diff --git a/EyeTrackApp/Benchmark/bench_hsrac.py b/EyeTrackApp/Benchmark/bench_hsrac.py index 316a83d..9fe3fa0 100644 --- a/EyeTrackApp/Benchmark/bench_hsrac.py +++ b/EyeTrackApp/Benchmark/bench_hsrac.py @@ -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) @@ -1758,9 +1753,10 @@ class HSRAC_cls(object): # ) # cv2.imshow("crop", cropped_image) # cv2.imshow("frame", frame) - cv2.imshow("ori_frame", ori_frame) - if cv2.waitKey(1) & 0xFF == ord("q"): - pass + if imshow_enable: + cv2.imshow("ori_frame", ori_frame) + if cv2.waitKey(1) & 0xFF == ord("q"): + pass # except Exception as e: # print(e) @@ -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)), - 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))) + # 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() - cv2.imshow("hsf_hsrac", base_img) - video.write(cv2.resize(base_img, (200, 150))) + 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() - # _ = 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)}") \ No newline at end of file