import functools import math import sys import timeit from functools import lru_cache import cv2 import numpy as np # from line_profiler_pycharm import profile video_path = "ezgif.com-gif-maker.avi" imshow_enable = True calc_print_enable = True save_video = False skip_autoradius = False skip_blink_detect = False # cache param lru_maxsize_vvs = 16 lru_maxsize_vs = 64 # CV param default_radius = 20 auto_radius_range = (default_radius - 10, default_radius + 10) # (10,30) auto_radius_step = 1 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 """ Attention. If using cv2.filter2D in this code, be careful with the kernel https://stackoverflow.com/questions/39457468/convolution-without-any-padding-opencv-python """ def TimeitWrapper(*args, **kwargs): """ This decorator @TimeitWrapper() prints the function name and execution time in seconds. :param args: :param kwargs: :return: """ def decorator(function): @functools.wraps(function) def wrapper(*args, **kwargs): start = timeit.default_timer() results = function(*args, **kwargs) end = timeit.default_timer() print('{} execution time: {:.10f} s'.format(function.__name__, end - start)) return results return wrapper return decorator class TimeitResult(object): """ from https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L55 Object returned by the timeit magic with info about the run. Contains the following attributes : loops: (int) number of loops done per measurement repeat: (int) number of times the measurement has been repeated best: (float) best execution time / number all_runs: (list of float) execution time of each run (in s) """ def __init__(self, loops, repeat, best, worst, all_runs, precision): self.loops = loops self.repeat = repeat self.best = best self.worst = worst self.all_runs = all_runs self._precision = precision self.timings = [dt / self.loops for dt in all_runs] @property def average(self): return math.fsum(self.timings) / len(self.timings) @property def stdev(self): mean = self.average return (math.fsum([(x - mean) ** 2 for x in self.timings]) / len(self.timings)) ** 0.5 def __str__(self): pm = '+-' if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding: try: u'\xb1'.encode(sys.stdout.encoding) pm = u'\xb1' except: pass return "min:{best} max:{worst} mean:{mean} {pm} {std} per loop (mean {pm} std. dev. of {runs} run{run_plural}, {loops:,} loop{loop_plural} each)".format( pm=pm, runs=self.repeat, loops=self.loops, loop_plural="" if self.loops == 1 else "s", run_plural="" if self.repeat == 1 else "s", mean=format_time(self.average, self._precision), std=format_time(self.stdev, self._precision), best=format_time(self.best, self._precision), worst=format_time(self.worst, self._precision), ) def _repr_pretty_(self, p, cycle): unic = self.__str__() p.text(u'') class FPSResult(object): """ base https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L55 """ def __init__(self, loops, repeat, best, worst, all_runs, precision): self.loops = loops self.repeat = repeat self.best = 1 / best self.worst = 1 / worst self.all_runs = all_runs self._precision = precision self.fps = [1 / dt for dt in all_runs] self.unit = "fps" @property def average(self): return math.fsum(self.fps) / len(self.fps) @property def stdev(self): mean = self.average return (math.fsum([(x - mean) ** 2 for x in self.fps]) / len(self.fps)) ** 0.5 def __str__(self): pm = '+-' if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding: try: u'\xb1'.encode(sys.stdout.encoding) pm = u'\xb1' except: pass return "min:{best} max:{worst} mean:{mean} {pm} {std} per loop (mean {pm} std. dev. of {runs} run{run_plural}, {loops:,} loop{loop_plural} each)".format( pm=pm, runs=self.repeat, loops=self.loops, loop_plural="" if self.loops == 1 else "s", run_plural="" if self.repeat == 1 else "s", mean="%.*g%s" % (self._precision, self.average, self.unit), std="%.*g%s" % (self._precision, self.stdev, self.unit), best="%.*g%s" % (self._precision, self.best, self.unit), worst="%.*g%s" % (self._precision, self.worst, self.unit), ) def _repr_pretty_(self, p, cycle): unic = self.__str__() p.text(u'') def format_time(timespan, precision=3): """ https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L1473 Formats the timespan in a human readable form """ if timespan >= 60.0: # we have more than a minute, format that in a human readable form # Idea from http://snipplr.com/view/5713/ parts = [("d", 60 * 60 * 24), ("h", 60 * 60), ("min", 60), ("s", 1)] time = [] leftover = timespan for suffix, length in parts: value = int(leftover / length) if value > 0: leftover = leftover % length time.append(u'%s%s' % (str(value), suffix)) if leftover < 1: break return " ".join(time) # Unfortunately the unicode 'micro' symbol can cause problems in # certain terminals. # See bug: https://bugs.launchpad.net/ipython/+bug/348466 # Try to prevent crashes by being more secure than it needs to # E.g. eclipse is able to print a µ, but has no sys.stdout.encoding set. units = [u"s", u"ms", u'us', "ns"] # the save value if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding: try: u'\xb5'.encode(sys.stdout.encoding) units = [u"s", u"ms", u'\xb5s', "ns"] except: pass scaling = [1, 1e3, 1e6, 1e9] if timespan > 0.0: order = min(-int(math.floor(math.log10(timespan)) // 3), 3) else: order = 3 return u"%.*g %s" % (precision, timespan * scaling[order], units[order]) class CvParameters: # It may be a little slower because a dict named "self" is read for each function call. def __init__(self, radius, step): # self.prev_radius=radius self._radius = radius self.pad = 2 * radius # self.prev_step=step self._step = step self._hsf = HaarSurroundFeature(radius) def get_rpsh(self): return self._radius, self.pad, self._step, self._hsf # Essentially, the following would be preferable, but it would take twice as long to call. # return self.radius, self.pad, self.step, self.hsf @property def radius(self): return self._radius @radius.setter def radius(self, now_radius): # self.prev_radius=self._radius self._radius = now_radius self.pad = 2 * now_radius self.hsf = now_radius @property def step(self): return self._step @step.setter def step(self, now_step): # self.prev_step=self.step self._step = now_step @property def hsf(self): return self._hsf @hsf.setter def hsf(self, now_radius): self._hsf = HaarSurroundFeature(now_radius) class HaarSurroundFeature: def __init__(self, r_inner, r_outer=None, val=None): if r_outer is None: r_outer = r_inner * 3 # print(r_outer) r_inner2 = r_inner * r_inner count_inner = r_inner2 count_outer = r_outer * r_outer - r_inner2 if val is None: val_inner = 1.0 / r_inner2 val_outer = -val_inner * count_inner / count_outer else: val_inner = val[0] val_outer = val[1] self.val_in = np.array(val_inner, dtype=np.float64) self.val_out = np.array(val_outer, dtype=np.float64) self.r_in = r_inner self.r_out = r_outer def get_kernel(self): # Defined here, but not yet used? # Create a kernel filled with the value of self.val_out kernel = np.ones(shape=(2 * self.r_out - 1, 2 * self.r_out - 1), dtype=np.float64) * self.val_out # Set the values of the inner area of the kernel using array slicing start = (self.r_out - self.r_in) end = (self.r_out + self.r_in - 1) kernel[start:end, start:end] = self.val_in return kernel def to_gray(frame): # Faster by quitting checking if the input image is already grayscale # Perhaps it would be faster with less overhead to call cv2.cvtColor directly instead of using this function return cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) @lru_cache(maxsize=lru_maxsize_vs) def frameint_get_xy_step(imageshape, xysteps, pad, start_offset=None, end_offset=None): """ :param imageshape: (height(row),width(col)). row==y,cal==x :param xysteps: (x,y) :param pad: int :param start_offset: (x,y) or None :param end_offset: (x,y) or None :return: xy_np:tuple(x,y) """ row, col = imageshape row -= 1 col -= 1 x_step, y_step = xysteps # This is not beautiful. start_pad_x = start_pad_y = end_pad_x = end_pad_y = pad if start_offset is not None: start_pad_x += start_offset[0] start_pad_y += start_offset[1] if end_offset is not None: end_pad_x += end_offset[0] end_pad_y += end_offset[1] y_np = np.arange(start_pad_y, row - end_pad_y, y_step) x_np = np.arange(start_pad_x, col - end_pad_x, x_step) xy_np = (x_np, y_np) return xy_np @lru_cache(maxsize=lru_maxsize_vvs) 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 inner_sum = np.empty(len_syx, dtype=frame_int_dtype) outer_sum = np.empty(len_syx, dtype=frame_int_dtype) p_temp = np.empty((len_syx[0], frameint_x), dtype=frame_int_dtype) p00 = np.empty(len_syx, dtype=frame_int_dtype) p11 = np.empty(len_syx, dtype=frame_int_dtype) p01 = np.empty(len_syx, dtype=frame_int_dtype) p10 = np.empty(len_syx, dtype=frame_int_dtype) response_list = np.empty(len_syx, dtype=np.float64) frame_conv = np.zeros(shape=fcshape[0], dtype=np.uint8) frame_conv_stride = frame_conv[::fcshape[1], ::fcshape[2]] return (inner_sum, outer_sum), p_temp, (p00, p11, p01, p10), response_list, (frame_conv, frame_conv_stride) # @profile def conv_int(frame_int, kernel, xy_step, padding, xy_steps_list): """ :param frame_int: :param kernel: hsf :param step: (x,y) :param padding: int :return: """ row, col = frame_int.shape row -= 1 col -= 1 x_step, y_step = xy_step # padding2 = 2 * padding f_shape = row - 2 * padding, col - 2 * padding r_in = kernel.r_in len_sx, len_sy = len(xy_steps_list[0]), len(xy_steps_list[1]) inout_sum, p_temp, p_list, response_list, frameconvlist = get_hsf_empty_array((len_sy, len_sx), col + 1, frame_int.dtype, (f_shape, y_step, x_step)) inner_sum, outer_sum = inout_sum p00, p11, p01, p10 = p_list frame_conv, frame_conv_stride = frameconvlist y_rin_m = xy_steps_list[1] - r_in x_rin_m = xy_steps_list[0] - r_in y_rin_p = xy_steps_list[1] + r_in x_rin_p = xy_steps_list[0] + r_in # xx==(y,x),m==MINUS,p==PLUS, ex: mm==(y-,x-) inarr_mm = frame_int[y_rin_m[0]:y_rin_m[-1] + 1:y_step, x_rin_m[0]:x_rin_m[-1] + 1:x_step] inarr_mp = frame_int[y_rin_m[0]:y_rin_m[-1] + 1:y_step, x_rin_p[0]:x_rin_p[-1] + 1:x_step] inarr_pm = frame_int[y_rin_p[0]:y_rin_p[-1] + 1:y_step, x_rin_m[0]:x_rin_m[-1] + 1:x_step] inarr_pp = frame_int[y_rin_p[0]:y_rin_p[-1] + 1:y_step, x_rin_p[0]:x_rin_p[-1] + 1:x_step] # == inarr_mm + inarr_pp - inarr_mp - inarr_pm inner_sum[:, :] = inarr_mm inner_sum += inarr_pp inner_sum -= inarr_mp inner_sum -= inarr_pm # Bottleneck here, I want to make it smarter. Someone do it. # (y,x) # p00=max(y_ro_m,0),max(x_ro_m,0) # p11=min(y_ro_p,ylim),min(x_ro_p,xlim) # p01=max(y_ro_m,0),min(x_ro_p,xlim) # p10=min(y_ro_p,ylim),max(x_ro_m,0) y_ro_m = xy_steps_list[1] - kernel.r_out x_ro_m = xy_steps_list[0] - kernel.r_out y_ro_p = xy_steps_list[1] + kernel.r_out x_ro_p = xy_steps_list[0] + kernel.r_out # p00 calc np.take(frame_int, y_ro_m, axis=0, mode="clip", out=p_temp) np.take(p_temp, x_ro_m, axis=1, mode="clip", out=p00) # p01 calc np.take(p_temp, x_ro_p, axis=1, mode="clip", out=p01) # p11 calc np.take(frame_int, y_ro_p, axis=0, mode="clip", out=p_temp) np.take(p_temp, x_ro_p, axis=1, mode="clip", out=p11) # p10 calc np.take(p_temp, x_ro_m, axis=1, mode="clip", out=p10) # the point is this # p00=np.take(np.take(frame_int, y_ro_m, axis=0, mode="clip"), x_ro_m, axis=1, mode="clip") # p11=np.take(np.take(frame_int, y_ro_p, axis=0, mode="clip"), x_ro_p, axis=1, mode="clip") # p01=np.take(np.take(frame_int, y_ro_m, axis=0, mode="clip"), x_ro_p, axis=1, mode="clip") # p10=np.take(np.take(frame_int, y_ro_p, axis=0, mode="clip"), x_ro_m, axis=1, mode="clip") outer_sum[:, :] = p00 + p11 - p01 - p10 - inner_sum np.multiply(kernel.val_in, inner_sum, dtype=np.float64, out=response_list) response_list += kernel.val_out * outer_sum # min_response, max_val, min_loc, max_loc = cv2.minMaxLoc(response_list) min_response, _, min_loc, _ = cv2.minMaxLoc(response_list) center = ((xy_steps_list[0][min_loc[0]] - padding), (xy_steps_list[1][min_loc[1]] - padding)) frame_conv_stride[:, :] = response_list # or # frame_conv_stride[:, :] = response_list.astype(np.uint8) return frame_conv, min_response, center class Auto_Radius_Calc(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 Blink_Detector(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) class CenterCorrection(object): def __init__(self): # Tunable parameters kernel_size = 7 # 3 or 5 or 7 self.hist_thr = float(4) # 4% self.center_q1_radius = 20 self.setup_comp = False self.quartile_1 = None self.radius = None self.frame_shape = None self.frame_mask = None self.frame_bin = None self.frame_final = None self.morph_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_size, kernel_size)) self.morph_kernel2 = np.ones((3, 3)) self.hist_index = np.arange(256) self.hist = np.empty((256, 1)) self.hist_norm = np.empty((256, 1)) def init_array(self, gray_shape, quartile_1, radius): self.frame_shape = gray_shape self.frame_mask = np.empty(gray_shape, dtype=np.uint8) self.frame_bin = np.empty(gray_shape, dtype=np.uint8) self.frame_final = np.empty(gray_shape, dtype=np.uint8) self.quartile_1 = quartile_1 self.radius = radius self.setup_comp = True # def reset_array(self): # self.frame_mask.fill(0) def correction(self, gray_frame, orig_x, orig_y): center_x, center_y = orig_x, orig_y self.frame_mask.fill(0) # cv2.circle(self.frame_mask, center=(center_x, center_y), radius=int(self.radius * 2), color=255, thickness=-1) # bottleneck cv2.calcHist([gray_frame], [0], None, [256], [0, 256], hist=self.hist) cv2.normalize(self.hist, self.hist_norm, alpha=100.0, norm_type=cv2.NORM_L1) hist_per = self.hist_norm.cumsum() hist_index_list = self.hist_index[hist_per >= self.hist_thr] frame_thr = hist_index_list[0] if len(hist_index_list) else np.percentile(cv2.bitwise_or(255 - self.frame_mask, gray_frame), 4) # bottleneck self.frame_bin = cv2.threshold(gray_frame, frame_thr, 1, cv2.THRESH_BINARY_INV)[1] cropped_x, cropped_y, cropped_w, cropped_h = cv2.boundingRect(self.frame_bin) self.frame_final = cv2.bitwise_and(self.frame_bin, self.frame_mask) # bottleneck self.frame_final = cv2.morphologyEx(self.frame_final, cv2.MORPH_CLOSE, self.morph_kernel) self.frame_final = cv2.morphologyEx(self.frame_final, cv2.MORPH_OPEN, self.morph_kernel) if (cropped_h, cropped_w) == self.frame_shape: # Not detected. base_x, base_y = center_x, center_y else: base_x = cropped_x + cropped_w // 2 base_y = cropped_y + cropped_h // 2 if self.frame_final[base_y, base_x] != 1: if self.frame_final[center_y, center_x] != 1: self.frame_final = cv2.morphologyEx(self.frame_final, cv2.MORPH_DILATE, self.morph_kernel2, iterations=3) else: base_x, base_y = center_x, center_y contours, _ = cv2.findContours(self.frame_final, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) contours_box = [cv2.boundingRect(cnt) for cnt in contours] contours_dist = np.array( [abs(base_x - (cnt_x + cnt_w / 2)) + abs(base_y - (cnt_y + cnt_h / 2)) for cnt_x, cnt_y, cnt_w, cnt_h in contours_box]) if len(contours_box): cropped_x2, cropped_y2, cropped_w2, cropped_h2 = contours_box[contours_dist.argmin()] x = cropped_x2 + cropped_w2 // 2 y = cropped_y2 + cropped_h2 // 2 else: x = center_x y = center_y # if imshow_enable: # cv2.circle(frame, (orig_x, orig_y), 10, (255, 0, 0), -1) # cv2.circle(frame, (x, y), 7, (0, 0, 255), -1) # # out_x = center_x if abs(x - center_x) > radius else x # out_y = center_y if abs(y - center_y) > radius else y out_x, out_y = orig_x, orig_y if gray_frame[int(max(y - 5, 0)):int(min(y + 5, self.frame_shape[0])), int(max(x - 5, 0)):int(min(x + 5, self.frame_shape[1]))].min() < self.quartile_1: out_x = x out_y = y # if imshow_enable: # cv2.circle(frame, (out_x, out_y), 5, (0, 255, 0), -1) # # cv2.imshow("frame_bin", self.frame_bin * 255) # cv2.imshow("frame_final", self.frame_final * 255) return out_x, out_y #RANSAC video_path = "demo2.mp4" imshow_enable = False save_video = False thresh_add = 10 class TimeitResult(object): """ from https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L55 Object returned by the timeit magic with info about the run. Contains the following attributes : loops: (int) number of loops done per measurement repeat: (int) number of times the measurement has been repeated best: (float) best execution time / number all_runs: (list of float) execution time of each run (in s) """ def __init__(self, loops, repeat, best, worst, all_runs, precision): self.loops = loops self.repeat = repeat self.best = best self.worst = worst self.all_runs = all_runs self._precision = precision self.timings = [dt / self.loops for dt in all_runs] @property def average(self): return math.fsum(self.timings) / len(self.timings) @property def stdev(self): mean = self.average return (math.fsum([(x - mean) ** 2 for x in self.timings]) / len(self.timings)) ** 0.5 def __str__(self): pm = '+-' if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding: try: u'\xb1'.encode(sys.stdout.encoding) pm = u'\xb1' except: pass return "min:{best} max:{worst} mean:{mean} {pm} {std} per loop (mean {pm} std. dev. of {runs} run{run_plural}, {loops:,} loop{loop_plural} each)".format( pm=pm, runs=self.repeat, loops=self.loops, loop_plural="" if self.loops == 1 else "s", run_plural="" if self.repeat == 1 else "s", mean=format_time(self.average, self._precision), std=format_time(self.stdev, self._precision), best=format_time(self.best, self._precision), worst=format_time(self.worst, self._precision), ) def _repr_pretty_(self, p, cycle): unic = self.__str__() p.text(u'') class FPSResult(object): """ base https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L55 """ def __init__(self, loops, repeat, best, worst, all_runs, precision): self.loops = loops self.repeat = repeat self.best = 1 / best self.worst = 1 / worst self.all_runs = all_runs self._precision = precision self.fps = [1 / dt for dt in all_runs] self.unit = "fps" @property def average(self): return math.fsum(self.fps) / len(self.fps) @property def stdev(self): mean = self.average return (math.fsum([(x - mean) ** 2 for x in self.fps]) / len(self.fps)) ** 0.5 def __str__(self): pm = '+-' if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding: try: u'\xb1'.encode(sys.stdout.encoding) pm = u'\xb1' except: pass return "min:{best} max:{worst} mean:{mean} {pm} {std} per loop (mean {pm} std. dev. of {runs} run{run_plural}, {loops:,} loop{loop_plural} each)".format( pm=pm, runs=self.repeat, loops=self.loops, loop_plural="" if self.loops == 1 else "s", run_plural="" if self.repeat == 1 else "s", mean="%.*g%s" % (self._precision, self.average, self.unit), std="%.*g%s" % (self._precision, self.stdev, self.unit), best="%.*g%s" % (self._precision, self.best, self.unit), worst="%.*g%s" % (self._precision, self.worst, self.unit), ) def _repr_pretty_(self, p, cycle): unic = self.__str__() p.text(u'') def format_time(timespan, precision=3): """ https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L1473 Formats the timespan in a human readable form """ if timespan >= 60.0: # we have more than a minute, format that in a human readable form # Idea from http://snipplr.com/view/5713/ parts = [("d", 60 * 60 * 24), ("h", 60 * 60), ("min", 60), ("s", 1)] time = [] leftover = timespan for suffix, length in parts: value = int(leftover / length) if value > 0: leftover = leftover % length time.append(u'%s%s' % (str(value), suffix)) if leftover < 1: break return " ".join(time) # Unfortunately the unicode 'micro' symbol can cause problems in # certain terminals. # See bug: https://bugs.launchpad.net/ipython/+bug/348466 # Try to prevent crashes by being more secure than it needs to # E.g. eclipse is able to print a µ, but has no sys.stdout.encoding set. units = [u"s", u"ms", u'us', "ns"] # the save value if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding: try: u'\xb5'.encode(sys.stdout.encoding) units = [u"s", u"ms", u'\xb5s', "ns"] except: pass scaling = [1, 1e3, 1e6, 1e9] if timespan > 0.0: order = min(-int(math.floor(math.log10(timespan)) // 3), 3) else: order = 3 return u"%.*g %s" % (precision, timespan * scaling[order], units[order]) 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. The results may be slightly different from the lambda version due to calculation errors derived from float types, but the calculation results are virtually the same. a = 1.0,b = P[0],c = P[1],d = P[2],e = P[3],f = P[4] :param data: :param y: np.c_[d, e, a, c, b] :param f: f == P[4, 0] :return: this_return == np.array([ellipse_model(x, y) for (x, y) in data ]) """ return data.dot(y) + f # @profile def fit_rotated_ellipse_ransac(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 datamod = np.concatenate( [data, data ** 2, (data[:, 0] * data[:, 1])[:, np.newaxis], np.ones((len_data, 1), dtype=ret_dtype), (-1 * data[:, 0] ** 2)[:, np.newaxis]], axis=1, dtype=ret_dtype) datamod_slim = np.array(datamod[:, :5], dtype=ret_dtype) datamod_rng = datamod[rng_sample] 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] return fit_rotated_ellipse(effective_data_arr, effective_sample_p_arr) # @profile def fit_rotated_ellipse(data, P): a = 1.0 b = P[0] c = P[1] d = P[2] e = P[3] f = P[4] # The cost of trigonometric functions is high. theta = 0.5 * np.arctan(b / (a - c), dtype=np.float64) theta_sin = np.sin(theta, dtype=np.float64) theta_cos = np.cos(theta, dtype=np.float64) 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) class HSRAC_cls(object): def __init__(self): # I'd like to take into account things like print, end_time - start_time processing time, etc., but it's too much trouble. # For measuring total processing time self.main_start_time = timeit.default_timer() self.rng = np.random.default_rng() self.cvparam = CvParameters(default_radius, default_step) self.cv_modeo = ["first_frame", "radius_adjust", "blink_adjust", "normal"] self.now_modeo = self.cv_modeo[0] self.auto_radius_calc = Auto_Radius_Calc() self.blink_detector = Blink_Detector() self.center_q1 = Blink_Detector() self.center_correct = CenterCorrection() self.cap = None self.timedict = {"to_gray": [], "int_img": [], "conv_int": [], "crop": [], "total_cv": []} # ransac self.rng = np.random.default_rng() self.kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)) def open_video(self, video_path): # Temporary implementation to run cap = cv2.VideoCapture(video_path) if not cap.isOpened(): raise IOError("Error opening video stream or file") self.cap = cap return True def read_frame(self): # Temporary implementation to run if not self.cap.isOpened(): return False ret, frame = self.cap.read() if ret: # I have set it to grayscale (1ch) just in case, but if the frame is 1ch, this line can be commented out. self.current_image_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) return True return False def single_run(self): # Temporary implementation to run ## default_radius = 14 frame = self.current_image_gray frame = cv2.copyMakeBorder(frame, 21, 21, 21, 21, cv2.BORDER_CONSTANT, value=[255, 255, 255]) # add a border to prevent overcropping the image. if self.now_modeo == self.cv_modeo[1]: # adjustment of radius # debug print # if calc_print_enable: # temp_radius = self.auto_radius_calc.get_radius() # print('Now radius:', temp_radius) # self.cvparam.radius = temp_radius self.cvparam.radius = self.auto_radius_calc.get_radius() if self.auto_radius_calc.adj_comp_flag: self.now_modeo = self.cv_modeo[2] if not skip_blink_detect else self.cv_modeo[3] radius, pad, step, hsf = self.cvparam.get_rpsh() # For measuring processing time of image processing cv_start_time = timeit.default_timer() gray_frame = frame self.timedict["to_gray"].append(timeit.default_timer() - cv_start_time) # Calculate the integral image of the frame int_start_time = timeit.default_timer() # BORDER_CONSTANT is faster than BORDER_REPLICATE There seems to be almost no negative impact when BORDER_CONSTANT is used. frame_pad = cv2.copyMakeBorder(gray_frame, pad, pad, pad, pad, cv2.BORDER_CONSTANT) frame_int = cv2.integral(frame_pad) self.timedict["int_img"].append(timeit.default_timer() - int_start_time) # Convolve the feature with the integral image conv_int_start_time = timeit.default_timer() xy_step = frameint_get_xy_step(frame_int.shape, step, pad, start_offset=None, end_offset=None) frame_conv, response, center_xy = conv_int(frame_int, hsf, step, pad, xy_step) self.timedict["conv_int"].append(timeit.default_timer() - conv_int_start_time) crop_start_time = timeit.default_timer() # Define the center point and radius center_x, center_y = center_xy upper_x = center_x + radius lower_x = center_x - radius upper_y = center_y + radius lower_y = center_y - radius # Crop the image using the calculated bounds cropped_image = gray_frame[lower_y:upper_y, lower_x:upper_x] 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 + self.center_correct.center_q1_radius lower_x = center_x - self.center_correct.center_q1_radius upper_y = center_y + self.center_correct.center_q1_radius lower_y = center_y - self.center_correct.center_q1_radius self.center_q1.add_response(cv2.mean(gray_frame[lower_y:upper_y, lower_x:upper_x])[0]) else: self.blink_detector.calc_thresh() self.center_q1.calc_thresh() self.now_modeo = self.cv_modeo[3] else: if 0 in cropped_image.shape: # 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 pass else: # pass if not self.center_correct.setup_comp: self.center_correct.init_array(gray_frame.shape, self.center_q1.quartile_1, radius) center_x, center_y = self.center_correct.correction(gray_frame, center_x, center_y) # Define the center point and radius center_xy = (center_x, center_y) upper_x = center_x + radius lower_x = center_x - radius upper_y = center_y + radius lower_y = center_y - radius # Crop the image using the calculated bounds cropped_image = gray_frame[lower_y:upper_y, lower_x:upper_x] # if imshow_enable or save_video: # cv2.circle(frame, (orig_x, orig_y), 6, (0, 0, 255), -1) # cv2.circle(frame, (center_x, center_y), 3, (255, 0, 0), -1) # If you want to update response_max. it may be more cost-effective to rewrite response_list in the following way # https://stackoverflow.com/questions/42771110/fastest-way-to-left-cycle-a-numpy-array-like-pop-push-for-a-queue cv_end_time = timeit.default_timer() self.timedict["crop"].append(cv_end_time - crop_start_time) self.timedict["total_cv"].append(cv_end_time - cv_start_time) # if calc_print_enable: # the lower the response the better the likelyhood of there being a pupil. you can adujst the radius and steps accordingly # print('Kernel response:', response) # print('Pixel position:', center_xy) if imshow_enable: if self.now_modeo != self.cv_modeo[0] and self.now_modeo != self.cv_modeo[1]: if 0 in cropped_image.shape: # If shape contains 0, it is not detected well. pass else: cv2.imshow("crop", cropped_image) cv2.imshow("frame", frame) if cv2.waitKey(1) & 0xFF == ord("q"): pass if self.now_modeo == self.cv_modeo[0]: # Moving from first_frame to the next mode if skip_autoradius and skip_blink_detect: self.now_modeo = self.cv_modeo[3] elif skip_autoradius: self.now_modeo = self.cv_modeo[2] else: self.now_modeo = self.cv_modeo[1] newFrame2 = frame.copy() #frame = cropped_image # For measuring processing time of image processing cv_start_time = timeit.default_timer() # Crop first to reduce the amount of data to process. # frame = cropped_image[0:len(cropped_image) - 10, :] # To reduce the processing data, first convert to 1-channel and then blur. # The processing results were the same when I swapped the order of blurring and 1-channelization. frame_gray = cv2.GaussianBlur(frame, (5, 5), 0) upper_x = center_x + 20 lower_x = center_x - 20 upper_y = center_y + 20 lower_y = center_y - 20 # Crop the image using the calculated bounds frame_gray = frame_gray[lower_y:upper_y, lower_x:upper_x] frame = frame_gray # 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) maxloc0_hf, maxloc1_hf = int(0.5 * max_loc[0]), int(0.5 * max_loc[1]) # crop 15% sqare around min_loc # frame_gray = frame_gray[max_loc[1] - maxloc1_hf:max_loc[1] + maxloc1_hf, # max_loc[0] - maxloc0_hf:max_loc[0] + maxloc0_hf] threshold_value = min_val + thresh_add _, thresh = cv2.threshold(frame_gray, threshold_value, 255, cv2.THRESH_BINARY) # print(thresh.shape, frame_gray.shape) try: opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, self.kernel) closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, self.kernel) th_frame = 255 - closing except: # I want to eliminate try here because try tends to be slow in execution. th_frame = 255 - frame_gray contours, _ = cv2.findContours(th_frame, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) hull = [] # This way is faster than contours[i] # But maybe this one is faster. hull = [cv2.convexHull(cnt, False) for cnt in contours] for cnt in contours: hull.append(cv2.convexHull(cnt, False)) if not hull: # If empty, go to next loop pass try: cnt = sorted(hull, key=cv2.contourArea) maxcnt = cnt[-1] # ellipse = cv2.fitEllipse(maxcnt) ransac_data = fit_rotated_ellipse_ransac(maxcnt.reshape(-1, 2), self.rng) if ransac_data is None: # ransac_data is None==maxcnt.shape[0]= 2.1 * h: #new blink detection algo lmao this works pretty good actually print("RAN BLINK") #return center_x, center_y, frame, frame, True csx = frame.shape[0] csy = frame.shape[1] cx = center_x - (csx - cx) # we find the difference between the crop size and ransac point, and subtract from the center point from HSF cy = center_y - (csy - cy) cv_end_time = timeit.default_timer() if imshow_enable or save_video: cv2.drawContours(frame_gray, contours, -1, (255, 0, 0), 1) cv2.circle(frame_gray, (cx, cy), 2, (0, 0, 255), -1) # cx1, cy1, w1, h1, theta1 = fit_rotated_ellipse(maxcnt.reshape(-1, 2)) cv2.ellipse(frame_gray, (cx, cy), (w, h), theta * 180.0 / np.pi, 0.0, 360.0, (50, 250, 200), 1, ) except: pass # print(frame_gray.shape, thresh.shape) try: return cx, cy, thresh, frame except: return center_x, center_y, thresh, frame class External_Run_HSRACS: hsrac = HSRAC_cls() def HSRACS(self): External_Run_HSRACS.hsrac.current_image_gray = self.current_image_gray center_x, center_y, thresh, frame = External_Run_HSRACS.hsrac.single_run() return center_x, center_y, thresh, frame if __name__ == '__main__': hsrac = HSRAC_cls() hsrac.open_video(video_path) while hsrac.read_frame(): _ = hsrac.single_run()