, x_rin_p, y_ro_m, x_ro_m, y_ro_p, x_ro_p,(row - 2 * padding, col - 2 * padding)
|
|
|
|
@lru_cache(maxsize=lru_maxsize_s)
|
|
def get_hsf_center(padding, x_step, y_step, min_loc):#min_x,min_y):
|
|
# y_steps,x_steps=np.ogrid[padding:y_step * len_sy + padding:y_step, padding:x_step * len_sx + padding:x_step]
|
|
# y_steps_arr = np.arange(padding, row - padding, y_step)
|
|
# x_steps_arr = np.arange(padding, col - padding, x_step)
|
|
# return x_steps_arr[min_x] - padding, y_steps_arr[min_y] - padding
|
|
# return np.array(padding+(x_step*min_loc[0])-padding),np.array(padding+(y_step*min_loc[1])-padding)
|
|
return padding+(x_step*min_loc[0])-padding,padding+(y_step*min_loc[1])-padding
|
|
|
|
@lru_cache(maxsize=lru_maxsize_vvs)
|
|
def get_frameint_empty_array(frame_shape,pad,x_step, y_step, r_in, r_out):
|
|
frame_int_dtype=np.intc
|
|
|
|
frame_pad=np.empty((frame_shape[0]+(pad*2),frame_shape[1]+(pad*2)),dtype=np.uint8)
|
|
|
|
row,col=frame_pad.shape
|
|
|
|
frame_int=np.empty((row+1,col+1),dtype=frame_int_dtype)
|
|
|
|
y_steps_arr = np.arange(pad, row - pad, y_step,dtype=np.int16)
|
|
x_steps_arr = np.arange(pad, col - pad, x_step,dtype=np.int16)
|
|
len_sx, len_sy = len(x_steps_arr), len(y_steps_arr)
|
|
y_end = pad + (y_step * (len_sy - 1))
|
|
x_end = pad + (x_step * (len_sx - 1))
|
|
|
|
y_rin_m = slice( pad - r_in, y_end - r_in + 1, y_step)
|
|
y_rin_p = slice(pad + r_in, y_end + r_in + 1, y_step)
|
|
x_rin_m = slice(pad - r_in, x_end - r_in + 1, x_step)
|
|
x_rin_p = slice(pad + r_in, x_end + r_in + 1, x_step)
|
|
|
|
in_p00_view=frame_int[y_rin_m,x_rin_m]
|
|
in_p11_view=frame_int[y_rin_p,x_rin_p]
|
|
in_p01_view=frame_int[y_rin_m,x_rin_p]
|
|
in_p10_view=frame_int[y_rin_p,x_rin_m]
|
|
|
|
y_ro_m = np.maximum(y_steps_arr - r_out,0)#[:,np.newaxis]
|
|
x_ro_m = np.maximum(x_steps_arr - r_out,0)#[np.newaxis,:]
|
|
y_ro_p = np.minimum(row,y_steps_arr + r_out)#[:,np.newaxis]
|
|
x_ro_p = np.minimum(col,x_steps_arr + r_out)#[np.newaxis,:]
|
|
|
|
return frame_pad,frame_int,in_p00_view,in_p11_view,in_p01_view,in_p10_view,y_ro_m, x_ro_m, y_ro_p, x_ro_p,(row - 2 * pad, col - 2 * pad),len_sx, len_sy
|
|
|
|
# todo: Check performance when changing integer type numpy array to low bits integer type
|
|
# todo: Consider using np.clip if the clamp function input meets some conditions
|
|
# @profile
|
|
def conv_int(frame_int, kernel, x_step,y_step, padding,in_p00_view, in_p11_view, in_p01_view, in_p10_view, y_ro_m, x_ro_m, y_ro_p, x_ro_p, f_shape, len_sx, len_sy): # , x_steps,y_steps):#xy_steps_list):
|
|
"""
|
|
:param frame_int:
|
|
:param kernel: hsf
|
|
:param step: (x,y)
|
|
:param padding: int
|
|
:return:
|
|
"""
|
|
# row, col = frame_int.shape
|
|
# row -= 1
|
|
# col -= 1
|
|
# x_step, y_step = xy_step
|
|
# padding2 = 2 * padding
|
|
# f_shape = row - 2 * padding, col - 2 * padding
|
|
# r_in = kernel.r_in
|
|
# len_sx, len_sy = len(x_steps), len(y_steps)
|
|
# inout_sum, p_temp, p_list, response_list, frameconvlist = get_hsf_empty_array((len_sy, len_sx), col + 1,
|
|
# frame_int.dtype, (f_shape, y_step, x_step))
|
|
# inner_sum, outer_sum = inout_sum
|
|
# p00, p11, p01, p10 = p_list
|
|
# frame_conv, frame_conv_stride = frameconvlist
|
|
# len_sx, len_sy, y_rin_m, y_rin_p, x_rin_m, x_rin_p, y_ro_m, x_ro_m, y_ro_p, x_ro_p,val_in,val_out,f_shape = get_hsf_inout_index(
|
|
# padding, x_step, y_step, col, row, kernel.r_in, kernel.r_out,kernel.val_in,kernel.val_out)
|
|
# len_sx, len_sy, y_rin_m, y_rin_p, x_rin_m, x_rin_p, y_ro_m, x_ro_m, y_ro_p, x_ro_p, f_shape = get_hsf_inout_index(
|
|
# padding, x_step, y_step, col, row, kernel.r_in, kernel.r_out)
|
|
|
|
inner_sum, outer_sum,p_temp, p00, p11, p01, p10, response_list, frame_conv, frame_conv_stride = get_hsf_empty_array(len_sx,len_sy,#(len_sy, len_sx),
|
|
frame_int.shape[1],#col + 1,
|
|
frame_int.dtype, (
|
|
f_shape, y_step,
|
|
x_step))
|
|
#
|
|
# inner_sum, in_p_temp, in_p00, in_p11, in_p01, in_p10, outer_sum, p_temp, p00, p11, p01, p10, response_list, frame_conv, frame_conv_stride= get_hsf_empty_array((len_sy, len_sx),
|
|
# col + 1,
|
|
# frame_int.dtype, (
|
|
# f_shape, y_step,
|
|
# x_step))
|
|
# inout_sum, p_temp, p_list, response_list, frameconvlist = hsf_empty_array
|
|
# inner_sum, outer_sum = inout_sum
|
|
# p00, p11, p01, p10 = p_list
|
|
# frame_conv, frame_conv_stride = frameconvlist
|
|
|
|
# x_steps_st_end=np.asarray([x_steps[0],x_steps[len_sx-1]+1])
|
|
# # x_steps_st_end[1]+=1
|
|
# y_steps_st_end=np.asarray([y_steps[0],y_steps[len_sy-1]+1])
|
|
# y_steps_st_end[1] += 1
|
|
|
|
# x_steps_st_end=np.asarray([x_steps[0],x_steps[len_sx-1]+1])
|
|
# y_steps_st_end=np.asarray([y_steps[0],y_steps[len_sy-1]+1])
|
|
# xy_steps_st_end=np.asarray([[x_steps[0],x_steps[-1]],[y_steps[0],y_steps[-1]]])
|
|
# xy_steps_st_end[:,1]+=1
|
|
|
|
# xy_rin_m = xy_steps_st_end-r_in
|
|
# xy_rin_p = xy_steps_st_end + r_in
|
|
|
|
# xx==(y,x),m==MINUS,p==PLUS, ex: mm==(y-,x-)
|
|
# inarr_m = frame_int[y_steps[0]-r_in:y_steps[-1]-r_in+1:y_step]
|
|
# inarr_p = frame_int[y_steps[0]+r_in:y_steps[-1]+r_in+1:y_step]
|
|
# inarr_mm = inarr_m[:, x_steps[0]-r_in:x_steps[-1]-r_in+1:x_step]
|
|
# inarr_mp = inarr_m[:, x_steps[0]+r_in:x_steps[-1]+r_in+1:x_step]
|
|
# inarr_pm = inarr_p[:, x_steps[0]-r_in:x_steps[-1]-r_in+1:x_step]
|
|
# inarr_pp = inarr_p[:, x_steps[0]+r_in:x_steps[-1]+r_in+1:x_step]
|
|
|
|
# y_rin_m = y_steps_st_end - r_in
|
|
# x_rin_m = x_steps_st_end - r_in
|
|
# y_rin_p = y_steps_st_end + r_in
|
|
# x_rin_p = x_steps_st_end + r_in
|
|
# # xx==(y,x),m==MINUS,p==PLUS, ex: mm==(y-,x-)
|
|
# inarr_mm = frame_int[y_rin_m[0]:y_rin_m[1]:y_step, x_rin_m[0]:x_rin_m[1]:x_step]
|
|
# inarr_mp = frame_int[y_rin_m[0]:y_rin_m[1]:y_step, x_rin_p[0]:x_rin_p[1]:x_step]
|
|
# inarr_pm = frame_int[y_rin_p[0]:y_rin_p[1]:y_step, x_rin_m[0]:x_rin_m[1]:x_step]
|
|
# inarr_pp = frame_int[y_rin_p[0]:y_rin_p[1]:y_step, x_rin_p[0]:x_rin_p[1]:x_step]
|
|
|
|
# == inarr_mm + inarr_pp - inarr_mp - inarr_pm
|
|
# inner_sum[:, :] = inarr_mm
|
|
# inner_sum += inarr_pp
|
|
# inner_sum -= inarr_mp
|
|
# inner_sum -= inarr_pm
|
|
|
|
# cv2.subtract(cv2.subtract(cv2.add(inarr_mm,inarr_pp),inarr_mp),inarr_pm,dst=inner_sum[:,:])
|
|
# inner_sum[:, :]=inarr_mm.__add__(inarr_pp).__sub__(inarr_mp).__sub__(inarr_pm)
|
|
# inner_sum[:,:]=inarr_m[:, x_steps[0]-r_in:x_steps[-1]-r_in+1:x_step]
|
|
# inner_sum += inarr_p[:, x_steps[0]+r_in:x_steps[-1]+r_in+1:x_step]
|
|
# inner_sum -= inarr_m[:, x_steps[0]+r_in:x_steps[-1]+r_in+1:x_step]
|
|
# inner_sum -= inarr_p[:, x_steps[0]-r_in:x_steps[-1]-r_in+1:x_step]
|
|
# y_m_f=y_steps[0] - r_in
|
|
# y_m_e=y_steps[-1] - r_in + 1
|
|
# y_p_f=y_steps[0] + r_in
|
|
# y_p_e=y_steps[-1] + r_in + 1
|
|
#
|
|
# x_m_f=x_steps[0] - r_in
|
|
# x_m_e=x_steps[-1] - r_in + 1
|
|
# x_p_f=x_steps[0] + r_in
|
|
# x_p_e=x_steps[-1] + r_in + 1
|
|
|
|
# inner_sum[:,:]=frame_int[y_steps[0]-r_in:y_steps[-1]-r_in+1:y_step, x_steps[0]-r_in:x_steps[-1]-r_in+1:x_step]
|
|
# inner_sum += frame_int[y_steps[0]+r_in:y_steps[-1]+r_in+1:y_step, x_steps[0]+r_in:x_steps[-1]+r_in+1:x_step]
|
|
# inner_sum -= frame_int[y_steps[0]-r_in:y_steps[-1]-r_in+1:y_step, x_steps[0]+r_in:x_steps[-1]+r_in+1:x_step]
|
|
# inner_sum -= frame_int[y_steps[0]+r_in:y_steps[-1]+r_in+1:y_step, x_steps[0]-r_in:x_steps[-1]-r_in+1:x_step]
|
|
|
|
|
|
# inner_sum[:, :] = frame_int[y_rin_m_f:y_rin_m_e:y_step, x_rin_m_f:x_rin_m_e:x_step]
|
|
# inner_sum += frame_int[y_rin_p_f:y_rin_p_e:y_step, x_rin_p_f:x_rin_p_e:x_step]
|
|
# inner_sum -= frame_int[y_rin_m_f:y_rin_m_e:y_step, x_rin_p_f:x_rin_p_e:x_step]
|
|
# inner_sum -= frame_int[y_rin_p_f:y_rin_p_e:y_step, x_rin_m_f:x_rin_m_e:x_step]
|
|
|
|
# inner_sum_temp[:, :,0] = frame_int[y_rin_m, x_rin_m].copy()
|
|
# inner_sum_temp[:, :,1] = frame_int[y_rin_p, x_rin_p].copy()
|
|
# inner_sum_temp[:, :,2] = -frame_int[y_rin_m, x_rin_p].copy()
|
|
# inner_sum_temp[:, :,3] = -frame_int[y_rin_p, x_rin_m].copy()
|
|
# cv2.transform(inner_sum_temp, np.ones((1, 4)),
|
|
# dst=inner_sum)
|
|
|
|
#
|
|
# inner_sum[:, :] = frame_int[y_rin_m, x_rin_m]
|
|
# inner_sum += frame_int[y_rin_p, x_rin_p]
|
|
# inner_sum -= frame_int[y_rin_m, x_rin_p]
|
|
# inner_sum -= frame_int[y_rin_p, x_rin_m]
|
|
|
|
# inner_sum[:, :] = frame_int[y_rin_m, x_rin_m]+ frame_int[y_rin_p, x_rin_p]-frame_int[y_rin_m, x_rin_p]- frame_int[y_rin_p, x_rin_m]
|
|
|
|
# inner_sum[:, :] = frame_int[y_rin_m, x_rin_m] + frame_int[y_rin_p, x_rin_p] - frame_int[y_rin_m, x_rin_p] - frame_int[y_rin_p, x_rin_m]
|
|
|
|
inner_sum[:, :] = in_p00_view + in_p11_view - in_p01_view - in_p10_view
|
|
|
|
# inarr_m = frame_int[inarr_ym]
|
|
# inarr_p = frame_int[inarr_yp]
|
|
# inner_sum[:, :] = inarr_m[:,inarr_xm]#inarr_mm
|
|
# inner_sum += inarr_p[:,inarr_xp]#inarr_pp
|
|
# inner_sum -= inarr_m[:,inarr_xp]#inarr_mp
|
|
# inner_sum -= inarr_p[:,inarr_xm]#inarr_pm
|
|
|
|
# Bottleneck here, I want to make it smarter. Someone do it.
|
|
# (y,x)
|
|
# p00=max(y_ro_m,0),max(x_ro_m,0)
|
|
# p11=min(y_ro_p,ylim),min(x_ro_p,xlim)
|
|
# p01=max(y_ro_m,0),min(x_ro_p,xlim)
|
|
# p10=min(y_ro_p,ylim),max(x_ro_m,0)
|
|
# y_ro_m = y_steps - kernel.r_out
|
|
# x_ro_m = x_steps - kernel.r_out
|
|
# y_ro_p = y_steps + kernel.r_out
|
|
# x_ro_p = x_steps + kernel.r_out
|
|
# p00 calc
|
|
# np.take(frame_int, y_steps - kernel.r_out, axis=0, mode="clip", out=p_temp)
|
|
|
|
# np.take(frame_int, y_ro_m, axis=0, mode="clip", out=p_temp)
|
|
# np.take(p_temp, x_ro_m, axis=1, mode="clip", out=p00)
|
|
# # p01 calc
|
|
# np.take(p_temp, x_ro_p, axis=1, mode="clip", out=p01)
|
|
# # p11 calc
|
|
# np.take(frame_int, y_ro_p, axis=0, mode="clip", out=p_temp)
|
|
# np.take(p_temp, x_ro_p, axis=1, mode="clip", out=p11)
|
|
# # p10 calc
|
|
# np.take(p_temp, x_ro_m, axis=1, mode="clip", out=p10)
|
|
|
|
# np.take(frame_int, y_ro_m, axis=0, mode="clip", out=p_temp)
|
|
# np.take(p_temp, x_ro_m, axis=1, mode="clip", out=p00)
|
|
# # p01 calc
|
|
# np.take(p_temp, x_ro_p, axis=1, mode="clip", out=p01)
|
|
# # p11 calc
|
|
# np.take(frame_int, y_ro_p, axis=0, mode="clip", out=p_temp)
|
|
# np.take(p_temp, x_ro_p, axis=1, mode="clip", out=p11)
|
|
# # p10 calc
|
|
# np.take(p_temp, x_ro_m, axis=1, mode="clip", out=p10)
|
|
|
|
frame_int.take( y_ro_m, axis=0, mode="clip", out=p_temp)
|
|
p_temp.take(x_ro_m, axis=1, mode="clip", out=p00)
|
|
# p01 calc
|
|
p_temp.take( x_ro_p, axis=1, mode="clip", out=p01)
|
|
# p11 calc
|
|
frame_int.take( y_ro_p, axis=0, mode="clip", out=p_temp)
|
|
p_temp.take( x_ro_p, axis=1, mode="clip", out=p11)
|
|
# p10 calc
|
|
p_temp.take( x_ro_m, axis=1, mode="clip", out=p10)
|
|
|
|
# p_temp[:,:]=frame_int[y_ro_m.reshape(-1),:]#.copy()
|
|
# p00[:,:]=np.asarray(p_temp[:,x_ro_m.reshape(-1)])#.copy()
|
|
# # p01 calc
|
|
# p01[:,:]=-p_temp[:,x_ro_p.reshape(-1)]#.copy()
|
|
# # p11 calc
|
|
# p_temp[:,:]=frame_int[y_ro_p.reshape(-1),:]
|
|
# p11[:,:]=np.asarray(p_temp[:,x_ro_p.reshape(-1)])#.copy()
|
|
# # p10 calc
|
|
# p10[:,:]=-p_temp[:,x_ro_m.reshape(-1)]#.copy()
|
|
|
|
# the point is this
|
|
# p00=np.take(np.take(frame_int, y_ro_m, axis=0, mode="clip"), x_ro_m, axis=1, mode="clip")
|
|
# p11=np.take(np.take(frame_int, y_ro_p, axis=0, mode="clip"), x_ro_p, axis=1, mode="clip")
|
|
# p01=np.take(np.take(frame_int, y_ro_m, axis=0, mode="clip"), x_ro_p, axis=1, mode="clip")
|
|
# p10=np.take(np.take(frame_int, y_ro_p, axis=0, mode="clip"), x_ro_m, axis=1, mode="clip")
|
|
|
|
outer_sum[:, :] = p00 + p11 - p01 - p10 - inner_sum
|
|
# cv2.transform(np.asarray([p00, p11, -p01, -p10, -inner_sum]).transpose((1, 2, 0)), np.ones((1, 5)),
|
|
# dst=outer_sum) # https://answers.opencv.org/question/3120/how-to-sum-a-3-channel-matrix-to-a-one-channel-matrix/
|
|
# cv2.transform(np.asarray([frame_int[y_ro_m,x_ro_m], frame_int[y_ro_m,x_ro_p], -frame_int[y_ro_p,x_ro_p], -frame_int[y_ro_p,x_ro_m], -inner_sum]).transpose((1, 2, 0)), np.ones((1, 5)),
|
|
# dst=outer_sum) # https://answers.opencv.org/question/3120/how-to-sum-a-3-channel-matrix-to-a-one-channel-matrix/
|
|
# cv2.transform(np.asarray([
|
|
# frame_int[y_ro_m,x_ro_m],
|
|
# frame_int[y_ro_p,x_ro_p],
|
|
# -frame_int[y_ro_m,x_ro_p],
|
|
# -frame_int[y_ro_p,x_ro_m],
|
|
# -inner_sum]).transpose((1, 2, 0)), np.ones((1, 5)),
|
|
# dst=outer_sum) # https://answers.opencv.org/question/3120/how-to-sum-a-3-channel-matrix-to-a-one-channel-matrix/
|
|
|
|
# outer_sum_temp[:,:,0]=p00#frame_int[y_ro_m, x_ro_m]
|
|
# outer_sum_temp[:, :, 1] = p11#frame_int[y_ro_p, x_ro_p]
|
|
# outer_sum_temp[:, :, 2] = p01#frame_int[y_ro_m, x_ro_p]
|
|
# outer_sum_temp[:, :, 3] = p10#frame_int[y_ro_p, x_ro_m]
|
|
|
|
# p_temp[:,:]=frame_int[y_ro_m.reshape(-1),:]#.copy()
|
|
# outer_sum_temp[:,:,0]=np.asarray(p_temp[:,x_ro_m.reshape(-1)])#frame_int[y_ro_m, x_ro_m]
|
|
# outer_sum_temp[:, :, 2] = -p_temp[:,x_ro_p.reshape(-1)]#frame_int[y_ro_m, x_ro_p]
|
|
# p_temp[:,:]=frame_int[y_ro_p.reshape(-1),:]
|
|
# outer_sum_temp[:, :, 1] = np.asarray(p_temp[:,x_ro_p.reshape(-1)])#frame_int[y_ro_p, x_ro_p]
|
|
# outer_sum_temp[:, :, 3] = -p_temp[:,x_ro_m.reshape(-1)]#frame_int[y_ro_p, x_ro_m]
|
|
# outer_sum_temp[:, :, 4] = -inner_sum
|
|
# cv2.transform(outer_sum_temp, np.ones((1, 5)),
|
|
# dst=outer_sum) # https://answers.opencv.org/question/3120/how-to-sum-a-3-channel-matrix-to-a-one-channel-matrix/
|
|
|
|
|
|
# np.multiply(kernel.val_in, inner_sum, dtype=np.float64, out=response_list)
|
|
# response_list += kernel.val_out * outer_sum
|
|
cv2.addWeighted(inner_sum,
|
|
kernel.val_in,
|
|
outer_sum,# or p00 + p11 - p01 - p10 - inner_sum
|
|
kernel.val_out,
|
|
0.0,
|
|
dtype=cv2.CV_64F,#or cv2.CV_32S
|
|
dst=response_list)
|
|
|
|
# min_response, max_val, min_loc, max_loc = cv2.minMaxLoc(response_list)
|
|
min_response, _, min_loc, _ = cv2.minMaxLoc(response_list)
|
|
|
|
# center = ((x_steps_arr[min_loc[0]] - padding), (y_steps_arr[min_loc[1]] - padding))
|
|
# center = get_hsf_center(padding,x_step,y_step,min_loc)#[0],min_loc[1])
|
|
|
|
frame_conv_stride[:, :] = response_list
|
|
# or
|
|
# frame_conv_stride[:, :] = response_list.astype(np.uint8)
|
|
|
|
# return frame_conv, min_response, center
|
|
return frame_conv, min_response, get_hsf_center(padding,x_step,y_step,min_loc)
|
|
|
|
|
|
@lru_cache(lru_maxsize_vvs)
|
|
def get_ransac_frame(frame_shape):
|
|
return np.empty(frame_shape,dtype=np.uint8),np.empty(frame_shape,dtype=np.uint8)#np.float64)
|
|
|
|
|
|
@lru_cache(lru_maxsize_s)
|
|
def get_center_noclamp(center_xy,radius):
|
|
center_x, center_y = center_xy
|
|
upper_x = center_x + radius
|
|
lower_x = center_x - radius
|
|
upper_y = center_y + radius
|
|
lower_y = center_y - radius
|
|
return center_x,center_y,upper_x,lower_x,upper_y,lower_y
|
|
|
|
|
|
@lru_cache(lru_maxsize_s)
|
|
def get_hsf_center_uplow(center_x,center_y,radius):
|
|
hsf_center_x, hsf_center_y = center_x, center_y
|
|
# ransac_xy_offset = (hsf_center_x-20, hsf_center_y-20)
|
|
upper_x = hsf_center_x + max(20, radius)
|
|
lower_x = hsf_center_x - max(20, radius)
|
|
upper_y = hsf_center_y + max(20, radius)
|
|
lower_y = hsf_center_y - max(20, radius)
|
|
ransac_xy_offset = (lower_x, lower_y)
|
|
return upper_x,lower_x,upper_y,lower_y,ransac_xy_offset
|
|
|
|
class HSRAC_cls(object):
|
|
def __init__(self):
|
|
# I'd like to take into account things like print, end_time - start_time processing time, etc., but it's too much trouble.
|
|
|
|
# For measuring total processing time
|
|
|
|
self.main_start_time = timeit.default_timer()
|
|
|
|
self.rng = np.random.default_rng()
|
|
if old_mode:
|
|
self.cvparam = CvParameters_old(default_radius, default_step)
|
|
else:
|
|
# os.environ["OPENBLAS_NUM_THREADS"]="1" # https://github.com/numpy/numpy/issues/22928
|
|
self.cvparam = CvParameters_new(default_radius, default_step)
|
|
|
|
self.cv_modeo = ["first_frame", "radius_adjust", "blink_adjust", "normal"]
|
|
self.now_modeo = self.cv_modeo[0]
|
|
|
|
self.auto_radius_calc = AutoRadiusCalc()
|
|
self.blink_detector = BlinkDetector()
|
|
self.center_q1 = BlinkDetector()
|
|
|
|
self.cap = None
|
|
|
|
self.timedict = {"to_gray": [], "int_img": [], "conv_int": [], "crop": [], "ransac": [], "total_cv": []}
|
|
|
|
# ransac
|
|
self.rng = np.random.default_rng()
|
|
self.sfc = np.random.default_rng(np.random.SFC64())
|
|
|
|
# self.kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
|
|
# or
|
|
self.kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
|
|
self.gauss_k = cv2.getGaussianKernel(5, 0)
|
|
|
|
def open_video(self, video_path):
|
|
# Temporary implementation to run
|
|
cap = cv2.VideoCapture(video_path)
|
|
if not cap.isOpened():
|
|
raise IOError("Error opening video stream or file")
|
|
self.cap = cap
|
|
return True
|
|
|
|
def read_frame(self):
|
|
# Temporary implementation to run
|
|
if not self.cap.isOpened():
|
|
return False
|
|
ret, frame = self.cap.read()
|
|
if ret:
|
|
# I have set it to grayscale (1ch) just in case, but if the frame is 1ch, this line can be commented out.
|
|
self.current_image = frame # debug code
|
|
self.current_image_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
|
|
return True
|
|
return False
|
|
|
|
# @profile
|
|
def single_run(self):
|
|
# Temporary implementation to run
|
|
## default_radius = 14
|
|
|
|
if imshow_enable or save_video:
|
|
ori_frame = self.current_image.copy()# debug code
|
|
# cropbox=[] # debug code
|
|
|
|
blink_bd = False
|
|
# frame = self.current_image_gray
|
|
|
|
if self.now_modeo == self.cv_modeo[1]:
|
|
# adjustment of radius
|
|
|
|
# debug print
|
|
# if calc_print_enable:
|
|
# temp_radius = self.auto_radius_calc.get_radius()
|
|
# print('Now radius:', temp_radius)
|
|
# self.cvparam.radius = temp_radius
|
|
|
|
self.cvparam.radius = self.auto_radius_calc.get_radius()
|
|
if self.auto_radius_calc.adj_comp_flag:
|
|
self.now_modeo = self.cv_modeo[2] if not skip_blink_detect else self.cv_modeo[3]
|
|
|
|
radius, pad, step, hsf = self.cvparam.get_rpsh()
|
|
|
|
# For measuring processing time of image processing
|
|
cv_start_time = timeit.default_timer()
|
|
frame = self.current_image_gray
|
|
gray_frame = frame
|
|
self.timedict["to_gray"].append(timeit.default_timer() - cv_start_time)
|
|
|
|
# Calculate the integral image of the frame
|
|
int_start_time = timeit.default_timer()
|
|
if old_mode:
|
|
# BORDER_CONSTANT is faster than BORDER_REPLICATE There seems to be almost no negative impact when BORDER_CONSTANT is used.
|
|
frame_pad = cv2.copyMakeBorder(gray_frame, pad, pad, pad, pad, cv2.BORDER_CONSTANT)
|
|
frame_int = cv2.integral(frame_pad)
|
|
else:
|
|
frame_pad, frame_int, in_p00_view, in_p11_view, in_p01_view, in_p10_view, y_ro_m, x_ro_m, y_ro_p, x_ro_p, f_shape, len_sx, len_sy = get_frameint_empty_array(gray_frame.shape,pad,step[0],step[1],hsf.r_in,hsf.r_out)
|
|
cv2.copyMakeBorder(gray_frame, pad, pad, pad, pad, cv2.BORDER_CONSTANT,dst=frame_pad)
|
|
cv2.integral(frame_pad,sum=frame_int,sdepth=cv2.CV_32S)
|
|
self.timedict["int_img"].append(timeit.default_timer() - int_start_time)
|
|
|
|
# Convolve the feature with the integral image
|
|
conv_int_start_time = timeit.default_timer()
|
|
if old_mode:
|
|
xy_step = frameint_get_xy_step_old(frame_int.shape, step, pad, start_offset=None, end_offset=None)
|
|
frame_conv, response, center_xy = conv_int_old(frame_int, hsf, step, pad, xy_step)
|
|
else:
|
|
# frame_conv, response, center_xy = conv_int(frame_int, hsf, step, pad) # , x_step,y_step)
|
|
frame_conv, response, center_xy = conv_int(frame_int, hsf, step[0],step[1], pad,in_p00_view, in_p11_view, in_p01_view, in_p10_view, y_ro_m, x_ro_m, y_ro_p, x_ro_p, f_shape, len_sx, len_sy) # , x_step,y_step)
|
|
# x_step,y_step = frameint_get_xy_step(frame_int.shape, step, pad, start_offset=None, end_offset=None)
|
|
|
|
self.timedict["conv_int"].append(timeit.default_timer() - conv_int_start_time)
|
|
|
|
crop_start_time = timeit.default_timer()
|
|
# Define the center point and radius
|
|
# center_x, center_y = center_xy
|
|
# upper_x = center_x + radius
|
|
# lower_x = center_x - radius
|
|
# upper_y = center_y + radius
|
|
# lower_y = center_y - radius
|
|
center_x, center_y, upper_x, lower_x, upper_y, lower_y=get_center_noclamp(center_xy,radius)
|
|
|
|
# Crop the image using the calculated bounds
|
|
cropped_image = safe_crop(gray_frame, lower_x, lower_y, upper_x, upper_y)
|
|
|
|
# cropbox=[clamp(val, 0, gray_frame.shape[i]) for i,val in zip([1,0,1,0],[lower_x,lower_y,upper_x,upper_y])] # debug code
|
|
|
|
if self.now_modeo == self.cv_modeo[0] or self.now_modeo == self.cv_modeo[1]:
|
|
# If mode is first_frame or radius_adjust, record current radius and response
|
|
self.auto_radius_calc.add_response(radius, response)
|
|
elif self.now_modeo == self.cv_modeo[2]:
|
|
# Statistics for blink detection
|
|
if self.blink_detector.response_len() < blink_init_frames:
|
|
self.blink_detector.add_response(cv2.mean(cropped_image)[0])
|
|
|
|
upper_x = center_x + max(20, radius)
|
|
lower_x = center_x - max(20, radius)
|
|
upper_y = center_y + max(20, radius)
|
|
lower_y = center_y - max(20, radius)
|
|
self.center_q1.add_response(
|
|
cv2.mean(safe_crop(gray_frame, lower_x, lower_y, upper_x, upper_y, keepsize=False))[
|
|
0
|
|
]
|
|
)
|
|
|
|
else:
|
|
|
|
self.blink_detector.calc_thresh()
|
|
self.center_q1.calc_thresh()
|
|
self.now_modeo = self.cv_modeo[3]
|
|
else:
|
|
if 0 in cropped_image.shape: # This line may not be needed. The image will be cropped using safecrop.
|
|
# If shape contains 0, it is not detected well.
|
|
print("Something's wrong.")
|
|
else:
|
|
orig_x, orig_y = center_x, center_y
|
|
if self.blink_detector.enable_detect_flg:
|
|
# If the average value of cropped_image is greater than response_max
|
|
# (i.e., if the cropimage is whitish
|
|
if self.blink_detector.detect(cv2.mean(cropped_image)[0]):
|
|
# blink
|
|
print("BLINK BD")
|
|
blink_bd = True
|
|
# if imshow_enable or save_video:
|
|
# cv2.circle(frame, (orig_x, orig_y), 6, (0, 0, 255), -1)
|
|
# cv2.circle(ori_frame, (center_x, center_y), 7, (255, 0, 0), -1)
|
|
# If you want to update response_max. it may be more cost-effective to rewrite response_list in the following way
|
|
# https://stackoverflow.com/questions/42771110/fastest-way-to-left-cycle-a-numpy-array-like-pop-push-for-a-queue
|
|
|
|
# cv_end_time = timeit.default_timer()
|
|
self.timedict["crop"].append(timeit.default_timer() - crop_start_time)
|
|
# self.timedict["total_cv"].append(cv_end_time - cv_start_time)
|
|
|
|
# if calc_print_enable:
|
|
# the lower the response the better the likelyhood of there being a pupil. you can adujst the radius and steps accordingly
|
|
# print('Kernel response:', response)
|
|
# print('Pixel position:', center_xy)
|
|
|
|
#
|
|
# if imshow_enable:
|
|
# if self.now_modeo != self.cv_modeo[0] and self.now_modeo != self.cv_modeo[1]:
|
|
# if 0 in cropped_image.shape:
|
|
# If shape contains 0, it is not detected well.
|
|
# pass
|
|
# else:
|
|
# cv2.imshow("crop", cropped_image)
|
|
# cv2.imshow("frame", frame)
|
|
# if cv2.waitKey(1) & 0xFF == ord("q"):
|
|
# pass
|
|
|
|
if self.now_modeo == self.cv_modeo[0]:
|
|
# Moving from first_frame to the next mode
|
|
if skip_autoradius and skip_blink_detect:
|
|
self.now_modeo = self.cv_modeo[3]
|
|
elif skip_autoradius:
|
|
self.now_modeo = self.cv_modeo[2]
|
|
else:
|
|
self.now_modeo = self.cv_modeo[1]
|
|
|
|
# For measuring processing time of image processing
|
|
ransac_start_time = timeit.default_timer()
|
|
# Crop first to reduce the amount of data to process.
|
|
# frame = cropped_image[0:len(cropped_image) - 10, :]
|
|
# To reduce the processing data, first convert to 1-channel and then blur.
|
|
# The processing results were the same when I swapped the order of blurring and 1-channelization.
|
|
# frame_gray = cv2.GaussianBlur(frame, (5, 5), 0)
|
|
# cv2.GaussianBlur is slow (use 10%)
|
|
# use cv2.blur() or cv2.boxFilter()?
|
|
# or
|
|
# frame_gray =cv2.boxFilter(frame, -1,(5, 5))# https://github.com/bfraboni/FastGaussianBlur
|
|
# cv2.boxFilter(frame_gray, -1,(5, 5),dst=frame_gray)
|
|
# cv2.boxFilter(frame_gray, -1,(5, 5),dst=frame_gray)
|
|
if old_mode:
|
|
frame_gray = cv2.GaussianBlur(frame, (5, 5), 0)
|
|
else:
|
|
frame_gray = cv2.sepFilter2D(frame, -1, self.gauss_k, self.gauss_k)
|
|
|
|
|
|
#todo:no numpy and use lru
|
|
|
|
# hsf_center_x, hsf_center_y = center_x.copy(), center_y.copy()
|
|
# # ransac_xy_offset = (hsf_center_x-20, hsf_center_y-20)
|
|
# upper_x = hsf_center_x + max(20, radius)
|
|
# lower_x = hsf_center_x - max(20, radius)
|
|
# upper_y = hsf_center_y + max(20, radius)
|
|
# lower_y = hsf_center_y - max(20, radius)
|
|
# ransac_xy_offset = (lower_x, lower_y)
|
|
upper_x, lower_x, upper_y, lower_y, ransac_xy_offset = get_hsf_center_uplow(center_x,center_y,radius)
|
|
|
|
# Crop the image using the calculated bounds
|
|
#todo:safecrop tune
|
|
frame_gray_crop = safe_crop(frame_gray, lower_x, lower_y, upper_x, upper_y)
|
|
th_frame,fic_frame=get_ransac_frame(frame_gray_crop.shape)
|
|
frame = frame_gray_crop # todo: It can cause bugs.
|
|
# this will need to be adjusted everytime hardware is changed (brightness of IR, Camera postion, etc)m
|
|
# min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(frame_gray_crop)
|
|
min_val = cv2.minMaxLoc(frame_gray_crop)[0]
|
|
# min_val=cv2.reduce(frame_gray_crop,1,cv2.REDUCE_MIN).min()
|
|
# threshold_value = min_val + thresh_add
|
|
if old_mode:
|
|
_, thresh = cv2.threshold(frame_gray_crop, min_val + thresh_add, 255, cv2.THRESH_BINARY)
|
|
else:
|
|
cv2.threshold(frame_gray_crop, min_val + thresh_add, 255, cv2.THRESH_BINARY, dst=th_frame)
|
|
# print(thresh.shape, frame_gray.shape)
|
|
try:
|
|
if old_mode:
|
|
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, self.kernel)
|
|
closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, self.kernel)
|
|
th_frame = 255 - closing
|
|
else:
|
|
cv2.morphologyEx(th_frame, cv2.MORPH_OPEN, self.kernel,dst=fic_frame)
|
|
cv2.morphologyEx(fic_frame, cv2.MORPH_CLOSE, self.kernel,dst=fic_frame)
|
|
cv2.bitwise_not(fic_frame,fic_frame)
|
|
# th_frame = 255 - closing
|
|
except Exception as e:
|
|
raise e
|
|
# I want to eliminate try here because try tends to be slow in execution.
|
|
fic_frame = 255 - frame_gray_crop
|
|
if old_mode:
|
|
contours, _ = cv2.findContours(th_frame, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
|
|
else:
|
|
contours = cv2.findContours(fic_frame, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[0]
|
|
# contours, _ = cv2.findContours(fic_frame, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
|
|
# or
|
|
# contours, _=cv2.findContours(th_frame, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
|
|
# cv2.findContours(th_frame, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
|
|
# cv2.findContours(th_frame, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
|
|
# cv2.findContours(th_frame, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
|
|
|
|
if not blink_bd and self.blink_detector.enable_detect_flg:
|
|
threshold_value = self.center_q1.quartile_1
|
|
if threshold_value < min_val + thresh_add:
|
|
# In most of these cases, the pupil is at the edge of the eye.
|
|
if old_mode:
|
|
thresh = cv2.threshold(frame_gray_crop, (min_val + thresh_add * 4 + threshold_value) / 2, 255, cv2.THRESH_BINARY)[1]
|
|
else:
|
|
cv2.threshold(frame_gray_crop, (min_val + thresh_add * 4 + threshold_value) / 2, 255, cv2.THRESH_BINARY,dst=th_frame)
|
|
else:
|
|
threshold_value = self.center_q1.quartile_1
|
|
if old_mode:
|
|
_, thresh = cv2.threshold(frame_gray_crop, threshold_value, 255, cv2.THRESH_BINARY)
|
|
else:
|
|
cv2.threshold(frame_gray_crop, threshold_value, 255, cv2.THRESH_BINARY,dst=th_frame)
|
|
try:
|
|
if old_mode:
|
|
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, self.kernel)
|
|
closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, self.kernel)
|
|
th_frame = 255 - closing
|
|
else:
|
|
cv2.morphologyEx(th_frame, cv2.MORPH_OPEN, self.kernel, dst=fic_frame)
|
|
cv2.morphologyEx(fic_frame, cv2.MORPH_CLOSE, self.kernel, dst=fic_frame)
|
|
cv2.bitwise_not(fic_frame, fic_frame)
|
|
except Exception as e:
|
|
raise e
|
|
# I want to eliminate try here because try tends to be slow in execution.
|
|
fic_frame = 255 - frame_gray_crop
|
|
if old_mode:
|
|
contours2, _ = cv2.findContours(th_frame, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
|
|
contours = (*contours, *contours2)
|
|
else:
|
|
# contours2, _ = cv2.findContours(fic_frame, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)#cv2.CHAIN_APPROX_NONE)
|
|
# contours2 = cv2.findContours(fic_frame, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[0]
|
|
contours = (*contours, *cv2.findContours(fic_frame, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[0])
|
|
# contours = (*contours, *contours2)
|
|
|
|
# hull = []
|
|
# # This way is faster than contours[i]
|
|
# # But maybe this one is faster. hull = [cv2.convexHull(cnt, False) for cnt in contours]
|
|
# for cnt in contours:
|
|
# # pass
|
|
# hull.append(cv2.convexHull(cnt, False))
|
|
if not contours:
|
|
# If empty, go to next loop
|
|
return int(center_x), int(center_y), th_frame, frame, gray_frame
|
|
if old_mode:
|
|
hull = [cv2.convexHull(cnt, False) for cnt in contours]
|
|
else:
|
|
cnt_ind = None
|
|
max_area = -1
|
|
for i, cnt in enumerate(contours):
|
|
now_area = cv2.contourArea(cnt)
|
|
if max_area < now_area:
|
|
max_area = now_area
|
|
cnt_ind = i
|
|
hull = cv2.convexHull(contours[cnt_ind], False)
|
|
# if not hull:
|
|
# If empty, go to next loop
|
|
# return int(center_x), int(center_y), th_frame, frame, gray_frame
|
|
if 1:
|
|
if old_mode:
|
|
cnt = sorted(hull, key=cv2.contourArea)
|
|
maxcnt = cnt[-1]
|
|
else:
|
|
maxcnt = hull
|
|
# ellipse = cv2.fitEllipse(maxcnt)
|
|
if old_mode:
|
|
ransac_data = fit_rotated_ellipse_ransac_old(maxcnt.reshape(-1, 2), self.rng)
|
|
else:
|
|
ransac_data = fit_rotated_ellipse_ransac(maxcnt.reshape(-1, 2).astype(np.float64), self.sfc)
|
|
if ransac_data is None:
|
|
# ransac_data is None==maxcnt.shape[0]<sample_num
|
|
# go to next loop
|
|
# pass
|
|
return int(center_x), int(center_y), th_frame, frame, gray_frame
|
|
|
|
# crop_start_time = timeit.default_timer()
|
|
cx, cy, w, h, theta = ransac_data
|
|
# print(cx, cy)
|
|
if w >= 2.1 * h: # new blink detection algo lmao this works pretty good actually
|
|
print("RAN BLINK")
|
|
# return center_x, center_y, frame, frame, True
|
|
|
|
# cx = center_x - (csx - cx) # we find the difference between the crop size and ransac point, and subtract from the center point from HSF
|
|
# cy = center_y - (csy - cy)
|
|
|
|
# csy = frame.shape[0]
|
|
# csx = frame.shape[1]
|
|
csy = gray_frame.shape[0]
|
|
csx = gray_frame.shape[1]
|
|
|
|
# cx = clamp((cx - 20) + center_x, 0, csx)
|
|
# cy = clamp((cy - 20) + center_y, 0, csy)
|
|
cx = int(clamp(cx + ransac_xy_offset[0], 0, csx))
|
|
cy = int(clamp(cy + ransac_xy_offset[1], 0, csy))
|
|
|
|
# cv_end_time = timeit.default_timer()
|
|
if imsave_flg:
|
|
|
|
cv2.circle(ori_frame, (int(center_x), int(center_y)), 3, (0, 255, 0), -1)
|
|
cv2.drawContours(ori_frame, contours, -1, (255, 0, 0), 1)
|
|
cv2.circle(ori_frame, (int(cx), int(cy)), 2, (0, 0, 255), -1)
|
|
# cx1, cy1, w1, h1, theta1 = fit_rotated_ellipse(maxcnt.reshape(-1, 2))
|
|
# cv2.ellipse(
|
|
# ori_frame,
|
|
# (cx, cy),
|
|
# (int(w), int(h)),
|
|
# theta * 180.0 / np.pi,
|
|
# 0.0,
|
|
# 360.0,
|
|
# (50, 250, 200),
|
|
# 1,
|
|
# )
|
|
# cv2.imshow("crop", cropped_image)
|
|
# cv2.imshow("frame", frame)
|
|
if imshow_enable:
|
|
cv2.imshow("ori_frame", ori_frame)
|
|
if cv2.waitKey(1) & 0xFF == ord("q"):
|
|
pass
|
|
|
|
# except Exception as e:
|
|
# print(e)
|
|
# pass
|
|
|
|
# debug code
|
|
# try:
|
|
# if any([isinstance(val, float) for val in [cx, cy]]):
|
|
# print()
|
|
# return int(cx), int(cy),cropbox, ori_frame,thresh, frame, gray_frame
|
|
# except:
|
|
# if any([isinstance(val, float) for val in [center_x, center_y]]):
|
|
# print()
|
|
# return center_x, center_y,cropbox, ori_frame,thresh, frame, gray_frame
|
|
# print(frame_gray.shape, thresh.shape)
|
|
|
|
cv_end_time = timeit.default_timer()
|
|
self.timedict["ransac"].append(cv_end_time - ransac_start_time)
|
|
self.timedict["total_cv"].append(cv_end_time - cv_start_time)
|
|
|
|
try:
|
|
return int(cx), int(cy), th_frame, frame, gray_frame
|
|
except:
|
|
return int(center_x), int(center_y), th_frame, frame, gray_frame
|
|
|
|
|
|
if __name__ == "__main__":
|
|
# print(np.show_config())
|
|
logger.info(this_file_basename)
|
|
if save_logfile:
|
|
logger.info("log path: {}".format(logfilename))
|
|
logger.info("alg ver: {}".format(alg_ver))
|
|
logger.info("alg mode: {}".format("old" if old_mode else "new"))
|
|
logger.info("loops: {}".format(loop_num))
|
|
logger.info("video name: {}".format(os.path.basename(input_video_path)))
|
|
cap = cv2.VideoCapture(input_video_path)
|
|
logger.info("video info: size:{}x{} fps:{} frames:{} total:{:.3f} sec".format(int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
|
|
int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)),
|
|
cap.get(cv2.CAP_PROP_FPS),
|
|
int(cap.get(cv2.CAP_PROP_FRAME_COUNT)),
|
|
cap.get(cv2.CAP_PROP_FRAME_COUNT) / cap.get(cv2.CAP_PROP_FPS)))
|
|
cap.release()
|
|
|
|
if not print_enable:
|
|
def print(*args, **kwargs):
|
|
pass
|
|
|
|
hsrac = HSRAC_cls()
|
|
# For measuring total processing time
|
|
main_start_time = timeit.default_timer()
|
|
|
|
for i in range(loop_num):
|
|
hsrac.open_video(input_video_path)
|
|
|
|
while hsrac.read_frame():
|
|
if imsave_flg:
|
|
base_gray = hsrac.current_image_gray.copy()
|
|
base_img = hsrac.current_image.copy()
|
|
cv2.imshow("frame", base_gray)
|
|
hsf_x, hsf_y, hsf_cropbox, *_ = hsrac.single_run()
|
|
|
|
# # hsrac_x, hsrac_y, hsrac_cropbox, *_ = er_hsracs.run(base_gray)
|
|
# if 0:#random.random()<0.1:
|
|
# hsrac_x, hsrac_y, hsrac_cropbox, *_ = er_hsracs.run(cv2.resize(base_gray,None,fx=0.75,fy=0.75).copy())
|
|
# hsrac_x=int(hsrac_x*1.25)
|
|
# hsrac_y=int(hsrac_y*1.25)
|
|
# hsrac_cropbox=[int(val*1.25) for val in hsrac_cropbox]
|
|
# else:
|
|
# hsrac_x, hsrac_y, hsrac_cropbox,ori_frame, *_ = er_hsracs.run(base_gray)
|
|
# cv2.rectangle(base_img, hsf_cropbox[:2], hsf_cropbox[2:], (0, 0, 255), 3)
|
|
# cv2.rectangle(base_img, hsrac_cropbox[:2], hsrac_cropbox[2:], (255, 0, 0), 1)
|
|
cv2.circle(base_img, (hsf_x, hsf_y), 6, (0, 0, 255), -1)
|
|
# try:
|
|
# cv2.circle(base_img, (hsrac_x, hsrac_y), 3, (255, 0, 0), -1)
|
|
# except:
|
|
# print()
|
|
if imshow_enable:
|
|
cv2.imshow("hsf_hsrac", base_img)
|
|
if save_video:
|
|
video_wr.write(cv2.resize(base_img, (200, 150)))
|
|
if cv2.waitKey(1) & 0xFF == ord("q"):
|
|
pass
|
|
else:
|
|
_ = hsrac.single_run()
|
|
|
|
if save_video:
|
|
video_wr.release()
|
|
hsrac.cap.release()
|
|
cv2.destroyAllWindows()
|
|
main_end_time = timeit.default_timer()
|
|
main_total_time = main_end_time - main_start_time
|
|
if not print_enable:
|
|
# del print
|
|
# or
|
|
print = __builtins__.print
|
|
logger.info("")
|
|
for k, v in hsrac.timedict.items():
|
|
# number=1, precision=5
|
|
len_v = len(v)
|
|
best = min(v) # / number
|
|
worst = max(v) # / number
|
|
logger.info(k + ":")
|
|
logger.info(TimeitResult(loop_num, len_v, best, worst, v, 5))
|
|
logger.info(FPSResult(loop_num, len_v, worst, best, v, 5))
|
|
# print("")
|
|
logger.info("")
|
|
logger.info(f"{this_file_basename}: ALL Finish {format_time(main_total_time)}") |