mirror of
https://github.com/EyeTrackVR/EyeTrackVR.git
synced 2025-11-04 14:39:42 +08:00
Remove unused functions
This commit is contained in:
parent
56509d60fe
commit
c6413f8ee1
@ -1,52 +1,54 @@
|
||||
import math
|
||||
import os
|
||||
import timeit
|
||||
from functools import lru_cache
|
||||
|
||||
from logging import getLogger, Formatter, StreamHandler, FileHandler, INFO
|
||||
|
||||
old_mode=True#False
|
||||
|
||||
this_file_name = os.path.basename(__file__)
|
||||
logger = getLogger(__name__)
|
||||
logger.setLevel(INFO)
|
||||
formatter = Formatter('%(message)s')
|
||||
handler = StreamHandler()
|
||||
handler.setLevel(INFO)
|
||||
handler.setFormatter(formatter)
|
||||
logger.addHandler(handler)
|
||||
|
||||
# handler = FileHandler(f'./{this_file_name.replace(".py","")}2.log' if not old_mode else f'./{this_file_name.replace(".py","")}.log',encoding="utf8",mode="w")
|
||||
# handler.setLevel(INFO)
|
||||
# handler.setFormatter(formatter)
|
||||
# logger.addHandler(handler)
|
||||
|
||||
from logging import Formatter, INFO, StreamHandler,FileHandler, getLogger
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
from numpy.linalg import _umath_linalg
|
||||
from EyeTrackApp.utils.time_utils import FPSResult, TimeitResult, format_time
|
||||
from EyeTrackApp.haar_surround_feature import (
|
||||
AutoRadiusCalc,
|
||||
BlinkDetector,
|
||||
# CvParameters,
|
||||
# conv_int,
|
||||
# frameint_get_xy_step,
|
||||
)
|
||||
|
||||
from EyeTrackApp.utils.img_utils import safe_crop
|
||||
from EyeTrackApp.utils.misc_utils import clamp
|
||||
import math
|
||||
from line_profiler_pycharm import profile
|
||||
from EyeTrackApp.utils.time_utils import FPSResult, TimeitResult, format_time
|
||||
|
||||
# from line_profiler_pycharm import profile
|
||||
|
||||
this_file_basename = os.path.basename(__file__)
|
||||
this_file_name = this_file_basename.replace(".py", "")
|
||||
alg_ver = "230314-1" # Do not change it.
|
||||
|
||||
|
||||
##############################
|
||||
# These can be changed
|
||||
old_mode = False
|
||||
save_logfile = False # This setting is disabled when imshow_enable or save_video is true
|
||||
imshow_enable = False
|
||||
save_video = False
|
||||
loop_num = 1 if imshow_enable or save_video else 100
|
||||
input_video_path = "Pro_demo2.mp4"
|
||||
output_video_path = f'./{this_file_name}_{alg_ver}_new.mp4' if not old_mode else f'./{this_file_name}_{alg_ver}_old.mp4'
|
||||
logfilename = f'./{this_file_name}_{alg_ver}_new.log' if not old_mode else f'./{this_file_name}_old.log'
|
||||
print_enable = False # I don't recommend changing to True.
|
||||
|
||||
|
||||
|
||||
# RANSAC
|
||||
|
||||
thresh_add = 10
|
||||
|
||||
# imshow_enable = True
|
||||
# calc_print_enable = True
|
||||
print_enable = False
|
||||
save_video = False
|
||||
skip_autoradius = False
|
||||
skip_blink_detect = False
|
||||
##############################
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
##############################
|
||||
# Do not change these.
|
||||
|
||||
imsave_flg = imshow_enable or save_video
|
||||
|
||||
# cache param
|
||||
lru_maxsize_vvs = 16
|
||||
@ -61,6 +63,170 @@ blink_init_frames = 60 * 3 # 60fps*3sec,Number of blink statistical frames
|
||||
default_step = (5, 5) # bigger the steps,lower the processing time! ofc acc also takes an impact
|
||||
|
||||
|
||||
logger = getLogger(__name__)
|
||||
logger.setLevel(INFO)
|
||||
formatter = Formatter('%(message)s')
|
||||
handler = StreamHandler()
|
||||
handler.setLevel(INFO)
|
||||
handler.setFormatter(formatter)
|
||||
logger.addHandler(handler)
|
||||
if save_logfile and not imsave_flg:
|
||||
handler = FileHandler(logfilename, encoding="utf8", mode="w")
|
||||
handler.setLevel(INFO)
|
||||
handler.setFormatter(formatter)
|
||||
logger.addHandler(handler)
|
||||
else:
|
||||
save_logfile = False
|
||||
|
||||
|
||||
video_wr = cv2.VideoWriter(output_video_path, cv2.VideoWriter_fourcc(*"x264"), 60.0, (200, 150)) if save_video else None
|
||||
|
||||
##############################
|
||||
|
||||
|
||||
class AutoRadiusCalc(object):
|
||||
def __init__(self):
|
||||
self.response_list = []
|
||||
self.radius_cand_list = []
|
||||
self.adj_comp_flag = False
|
||||
|
||||
self.radius_middle_index = None
|
||||
|
||||
self.left_item = None
|
||||
self.right_item = None
|
||||
self.left_index = None
|
||||
self.right_index = None
|
||||
|
||||
def get_radius(self):
|
||||
prev_res_len = len(self.response_list)
|
||||
# adjustment of radius
|
||||
if prev_res_len == 1:
|
||||
# len==1==response_list==[default_radius]
|
||||
self.adj_comp_flag = False
|
||||
return auto_radius_range[0]
|
||||
elif prev_res_len == 2:
|
||||
# len==2==response_list==[default_radius, auto_radius_range[0]]
|
||||
self.adj_comp_flag = False
|
||||
return auto_radius_range[1]
|
||||
elif prev_res_len == 3:
|
||||
# len==3==response_list==[default_radius,auto_radius_range[0],auto_radius_range[1]]
|
||||
if self.response_list[1][1] < self.response_list[2][1]:
|
||||
self.left_item = self.response_list[1]
|
||||
self.right_item = self.response_list[0]
|
||||
else:
|
||||
self.left_item = self.response_list[0]
|
||||
self.right_item = self.response_list[2]
|
||||
self.radius_cand_list = [i for i in range(self.left_item[0], self.right_item[0] + auto_radius_step, auto_radius_step)]
|
||||
self.left_index = 0
|
||||
self.right_index = len(self.radius_cand_list) - 1
|
||||
self.radius_middle_index = (self.left_index + self.right_index) // 2
|
||||
self.adj_comp_flag = False
|
||||
return self.radius_cand_list[self.radius_middle_index]
|
||||
else:
|
||||
if self.left_index <= self.right_index and self.left_index != self.radius_middle_index:
|
||||
if (self.left_item[1] + self.response_list[-1][1]) < (self.right_item[1] + self.response_list[-1][1]):
|
||||
self.right_item = self.response_list[-1]
|
||||
self.right_index = self.radius_middle_index - 1
|
||||
self.radius_middle_index = (self.left_index + self.right_index) // 2
|
||||
self.adj_comp_flag = False
|
||||
return self.radius_cand_list[self.radius_middle_index]
|
||||
if (self.left_item[1] + self.response_list[-1][1]) > (self.right_item[1] + self.response_list[-1][1]):
|
||||
self.left_item = self.response_list[-1]
|
||||
self.left_index = self.radius_middle_index + 1
|
||||
self.radius_middle_index = (self.left_index + self.right_index) // 2
|
||||
self.adj_comp_flag = False
|
||||
return self.radius_cand_list[self.radius_middle_index]
|
||||
self.adj_comp_flag = True
|
||||
return self.radius_cand_list[self.radius_middle_index]
|
||||
|
||||
def get_radius_base(self):
|
||||
"""
|
||||
Use it when the new version doesn't work well.
|
||||
:return:
|
||||
"""
|
||||
|
||||
prev_res_len = len(self.response_list)
|
||||
# adjustment of radius
|
||||
if prev_res_len == 1:
|
||||
# len==1==response_list==[default_radius]
|
||||
self.adj_comp_flag = False
|
||||
return auto_radius_range[0]
|
||||
elif prev_res_len == 2:
|
||||
# len==2==response_list==[default_radius, auto_radius_range[0]]
|
||||
self.adj_comp_flag = False
|
||||
return auto_radius_range[1]
|
||||
elif prev_res_len == 3:
|
||||
# len==3==response_list==[default_radius,auto_radius_range[0],auto_radius_range[1]]
|
||||
sort_res = sorted(self.response_list, key=lambda x: x[1])[0]
|
||||
# Extract the radius with the lowest response value
|
||||
if sort_res[0] == default_radius:
|
||||
# If the default value is best, change now_mode to init after setting radius to the default value.
|
||||
self.adj_comp_flag = True
|
||||
return default_radius
|
||||
elif sort_res[0] == auto_radius_range[0]:
|
||||
self.radius_cand_list = [i for i in range(auto_radius_range[0], default_radius, auto_radius_step)][1:]
|
||||
self.adj_comp_flag = False
|
||||
return self.radius_cand_list.pop()
|
||||
else:
|
||||
self.radius_cand_list = [i for i in range(default_radius, auto_radius_range[1], auto_radius_step)][1:]
|
||||
self.adj_comp_flag = False
|
||||
return self.radius_cand_list.pop()
|
||||
else:
|
||||
# Try the contents of the radius_cand_list in order until the radius_cand_list runs out
|
||||
# Better make it a binary search.
|
||||
if len(self.radius_cand_list) == 0:
|
||||
sort_res = sorted(self.response_list, key=lambda x: x[1])[0]
|
||||
self.adj_comp_flag = True
|
||||
return sort_res[0]
|
||||
else:
|
||||
self.adj_comp_flag = False
|
||||
return self.radius_cand_list.pop()
|
||||
|
||||
def add_response(self, radius, response):
|
||||
self.response_list.append((radius, response))
|
||||
return None
|
||||
|
||||
|
||||
class BlinkDetector(object):
|
||||
def __init__(self):
|
||||
self.response_list = []
|
||||
self.response_max = None
|
||||
self.enable_detect_flg = False
|
||||
self.quartile_1 = None
|
||||
|
||||
def calc_thresh(self):
|
||||
# Calculate response_max by computing interquartile range, IQR
|
||||
# self.response_listo = np.array(self.response_listo)
|
||||
# 25%,75%
|
||||
# This value may need to be adjusted depending on the environment.
|
||||
# quartile_1, quartile_3 = np.percentile(self.response_listo, [25, 75])
|
||||
# iqr = quartile_3 - quartile_1
|
||||
# self.response_maxo = quartile_3 + (iqr * 1.5)
|
||||
|
||||
# quartile_1, quartile_3 = np.percentile(self.response_list, [25, 75])
|
||||
# or
|
||||
quartile_1, quartile_3 = np.percentile(np.array(self.response_list), [25, 75])
|
||||
self.quartile_1 = quartile_1
|
||||
iqr = quartile_3 - quartile_1
|
||||
# response_min = quartile_1 - (iqr * 1.5)
|
||||
|
||||
self.response_max = float(quartile_3 + (iqr * 1.5))
|
||||
# or
|
||||
# self.response_max = quartile_3 + (iqr * 1.5)
|
||||
|
||||
self.enable_detect_flg = True
|
||||
return None
|
||||
|
||||
def detect(self, now_response):
|
||||
return now_response > self.response_max
|
||||
|
||||
def add_response(self, response):
|
||||
self.response_list.append(response)
|
||||
return None
|
||||
|
||||
def response_len(self):
|
||||
return len(self.response_list)
|
||||
|
||||
def ellipse_model(data, y, f):
|
||||
"""
|
||||
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.
|
||||
@ -73,191 +239,7 @@ def ellipse_model(data, y, f):
|
||||
"""
|
||||
return data.dot(y) + f
|
||||
|
||||
|
||||
# from scipy.linalg import cho_factor, cho_solve
|
||||
import scipy
|
||||
|
||||
|
||||
# from scipy.linalg import solve
|
||||
|
||||
# def inv_sc(a):
|
||||
# orig_shape = a.shape
|
||||
# dim1 = int(math.sqrt(a.shape[0]))
|
||||
# # u, s, vt = scipy.linalg.svd(a.reshape((a.shape[0],a.shape[1] * a.shape[2])), full_matrices=False,compute_uv=True,overwrite_a=False, check_finite=False)
|
||||
# u, s, vt = scipy.linalg.svd(a.reshape(((a.shape[1] * dim1) + (a.shape[0] % 2), a.shape[2] * dim1)), full_matrices=False,
|
||||
# compute_uv=True, overwrite_a=False,
|
||||
# check_finite=False)
|
||||
# # rcond = np.array(1e-15)
|
||||
# # # discard small singular values
|
||||
# # cutoff = rcond[..., np.newaxis] * np.amax(s, axis=-1, keepdims=True)
|
||||
# # large = s > cutoff
|
||||
# # s = np.divide(1, s, where=large, out=s)
|
||||
# # s[~large] = 0
|
||||
#
|
||||
# res = np.matmul(vt.T, s[..., np.newaxis] * u.T)
|
||||
# return res.reshape(orig_shape)
|
||||
|
||||
|
||||
# @profile
|
||||
def fit_rotated_ellipse_ransac_bad(data: np.ndarray, rng: np.random.Generator, iter=100, sample_num=10, offset=80 # 80.0, 10, 80
|
||||
): # before changing these values, please read up on the ransac algorithm
|
||||
# However if you want to change any value just know that higher iterations will make processing frames slower
|
||||
# effective_sample = None
|
||||
|
||||
# The array contents do not change during the loop, so only one call is needed.
|
||||
# They say len is faster than shape.
|
||||
# Reference url: https://stackoverflow.com/questions/35547853/what-is-faster-python3s-len-or-numpys-shape
|
||||
len_data = len(data)
|
||||
|
||||
if len_data < sample_num:
|
||||
return None
|
||||
|
||||
# Type of calculation result
|
||||
ret_dtype = np.float64
|
||||
|
||||
# Sorts a random number array of size (iter,len_data). After sorting, returns the index of sample_num random numbers before sorting.
|
||||
# If the array size is less than about 100, this is faster than rng.choice.
|
||||
# rng_sample = rng.random((iter, len_data)).argsort()[:, :sample_num]
|
||||
# or
|
||||
# I don't see any advantage to doing this.
|
||||
# rng_sample = np.asarray(rng.random((iter, len_data)).argsort()[:, :sample_num], dtype=np.int32)
|
||||
|
||||
# I don't think it looks beautiful.
|
||||
# x,y,x**2,y**2,x*y,1,-1*x**2
|
||||
data_squared = np.square(data)
|
||||
datamod = np.empty((len(data), 7), dtype=ret_dtype) # np.empty((len(data), 7), dtype=ret_dtype)
|
||||
datamod[:, :2] = data
|
||||
datamod[:, 2:4] = data_squared
|
||||
datamod[:, 4] = data[:, 0] * data[:, 1]
|
||||
datamod[:, 5] = 1
|
||||
datamod[:, 6] = -data_squared[:, 0]
|
||||
|
||||
# datamod = np.concatenate(
|
||||
# [data, data_squared, (data[:, 0] * data[:, 1])[:, np.newaxis], np.ones((len_data, 1), dtype=ret_dtype),
|
||||
# (-data_squared[:,0])[:, np.newaxis]], axis=1,
|
||||
# dtype=ret_dtype)
|
||||
|
||||
# datamod_slim = datamod[:, :5]#np.array(datamod[:, :5], dtype=ret_dtype)
|
||||
#
|
||||
# datamod_rng = datamod[rng_sample]
|
||||
datamod_rng = rng.choice(datamod, (iter, sample_num), replace=True, shuffle=False)
|
||||
# datamod_rng6 = datamod_rng[:, :, 6]
|
||||
# datamod_rng_swap = datamod_rng[:, :, [4, 3, 0, 1, 5]]
|
||||
# datamod_rng_swap_trans = datamod_rng_swap.transpose((0, 2, 1))
|
||||
#
|
||||
# # These two lines are one of the bottlenecks
|
||||
# datamod_rng_5x5 = np.matmul(datamod_rng_swap_trans, datamod_rng_swap)
|
||||
# datamod_rng_p5smp = np.matmul(np.linalg.inv(datamod_rng_5x5), datamod_rng_swap_trans)
|
||||
#
|
||||
# datamod_rng_p = np.matmul(datamod_rng_p5smp, datamod_rng6[:, :, np.newaxis]).reshape((-1, 5))
|
||||
#
|
||||
# # I don't think it looks beautiful.
|
||||
# ellipse_y_arr = np.asarray(
|
||||
# [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)
|
||||
#
|
||||
# ellipse_data_arr = ellipse_model(datamod_slim, ellipse_y_arr, np.asarray(datamod_rng_p[:, 4])).transpose((1, 0))
|
||||
# ellipse_data_abs = np.abs(ellipse_data_arr)
|
||||
# ellipse_data_index = np.argmax(np.sum(ellipse_data_abs < offset, axis=1), axis=0)
|
||||
# effective_data_arr = ellipse_data_arr[ellipse_data_index]
|
||||
# effective_sample_p_arr = datamod_rng_p[ellipse_data_index]
|
||||
# datamod_rng[:, [3, 4]] = datamod_rng[:, [4, 3]] # Swap columns 3 and 4
|
||||
|
||||
# datamod_rng_swap = datamod_rng[..., [2, 3, 4, 1, 0, 5]]
|
||||
datamod_rng_swap = datamod_rng[:, :, [4, 3, 0, 1, 5]]
|
||||
|
||||
# datamod_rng_5x5 = np.matmul(datamod_rng[:, :, None, :].transpose(0,1,3,2), datamod_rng[:, :, None, :]).squeeze()#np.matmul(datamod_rng[:, :, None, :].transpose(0,1,3,2), datamod_rng[:, :, :, None]).squeeze()
|
||||
# datamod_rng_5x5_inv = np.linalg.inv(datamod_rng_5x5)
|
||||
datamod_rng_swap_trans = datamod_rng_swap.transpose(0, 2, 1)
|
||||
# datamod_rng_5x5 = datamod_rng_swap_trans@datamod_rng_swap#np.matmul(datamod_rng_swap_trans, datamod_rng_swap)
|
||||
# datamod_rng_p5smp = np.matmul(np.linalg.inv(datamod_rng_5x5), datamod_rng_swap_trans)
|
||||
datamod_rng6 = datamod_rng[:, :, 6]
|
||||
# These two lines are one of the bottlenecks
|
||||
datamod_rng_5x5 = np.matmul(datamod_rng_swap_trans, datamod_rng_swap)
|
||||
datamod_rng_p5smp = np.matmul(np.linalg.inv(datamod_rng_5x5), datamod_rng_swap_trans)
|
||||
|
||||
datamod_rng_p = np.matmul(datamod_rng_p5smp, datamod_rng6[:, :, np.newaxis]).reshape((-1, 5))
|
||||
|
||||
# inv_sc(datamod_rng_5x5)
|
||||
# datamod_rng_p5smp = np.linalg.inv(datamod_rng_5x5) @ datamod_rng_swap_trans
|
||||
# chol = np.linalg.cholesky(datamod_rng_5x5)
|
||||
# datamod_rng_p5smp = np.linalg.solve(chol.T, np.linalg.solve(chol, datamod_rng_swap_trans))
|
||||
# Compute the Cholesky factorization of datamod_rng_5x5
|
||||
|
||||
# datamod_rng_p5smp = solve(datamod_rng_5x5, datamod_rng_swap_trans)
|
||||
# Q, R = np.linalg.qr(datamod_rng_5x5)
|
||||
# datamod_rng_p5smp = np.matmul(Q.T, np.matmul(Q, datamod_rng_swap_trans))
|
||||
# datamod_rng_p5smp=np.linalg.solve(datamod_rng_5x5,datamod_rng_swap_trans)
|
||||
# datamod_rng_p = np.matmul(datamod_rng_5x5_inv, datamod_rng[:, :, 5])[:, :5]
|
||||
|
||||
# datamod_rng_p=np.matmul(datamod_rng_p5smp, datamod_rng[..., 6,np.newaxis]).reshape(-1, 5)
|
||||
# datamod_rng_p=np.matmul(datamod_rng_p5smp, datamod_rng[..., 6, np.newaxis])[:, :5].reshape((-1, 5))
|
||||
ellipse_y_arr = np.asarray(
|
||||
[datamod_rng_p[:, 2], datamod_rng_p[:, 3], np.ones(len(datamod_rng_p), dtype=ret_dtype), datamod_rng_p[:, 1], datamod_rng_p[:, 0]],
|
||||
dtype=ret_dtype)
|
||||
ellipse_data_arr = np.dot(datamod[:, :5], ellipse_y_arr) + datamod_rng_p[:,
|
||||
4] # np.dot(datamod[:, :5], ellipse_y_arr) + datamod_rng_p[:, 4, None]
|
||||
ellipse_data_abs = np.abs(ellipse_data_arr)
|
||||
# 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)
|
||||
effective_data_arr = ellipse_data_arr[ellipse_data_index]
|
||||
effective_sample_p_arr = datamod_rng_p[ellipse_data_index]
|
||||
return fit_rotated_ellipse2(effective_data_arr, effective_sample_p_arr)
|
||||
|
||||
|
||||
# @profile
|
||||
def fit_rotated_ellipse2(data, P):
|
||||
a = 1.0
|
||||
b, c, d, e, f = P[:5]
|
||||
# The cost of trigonometric functions is high.
|
||||
# theta = 0.5 * np.arctan(b / (a - c), dtype=np.float64)
|
||||
# theta = 0.5 * np.arctan2(b, a - c, dtype=np.float64)
|
||||
theta = 0.5 * math.atan(b / (a - c))
|
||||
# theta_sin = np.sin(theta, dtype=np.float64)
|
||||
# theta_cos = np.cos(theta, dtype=np.float64)
|
||||
theta_sin = math.sin(theta)
|
||||
theta_cos = 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, h = wh[0], wh[1]
|
||||
|
||||
error_sum = np.sum(data)
|
||||
# print("fitting error = %.3f" % (error_sum))
|
||||
|
||||
return (cx, cy, w, h, theta)
|
||||
|
||||
|
||||
def fit_rotated_ellipse3(data, P):
|
||||
# a, b, c, d, e, f = P
|
||||
a = 1.0
|
||||
b, c, d, e, f = P # [:5]
|
||||
theta = 0.5 * math.atan2(b, a - c)
|
||||
ct, st = math.cos(theta), math.sin(theta)
|
||||
a2 = a * ct ** 2 + b * ct * st + c * st ** 2
|
||||
b2 = a * st ** 2 - b * ct * st + c * ct ** 2
|
||||
cu = a * d ** 2 + b * d * e + c * e ** 2 - f * a2 * b2
|
||||
# wh = np.sqrt(abs(cu / (a2+b2)))
|
||||
wh = [math.sqrt(abs(cu / a2)), math.sqrt(abs(cu / b2))]
|
||||
w, h = wh[0], wh[1]
|
||||
cx = (b * e - 2 * c * d) / (4 * a2 * b2 - c ** 2)
|
||||
cy = (b * d - 2 * a * e) / (4 * a2 * b2 - c ** 2)
|
||||
error_sum = np.sum(data)
|
||||
return cx, cy, w, h, theta
|
||||
|
||||
|
||||
def fit_rotated_ellipse_ransac_base(data: np.ndarray, rng: np.random.Generator, iter=100, sample_num=10,
|
||||
def fit_rotated_ellipse_ransac_old(data: np.ndarray, rng: np.random.Generator, iter=100, sample_num=10,
|
||||
offset=80): # before changing these values, please read up on the ransac algorithm
|
||||
# However if you want to change any value just know that higher iterations will make processing frames slower
|
||||
effective_sample = None
|
||||
@ -310,11 +292,11 @@ def fit_rotated_ellipse_ransac_base(data: np.ndarray, rng: np.random.Generator,
|
||||
effective_data_arr = ellipse_data_arr[ellipse_data_index]
|
||||
effective_sample_p_arr = datamod_rng_p[ellipse_data_index]
|
||||
|
||||
return fit_rotated_ellipse_base(effective_data_arr, effective_sample_p_arr)
|
||||
return fit_rotated_ellipse_old(effective_data_arr, effective_sample_p_arr)
|
||||
|
||||
|
||||
# @profile
|
||||
def fit_rotated_ellipse_base(data, P):
|
||||
def fit_rotated_ellipse_old(data, P):
|
||||
a = 1.0
|
||||
b = P[0]
|
||||
c = P[1]
|
||||
@ -488,8 +470,18 @@ def fit_rotated_ellipse_ransac(data: np.ndarray, sfc: np.random.Generator, iter_
|
||||
# I want to use cv2.mulTransposed, but for some reason the results are different and it can only use 1-channel arrays.
|
||||
# np.linalg.inv(datamod_rng_5x5)
|
||||
# datamod_rng_5x5[:,:,:]=np.linalg.inv(datamod_rng_5x5)
|
||||
_umath_linalg.inv(datamod_rng_5x5,out=datamod_rng_5x5)# check error
|
||||
# _umath_linalg.inv(datamod_rng_5x5,out=datamod_rng_5x5)# check error
|
||||
# https://github.com/bogovicj/JaneliaMLCourse/issues/1
|
||||
# solve is slow
|
||||
# np.linalg.solve(np.matmul(datamod_rng_swap_trans, datamod_rng_swap), datamod_rng_swap_trans)
|
||||
_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)
|
||||
|
||||
np.matmul(datamod_rng_5x5, datamod_rng_swap_trans,out=datamod_rng_p5smp)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# global ein_path
|
||||
# if ein_path is None:
|
||||
# ein_path = np.einsum_path("ijk,ijl->ikl", datamod_rng_swap, datamod_rng_swap, optimize='optimal')[0]#'optimal','greedy'
|
||||
@ -648,7 +640,7 @@ def fit_rotated_ellipse(data, P):
|
||||
return cx, cy, w, h, theta
|
||||
|
||||
|
||||
class CvParameters_base:
|
||||
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
|
||||
@ -656,7 +648,7 @@ class CvParameters_base:
|
||||
self.pad = 2 * radius
|
||||
# self.prev_step=step
|
||||
self._step = step
|
||||
self._hsf = HaarSurroundFeature_base(radius)
|
||||
self._hsf = HaarSurroundFeature_old(radius)
|
||||
|
||||
def get_rpsh(self):
|
||||
return self._radius, self.pad, self._step, self._hsf
|
||||
@ -689,10 +681,10 @@ class CvParameters_base:
|
||||
|
||||
@hsf.setter
|
||||
def hsf(self, now_radius):
|
||||
self._hsf = HaarSurroundFeature_base(now_radius)
|
||||
self._hsf = HaarSurroundFeature_old(now_radius)
|
||||
|
||||
|
||||
class HaarSurroundFeature_base:
|
||||
class HaarSurroundFeature_old:
|
||||
|
||||
def __init__(self, r_inner, r_outer=None, val=None):
|
||||
if r_outer is None:
|
||||
@ -728,7 +720,7 @@ class HaarSurroundFeature_base:
|
||||
return kernel
|
||||
|
||||
@lru_cache(maxsize=lru_maxsize_vvs)
|
||||
def get_hsf_empty_array_base(len_syx, frameint_x, frame_int_dtype, fcshape):
|
||||
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)
|
||||
@ -744,7 +736,7 @@ def get_hsf_empty_array_base(len_syx, frameint_x, frame_int_dtype, fcshape):
|
||||
|
||||
|
||||
@lru_cache(maxsize=lru_maxsize_vs)
|
||||
def frameint_get_xy_step_base(imageshape, xysteps, pad, start_offset=None, end_offset=None):
|
||||
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)
|
||||
@ -776,7 +768,7 @@ def frameint_get_xy_step_base(imageshape, xysteps, pad, start_offset=None, end_o
|
||||
|
||||
|
||||
# @profile
|
||||
def conv_int_base(frame_int, kernel, xy_step, padding, xy_steps_list):
|
||||
def conv_int_old(frame_int, kernel, xy_step, padding, xy_steps_list):
|
||||
"""
|
||||
:param frame_int:
|
||||
:param kernel: hsf
|
||||
@ -793,7 +785,7 @@ def conv_int_base(frame_int, kernel, xy_step, padding, xy_steps_list):
|
||||
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_base((len_sy, len_sx), col + 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
|
||||
@ -1388,8 +1380,9 @@ class HSRAC_cls(object):
|
||||
|
||||
self.rng = np.random.default_rng()
|
||||
if old_mode:
|
||||
self.cvparam = CvParameters_base(default_radius, default_step)
|
||||
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"]
|
||||
@ -1437,7 +1430,8 @@ class HSRAC_cls(object):
|
||||
# Temporary implementation to run
|
||||
## default_radius = 14
|
||||
|
||||
# ori_frame = self.current_image.copy()# debug code
|
||||
if imshow_enable or save_video:
|
||||
ori_frame = self.current_image.copy()# debug code
|
||||
# cropbox=[] # debug code
|
||||
|
||||
blink_bd = False
|
||||
@ -1479,8 +1473,8 @@ class HSRAC_cls(object):
|
||||
# Convolve the feature with the integral image
|
||||
conv_int_start_time = timeit.default_timer()
|
||||
if old_mode:
|
||||
xy_step = frameint_get_xy_step_base(frame_int.shape, step, pad, start_offset=None, end_offset=None)
|
||||
frame_conv, response, center_xy = conv_int_base(frame_int, hsf, step, pad, xy_step)
|
||||
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)
|
||||
@ -1593,7 +1587,8 @@ class HSRAC_cls(object):
|
||||
frame_gray = cv2.sepFilter2D(frame, -1, self.gauss_k, self.gauss_k)
|
||||
|
||||
|
||||
#todo:no numpy and int and use lru
|
||||
#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)
|
||||
@ -1607,7 +1602,7 @@ class HSRAC_cls(object):
|
||||
#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
|
||||
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]
|
||||
@ -1710,7 +1705,7 @@ class HSRAC_cls(object):
|
||||
maxcnt = hull
|
||||
# ellipse = cv2.fitEllipse(maxcnt)
|
||||
if old_mode:
|
||||
ransac_data = fit_rotated_ellipse_ransac_base(maxcnt.reshape(-1, 2), self.rng)
|
||||
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:
|
||||
@ -1740,7 +1735,7 @@ class HSRAC_cls(object):
|
||||
cy = int(clamp(cy + ransac_xy_offset[1], 0, csy))
|
||||
|
||||
# cv_end_time = timeit.default_timer()
|
||||
if 0: # imshow_enable or save_video:
|
||||
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)
|
||||
@ -1758,6 +1753,7 @@ class HSRAC_cls(object):
|
||||
# )
|
||||
# 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
|
||||
@ -1788,21 +1784,22 @@ class HSRAC_cls(object):
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
loop_num = 100
|
||||
|
||||
logger.info(this_file_name)
|
||||
video_path = "Pro_demo2.mp4"
|
||||
cap = cv2.VideoCapture(video_path)
|
||||
logger.info("video: size:{}x{} fps:{} frames:{} total:{:.3f} sec".format(int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
|
||||
# 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()
|
||||
filepath = 'test.mp4'
|
||||
codec = cv2.VideoWriter_fourcc(*"x264")
|
||||
# video = cv2.VideoWriter(filepath, codec, 60.0, (200,150))#(60, 60)) # (150, 200))
|
||||
|
||||
if not print_enable:
|
||||
def print(*args, **kwargs):
|
||||
pass
|
||||
@ -1810,11 +1807,12 @@ if __name__ == "__main__":
|
||||
hsrac = HSRAC_cls()
|
||||
# For measuring total processing time
|
||||
main_start_time = timeit.default_timer()
|
||||
|
||||
for i in range(loop_num):
|
||||
hsrac.open_video(video_path)
|
||||
hsrac.open_video(input_video_path)
|
||||
|
||||
while hsrac.read_frame():
|
||||
if 1:
|
||||
if imsave_flg:
|
||||
base_gray = hsrac.current_image_gray.copy()
|
||||
base_img = hsrac.current_image.copy()
|
||||
cv2.imshow("frame", base_gray)
|
||||
@ -1835,15 +1833,17 @@ if __name__ == "__main__":
|
||||
# cv2.circle(base_img, (hsrac_x, hsrac_y), 3, (255, 0, 0), -1)
|
||||
# except:
|
||||
# print()
|
||||
if imshow_enable:
|
||||
cv2.imshow("hsf_hsrac", base_img)
|
||||
video.write(cv2.resize(base_img, (200, 150)))
|
||||
if save_video:
|
||||
video_wr.write(cv2.resize(base_img, (200, 150)))
|
||||
if cv2.waitKey(1) & 0xFF == ord("q"):
|
||||
pass
|
||||
else:
|
||||
_ = hsrac.single_run()
|
||||
|
||||
# _ = hsrac.single_run()
|
||||
video.release()
|
||||
if save_video:
|
||||
video_wr.release()
|
||||
hsrac.cap.release()
|
||||
cv2.destroyAllWindows()
|
||||
main_end_time = timeit.default_timer()
|
||||
@ -1856,8 +1856,6 @@ if __name__ == "__main__":
|
||||
for k, v in hsrac.timedict.items():
|
||||
# number=1, precision=5
|
||||
len_v = len(v)
|
||||
if not len_v:
|
||||
print()
|
||||
best = min(v) # / number
|
||||
worst = max(v) # / number
|
||||
logger.info(k + ":")
|
||||
@ -1865,58 +1863,4 @@ if __name__ == "__main__":
|
||||
logger.info(FPSResult(loop_num, len_v, worst, best, v, 5))
|
||||
# print("")
|
||||
logger.info("")
|
||||
logger.info(f"{this_file_name}: ALL Finish {format_time(main_total_time)}")
|
||||
|
||||
# hsrac = HSRAC_cls()
|
||||
# hsrac.open_video(video_path)
|
||||
# hsf = HSF_cls()
|
||||
# while hsrac.read_frame():
|
||||
# hsf.current_image_gray = hsrac.current_image_gray.copy()
|
||||
# _ = hsrac.single_run()
|
||||
#
|
||||
# _ = hsf.single_run()
|
||||
|
||||
# w_video=True
|
||||
#
|
||||
# er_hsracs=External_Run_HSRACS()
|
||||
# er_hsracs.algo.open_video(video_path)
|
||||
# er_hsf=External_Run_HSF()
|
||||
#
|
||||
# if w_video:
|
||||
# filepath = 'test.mp4'
|
||||
# codec = cv2.VideoWriter_fourcc(*"x264")
|
||||
# video = cv2.VideoWriter(filepath, codec, 60.0, (200,150))#(60, 60)) # (150, 200))
|
||||
# while er_hsracs.algo.read_frame():
|
||||
# base_gray = er_hsracs.algo.current_image_gray.copy()
|
||||
# base_img=er_hsracs.algo.current_image.copy()
|
||||
# cv2.imshow("frame",base_gray)
|
||||
# hsf_x, hsf_y, hsf_cropbox,*_ = er_hsf.run(base_gray)
|
||||
#
|
||||
# # 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()
|
||||
# cv2.imshow("hsf_hsrac",base_img)
|
||||
# if cv2.waitKey(1) & 0xFF == ord("q"):
|
||||
# pass
|
||||
# if w_video:
|
||||
# video.write(ori_frame)
|
||||
# if w_video:
|
||||
# video.release()
|
||||
# # cv2.imwrite("b.png",er_hsracs.algo.result2)
|
||||
# er_hsracs.algo.cap.release()
|
||||
# cv2.destroyAllWindows()
|
||||
logger.info(f"{this_file_basename}: ALL Finish {format_time(main_total_time)}")
|
||||
Loading…
Reference in New Issue
Block a user