clean up and improvement

This commit is contained in:
PallasNeko 2023-03-14 23:11:57 +09:00
parent 1c3f382f40
commit 50f489ec63

View File

@ -2,7 +2,7 @@ import math
import os
import timeit
from functools import lru_cache
from logging import Formatter, INFO, StreamHandler,FileHandler, getLogger
from logging import Formatter, INFO, StreamHandler, FileHandler, getLogger
import cv2
import numpy as np
@ -18,7 +18,6 @@ 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
@ -31,8 +30,6 @@ output_video_path = f'./{this_file_name}_{alg_ver}_new.mp4' if not old_mode else
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
skip_autoradius = False
@ -40,10 +37,6 @@ skip_blink_detect = False
##############################
##############################
# Do not change these.
@ -61,7 +54,6 @@ blink_init_frames = 60 * 3 # 60fps*3sec,Number of blink statistical frames
# step==(x,y)
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')
@ -77,9 +69,9 @@ if save_logfile and not imsave_flg:
else:
save_logfile = False
video_wr = cv2.VideoWriter(output_video_path, cv2.VideoWriter_fourcc(*"x264"), 60.0, (200, 150)) if save_video else None
##############################
@ -226,6 +218,7 @@ class BlinkDetector(object):
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.
@ -238,8 +231,9 @@ def ellipse_model(data, y, f):
"""
return data.dot(y) + f
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
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
@ -539,58 +533,46 @@ def conv_int_old(frame_int, kernel, xy_step, padding, xy_steps_list):
return frame_conv, min_response, center
@lru_cache(maxsize=lru_maxsize_vs)
def get_ransac_empty_array_lendata_new(len_data,iter_num, sample_num):
@lru_cache(maxsize=lru_maxsize_s)
def get_ransac_empty_array_new(iter_num, sample_num, len_data):
# Function to reduce array allocation by providing an empty array first and recycling it with lru
use_dtype=np.float64
use_dtype = np.float64
dm_rng = np.empty((iter_num, sample_num, 7), dtype=use_dtype)
dm_rng_swap = np.empty((iter_num, sample_num, 5), dtype=use_dtype)
dm_rng_swap_trans = dm_rng_swap.transpose((0, 2, 1))
# dm_rng_swap_trans = np.empty((iter_num, 5,sample_num), dtype=use_dtype)
dm_rng_5x5 = np.empty((iter_num, 5, 5), dtype=use_dtype)
dm_rng_p5smp = np.empty((iter_num, 5, sample_num), dtype=use_dtype)
dm_rng_p = np.empty((iter_num, 5), dtype=use_dtype)
dm_rng_p_npaxis = dm_rng_p[:, :, np.newaxis]
ellipse_y_arr = np.empty((iter_num, 5), dtype=use_dtype)
ellipse_y_arr[:, 2] = 1
swap_index = np.array([4, 3, 0, 1, 5], dtype=np.uint8)
dm_brod = np.broadcast_to(dm_rng_p[:, 4, np.newaxis], (iter_num, len_data))
dm_rng_six = dm_rng[:, :, 6, np.newaxis]
dm_rng_p_24 = dm_rng_p[:, 2:4]
dm_rng_p_10 = dm_rng_p[:, 1::-1]
el_y_arr_2 = ellipse_y_arr[:, :2]
el_y_arr_3 = ellipse_y_arr[:, 3:]
datamod = np.empty((len_data, 7), dtype=use_dtype) # np.empty((len(data), 7), dtype=ret_dtype)
datamod[:, 5] = 1
datamod_b=datamod[:, :5]#.T
random_index_init_arr = np.empty((iter_num, len_data), dtype=np.uint16)
random_index_init_arr[:, :] = np.arange(len_data, dtype=np.uint16)
random_index = np.empty((iter_num, len_data), dtype=np.uint16)
random_index_samplenum=random_index[:, :sample_num]
ellipse_data_arr=np.empty((iter_num,len_data),dtype=use_dtype)
th_abs=np.empty((iter_num,len_data),dtype=use_dtype)
dm_data_view=datamod[:, :2]# = data
dm_p2_view=datamod[:, 2:4]# = data * data
dm_mul_view=datamod[:, 4]# = data[:, 0] * data[:, 1]
dm_neg_view=datamod[:, 6]# = -datamod[:, 2]
# return datamod,random_index_init_arr,random_index,ellipse_data_arr,th_abs
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
datamod_b = datamod[:, :5] # .T
rdm_index_init_arr = np.empty((iter_num, len_data), dtype=np.uint16)
rdm_index_init_arr[:, :] = np.arange(len_data, dtype=np.uint16)
rdm_index = np.empty((iter_num, len_data), dtype=np.uint16)
rdm_index_smpnum = rdm_index[:, :sample_num]
ellipse_data_arr = np.empty((iter_num, len_data), dtype=use_dtype)
th_abs = np.empty((iter_num, len_data), dtype=use_dtype)
dm_data = datamod[:, :2] # = data
dm_p2 = datamod[:, 2:4] # = data * data
dm_mul = datamod[:, 4] # = data[:, 0] * data[:, 1]
dm_neg = datamod[:, 6] # = -datamod[:, 2]
return dm_rng, dm_rng_swap, dm_rng_swap_trans, dm_rng_5x5, dm_rng_p5smp, dm_rng_p, dm_rng_p_npaxis, ellipse_y_arr, swap_index, dm_brod, dm_rng_six, dm_rng_p_24, dm_rng_p_10, el_y_arr_2, el_y_arr_3, datamod, datamod_b, dm_data, dm_p2, dm_mul, dm_neg, rdm_index_init_arr, rdm_index, rdm_index_smpnum, ellipse_data_arr, th_abs
@lru_cache(maxsize=lru_maxsize_s)
def get_ransac_empty_array_iternum_samplenum_new(iter_num, sample_num,len_data):
# Function to reduce array allocation by providing an empty array first and recycling it with lru
use_dtype=np.float64
datamod_rng=np.empty((iter_num,sample_num,7),dtype=use_dtype)
datamod_rng_swap = np.empty((iter_num, sample_num, 5), dtype=use_dtype)
datamod_rng_swap_trans=datamod_rng_swap.transpose((0,2,1))
# datamod_rng_swap_trans = np.empty((iter_num, 5,sample_num), dtype=use_dtype)
datamod_rng_5x5= np.empty((iter_num, 5,5), dtype=use_dtype)
datamod_rng_p5smp = np.empty((iter_num, 5,sample_num), dtype=use_dtype)
datamod_rng_p=np.empty((iter_num,5),dtype=use_dtype)
datamod_rng_p_npaxis=datamod_rng_p[:,:,np.newaxis]
ellipse_y_arr=np.empty((iter_num,5),dtype=use_dtype)
ellipse_y_arr[:, 2] = 1
swap_index=np.array([4, 3, 0, 1, 5])
dm_brod=np.broadcast_to(datamod_rng_p[:, 4, np.newaxis], (iter_num, len_data))
dm_rng_six=datamod_rng[:, :, 6, np.newaxis]
dm_rng_p_24_view= datamod_rng_p[:, 2:4]
dm_rng_p_10_view= datamod_rng_p[:, 1::-1]
el_y_arr_2_view=ellipse_y_arr[:, :2]
el_y_arr_3_view=ellipse_y_arr[:, 3:]
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
# @profile
def fit_rotated_ellipse_ransac_new(data: np.ndarray, sfc: np.random.Generator, iter_num=100, sample_num=10, offset=80 # 80.0, 10, 80
): # before changing these values, please read up on the ransac algorithm
): # 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
# The array contents do not change during the loop, so only one call is needed.
@ -601,13 +583,8 @@ def fit_rotated_ellipse_ransac_new(data: np.ndarray, sfc: np.random.Generator, i
if len_data < sample_num:
return None
# Type of calculation result
# ret_dtype = np.float64
# todo:create view
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_new(iter_num,sample_num,len_data)
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_new(len_data,iter_num,sample_num)
dm_rng, dm_rng_swap, dm_rng_swap_trans, dm_rng_5x5, dm_rng_p5smp, dm_rng_p, dm_rng_p_npaxis, ellipse_y_arr, swap_index, dm_brod, dm_rng_six, dm_rng_p_24, dm_rng_p_10, el_y_arr_2, el_y_arr_3, datamod, datamod_b, dm_data, dm_p2, dm_mul, dm_neg, rdm_index_init_arr, rdm_index, rdm_index_smpnum, ellipse_data_arr, th_abs = get_ransac_empty_array_new(
iter_num, sample_num, len_data)
# I don't think it looks beautiful.
# x,y,x**2,y**2,x*y,1,-1*x**2
@ -616,43 +593,43 @@ def fit_rotated_ellipse_ransac_new(data: np.ndarray, sfc: np.random.Generator, i
# (-1 * data[:, 0] ** 2)[:, np.newaxis]], axis=1,
# dtype=ret_dtype)
dm_data_view[:, :] = data#[:]
dm_p2_view[:,:] = data * data
dm_mul_view[:] = data[:, 0] * data[:, 1]
dm_neg_view[:] = -dm_p2_view[:,0] # -1 * data[:, 0] ** 2#
dm_data[:, :] = data # [:]
dm_p2[:, :] = data * data
dm_mul[:] = data[:, 0] * data[:, 1]
dm_neg[:] = -dm_p2[:, 0] # -1 * data[:, 0] ** 2#
# Sorts a random number array of size (iter,len_data). After sorting, returns the index of sample_num random numbers before sorting.
sfc.permuted(random_index_init_arr, axis=1, out=random_index)
sfc.permuted(rdm_index_init_arr, axis=1, out=rdm_index)
# np.take replaces a[ind,:] and is 3-4 times faster, https://gist.github.com/rossant/4645217
# a.take() is faster than np.take(a)
datamod.take(random_index_samplenum, axis=0, mode="clip", out=datamod_rng)
datamod.take(rdm_index_smpnum, axis=0, mode="clip", out=dm_rng)
datamod_rng.take(swap_index, axis=2, mode="clip", out=datamod_rng_swap)
dm_rng.take(swap_index, axis=2, mode="clip", out=dm_rng_swap)
# or
# datamod_rng_swap = np.take(datamod_rng,[4, 3, 0, 1, 5],axis=2)
# dm_rng_swap = np.take(dm_rng,[4, 3, 0, 1, 5],axis=2)
np.matmul(datamod_rng_swap_trans, datamod_rng_swap, out=datamod_rng_5x5)
# I want to use cv2.mulTransposed, but for some reason the results are different and it can only use 1-channel arrays.
# np.linalg.solve(np.matmul(datamod_rng_swap_trans, datamod_rng_swap), datamod_rng_swap_trans) # solve is slow https://github.com/bogovicj/JaneliaMLCourse/issues/1
_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)
np.matmul(dm_rng_swap_trans, dm_rng_swap, out=dm_rng_5x5)
# np.linalg.solve(np.matmul(dm_rng_swap_trans, dm_rng_swap), dm_rng_swap_trans) # solve is slow https://github.com/bogovicj/JaneliaMLCourse/issues/1
_umath_linalg.inv(dm_rng_5x5, signature='d->d',
extobj=np.linalg.linalg.get_linalg_error_extobj(np.linalg.linalg._raise_linalgerror_singular), out=dm_rng_5x5)
np.matmul(dm_rng_5x5, dm_rng_swap_trans, out=dm_rng_p5smp)
np.matmul(datamod_rng_p5smp, dm_rng_six, out=datamod_rng_p_npaxis)
np.matmul(dm_rng_p5smp, dm_rng_six, out=dm_rng_p_npaxis)
el_y_arr_2_view[:,:] = dm_rng_p_24_view
el_y_arr_3_view[:,:] = dm_rng_p_10_view
el_y_arr_2[:, :] = dm_rng_p_24
el_y_arr_3[:, :] = dm_rng_p_10
cv2.gemm(ellipse_y_arr,datamod_b,1.0,dm_brod,1.0,dst=ellipse_data_arr,flags=cv2.GEMM_2_T)
np.abs(ellipse_data_arr,out=th_abs)
cv2.threshold(th_abs, offset, 1.0, cv2.THRESH_BINARY_INV,dst=th_abs)#[1]
cv2.gemm(ellipse_y_arr, datamod_b, 1.0, dm_brod, 1.0, dst=ellipse_data_arr, flags=cv2.GEMM_2_T)
np.abs(ellipse_data_arr, out=th_abs)
cv2.threshold(th_abs, offset, 1.0, cv2.THRESH_BINARY_INV, dst=th_abs) # [1]
ellipse_data_index = \
cv2.minMaxLoc(cv2.reduce(th_abs, 1, cv2.REDUCE_SUM))[3][1]
cv2.minMaxLoc(cv2.reduce(th_abs, 1, cv2.REDUCE_SUM))[3][1]
# error_num = ellipse_data_arr[ellipse_data_index].sum()
error_num = cv2.sumElems(ellipse_data_arr[ellipse_data_index])[0]
effective_sample_p_arr = datamod_rng_p[ellipse_data_index].tolist()
effective_sample_p_arr = dm_rng_p[ellipse_data_index].tolist()
# if fit_rotated_ellipse(effective_data_arr.sum(), effective_sample_p_arr)!= fit_rotated_ellipse_base(effective_data_arr, effective_sample_p_arr):
# print()
@ -746,8 +723,8 @@ class HaarSurroundFeature_new:
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.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
@ -763,163 +740,71 @@ class HaarSurroundFeature_new:
return kernel
@lru_cache(maxsize=lru_maxsize_vvs)
def get_hsf_empty_array_new(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_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)
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)
len_syx = (len_sy, len_sx)
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_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,:]
in_p00 = frame_int[y_rin_m, x_rin_m]
in_p11 = frame_int[y_rin_p, x_rin_p]
in_p01 = frame_int[y_rin_m, x_rin_p]
in_p10 = frame_int[y_rin_p, x_rin_m]
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
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,:]
inner_sum = np.empty(len_syx, dtype=frame_int_dtype)
outer_sum = np.empty(len_syx, dtype=frame_int_dtype)
out_p_temp = np.empty((len_sy, col + 1), dtype=frame_int_dtype)
out_p00 = np.empty(len_syx, dtype=frame_int_dtype)
out_p11 = np.empty(len_syx, dtype=frame_int_dtype)
out_p01 = np.empty(len_syx, dtype=frame_int_dtype)
out_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=(row - 2 * pad, col - 2 * pad), dtype=np.uint8) # or np.float64
frame_conv_stride = frame_conv[::y_step, ::x_step]
return frame_pad, frame_int, inner_sum, in_p00, in_p11, in_p01, in_p10, y_ro_m, x_ro_m, y_ro_p, x_ro_p, outer_sum, out_p_temp, out_p00, out_p11, out_p01, out_p10, response_list, frame_conv, frame_conv_stride, 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:
"""
inner_sum, outer_sum,p_temp, p00, p11, p01, p10, response_list, frame_conv, frame_conv_stride = get_hsf_empty_array_new(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_p00_view + in_p11_view - in_p01_view - in_p10_view
def conv_int_new(frame_int, kernel, inner_sum, in_p00, in_p11, in_p01, in_p10, y_ro_m, x_ro_m, y_ro_p, x_ro_p, outer_sum, out_p_temp,
out_p00, out_p11, out_p01, out_p10, response_list, frame_conv_stride):
inner_sum[:, :] = in_p00 + in_p11 - in_p01 - in_p10
# p00 calc
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)
frame_int.take(y_ro_m, axis=0, mode="clip", out=out_p_temp)
out_p_temp.take(x_ro_m, axis=1, mode="clip", out=out_p00)
# p01 calc
p_temp.take( x_ro_p, axis=1, mode="clip", out=p01)
out_p_temp.take(x_ro_p, axis=1, mode="clip", out=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)
frame_int.take(y_ro_p, axis=0, mode="clip", out=out_p_temp)
out_p_temp.take(x_ro_p, axis=1, mode="clip", out=out_p11)
# p10 calc
p_temp.take( x_ro_m, axis=1, mode="clip", out=p10)
out_p_temp.take(x_ro_m, axis=1, mode="clip", out=out_p10)
outer_sum[:, :] = p00 + p11 - p01 - p10 - inner_sum
outer_sum[:, :] = out_p00 + out_p11 - out_p01 - out_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/
@ -927,51 +812,47 @@ def conv_int(frame_int, kernel, x_step,y_step, padding,in_p00_view, in_p11_view,
# response_list += kernel.val_out * outer_sum
cv2.addWeighted(inner_sum,
kernel.val_in,
outer_sum,# or p00 + p11 - p01 - p10 - inner_sum
outer_sum, # or p00 + p11 - p01 - p10 - inner_sum
kernel.val_out,
0.0,
dtype=cv2.CV_64F,#or cv2.CV_32S
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 = get_hsf_center(padding,x_step,y_step,min_loc)
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)
return min_response, min_loc
@lru_cache(maxsize=lru_maxsize_s)
def get_hsf_center(padding, x_step, y_step, min_loc): # min_x,min_y):
return padding + (x_step * min_loc[0]) - padding, padding + (y_step * min_loc[1]) - padding
@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)
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):
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
ransac_upper_x = center_x + max(20, radius)
ransac_lower_x = center_x - max(20, radius)
ransac_upper_y = center_y + max(20, radius)
ransac_lower_y = center_y - max(20, radius)
ransac_xy_offset = (ransac_lower_x, ransac_lower_y)
return center_x, center_y, upper_x, lower_x, upper_y, lower_y, ransac_lower_x, ransac_lower_y, ransac_upper_x, ransac_upper_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.
@ -1034,7 +915,7 @@ class HSRAC_cls(object):
## default_radius = 14
if imshow_enable or save_video:
ori_frame = self.current_image.copy()# debug code
ori_frame = self.current_image.copy() # debug code
# cropbox=[] # debug code
blink_bd = False
@ -1068,9 +949,10 @@ class HSRAC_cls(object):
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)
frame_pad, frame_int, inner_sum, in_p00, in_p11, in_p01, in_p10, y_ro_m, x_ro_m, y_ro_p, x_ro_p, outer_sum, out_p_temp, out_p00, out_p11, out_p01, out_p10, response_list, frame_conv, frame_conv_stride, 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
@ -1079,10 +961,12 @@ class HSRAC_cls(object):
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)
response, hsf_min_loc = conv_int_new(frame_int, hsf, inner_sum, in_p00, in_p11, in_p01, in_p10, y_ro_m, x_ro_m, y_ro_p, x_ro_p,
outer_sum, out_p_temp, out_p00, out_p11, out_p01, out_p10, response_list,
frame_conv_stride)
center_xy = get_hsf_center(pad, step[0], step[1], hsf_min_loc)
self.timedict["conv_int"].append(timeit.default_timer() - conv_int_start_time)
crop_start_time = timeit.default_timer()
@ -1094,49 +978,76 @@ class HSRAC_cls(object):
upper_y = center_y + radius
lower_y = center_y - radius
else:
center_x, center_y, upper_x, lower_x, upper_y, lower_y=get_center_noclamp(center_xy,radius)
center_x, center_y, upper_x, lower_x, upper_y, lower_y, ransac_lower_x, ransac_lower_y, ransac_upper_x, ransac_upper_y, ransac_xy_offset = 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
]
)
if old_mode:
# Crop the image using the calculated bounds
cropped_image = safe_crop(gray_frame, lower_x, lower_y, upper_x, upper_y)
else:
# 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])
self.center_q1.add_response(
cv2.mean(safe_crop(gray_frame, center_x - max(20, radius), center_y - max(20, radius), center_x + max(20, radius),
center_y + max(20, radius), keepsize=False))[
0
]
)
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:
self.blink_detector.calc_thresh()
self.center_q1.calc_thresh()
self.now_modeo = self.cv_modeo[3]
else:
orig_x, orig_y = center_x, center_y
if self.blink_detector.enable_detect_flg:
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
else:
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(safe_crop(gray_frame, lower_x, lower_y, upper_x, upper_y, 1))[0])
self.center_q1.add_response(
cv2.mean(safe_crop(gray_frame, center_x - max(20, radius), center_y - max(20, radius), center_x + max(20, radius),
center_y + max(20, radius), keepsize=False))[
0
]
)
else:
self.blink_detector.calc_thresh()
self.center_q1.calc_thresh()
self.now_modeo = self.cv_modeo[3]
else:
if self.blink_detector.enable_detect_flg and self.blink_detector.detect(
cv2.mean(safe_crop(gray_frame, lower_x, lower_y, upper_x, upper_y, 1))[0]):
# 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
# 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)
@ -1175,8 +1086,7 @@ class HSRAC_cls(object):
# For measuring processing time of image processing
ransac_start_time = timeit.default_timer()
if old_mode:
frame_gray = cv2.GaussianBlur(frame, (5, 5), 0)
else:
@ -1190,24 +1100,21 @@ class HSRAC_cls(object):
# or
frame_gray = cv2.sepFilter2D(frame, -1, self.gauss_k, self.gauss_k)
# Crop the image using the calculated bounds
# todo:safecrop tune
if old_mode:
hsf_center_x, hsf_center_y = center_x, center_y#center_x.copy(), center_y.copy()
hsf_center_x, hsf_center_y = center_x, 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)
frame_gray_crop = safe_crop(frame_gray, lower_x, lower_y, upper_x, upper_y)
else:
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)
if not old_mode:
th_frame,fic_frame=get_ransac_frame(frame_gray_crop.shape)
frame = frame_gray_crop # todo: It can cause bugs.
frame_gray_crop = safe_crop(frame_gray, ransac_lower_x, ransac_lower_y, ransac_upper_x, ransac_upper_y, 1)
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)
@ -1218,20 +1125,19 @@ class HSRAC_cls(object):
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:
if old_mode:
try:
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
except:
th_frame = 255 - frame_gray_crop
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)
if old_mode:
contours, _ = cv2.findContours(th_frame, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
else:
@ -1246,30 +1152,26 @@ class HSRAC_cls(object):
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)
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:
cv2.threshold(frame_gray_crop, threshold_value, 255, cv2.THRESH_BINARY, dst=th_frame)
if old_mode:
try:
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:
except:
th_frame = 255 - frame_gray_crop
contours2, _ = cv2.findContours(th_frame, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
contours = (*contours, *contours2)
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)
contours = (*contours, *cv2.findContours(fic_frame, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[0])
# or
# contours = (*contours, *cv2.findContours(fic_frame, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)[0])
@ -1352,7 +1254,6 @@ class HSRAC_cls(object):
if cv2.waitKey(1) & 0xFF == ord("q"):
pass
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)
@ -1379,9 +1280,10 @@ if __name__ == "__main__":
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.get(cv2.CAP_PROP_FRAME_COUNT) / cap.get(
cv2.CAP_PROP_FPS)))
cap.release()
if not print_enable:
def print(*args, **kwargs):
pass
@ -1423,7 +1325,7 @@ if __name__ == "__main__":
pass
else:
_ = hsrac.single_run()
if save_video:
video_wr.release()
hsrac.cap.release()
@ -1445,4 +1347,4 @@ if __name__ == "__main__":
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)}")
logger.info(f"{this_file_basename}: ALL Finish {format_time(main_total_time)}")