diff --git a/EyeTrackApp/daddy.py b/EyeTrackApp/daddy.py index 5e860a2..8b5740a 100644 --- a/EyeTrackApp/daddy.py +++ b/EyeTrackApp/daddy.py @@ -1,9 +1,13 @@ +import sys from typing import Tuple +import math import numpy as np import cv2 import onnxruntime +from one_euro_filter import OneEuroFilter +from utils.misc_utils import FastMedian # DADDY # Please change the name of this script and the name of the method if you have something better. @@ -14,10 +18,11 @@ kernel_size = 7 model_file = "daddy230210.onnx" # The model file name will be changed when performance stabilises. # SHA256 for model version verification # daddy230210.onnx = 59e59aa2a21024884200dd3acbd5e6a2e8d7209c46555fbdc727d4fe3adb68d3 -imshow_enable = True +imshow_enable = False save_video = False save_filepath = 'output.mp4' + def get_max_preds(batch_heatmaps): # base:https://github.com/ilovepose/DarkPose batch_size = batch_heatmaps.shape[0] @@ -26,18 +31,18 @@ def get_max_preds(batch_heatmaps): heatmaps_reshaped = batch_heatmaps.reshape((batch_size, num_joints, -1)) idx = np.argmax(heatmaps_reshaped, 2) maxvals = np.amax(heatmaps_reshaped, 2) - + maxvals = maxvals.reshape((batch_size, num_joints, 1)) idx = idx.reshape((batch_size, num_joints, 1)) - + preds = np.tile(idx, (1, 1, 2)).astype(np.float32) - + preds[:, :, 0] = (preds[:, :, 0]) % width preds[:, :, 1] = np.floor((preds[:, :, 1]) / width) - + pred_mask = np.tile(np.greater(maxvals, 0.0), (1, 1, 2)) pred_mask = pred_mask.astype(np.float32) - + preds *= pred_mask return preds, maxvals @@ -48,15 +53,15 @@ def taylor(hm, coord): heatmap_width = hm.shape[1] px = int(coord[0]) py = int(coord[1]) - if 1 < px < heatmap_width-2 and 1 < py < heatmap_height-2: - dx = 0.5 * (hm[py][px+1] - hm[py][px-1]) - dy = 0.5 * (hm[py+1][px] - hm[py-1][px]) - dxx = 0.25 * (hm[py][px+2] - 2 * hm[py][px] + hm[py][px-2]) - dxy = 0.25 * (hm[py+1][px+1] - hm[py-1][px+1] - hm[py+1][px-1] \ - + hm[py-1][px-1]) - dyy = 0.25 * (hm[py+2*1][px] - 2 * hm[py][px] + hm[py-2*1][px]) - derivative = np.matrix([[dx],[dy]]) - hessian = np.matrix([[dxx,dxy],[dxy,dyy]]) + if 1 < px < heatmap_width - 2 and 1 < py < heatmap_height - 2: + dx = 0.5 * (hm[py][px + 1] - hm[py][px - 1]) + dy = 0.5 * (hm[py + 1][px] - hm[py - 1][px]) + dxx = 0.25 * (hm[py][px + 2] - 2 * hm[py][px] + hm[py][px - 2]) + dxy = 0.25 * (hm[py + 1][px + 1] - hm[py - 1][px + 1] - hm[py + 1][px - 1] \ + + hm[py - 1][px - 1]) + dyy = 0.25 * (hm[py + 2 * 1][px] - 2 * hm[py][px] + hm[py - 2 * 1][px]) + derivative = np.matrix([[dx], [dy]]) + hessian = np.matrix([[dxx, dxy], [dxy, dyy]]) if dxx * dyy - dxy ** 2 != 0: hessianinv = hessian.I offset = -hessianinv * derivative @@ -74,38 +79,39 @@ def gaussian_blur(hm, kernel): width = hm.shape[3] for i in range(batch_size): for j in range(num_joints): - origin_max = np.max(hm[i,j]) + origin_max = np.max(hm[i, j]) dr = np.zeros((height + 2 * border, width + 2 * border)) - dr[border: -border, border: -border] = hm[i,j].copy() + dr[border: -border, border: -border] = hm[i, j].copy() dr = cv2.GaussianBlur(dr, (kernel, kernel), 0) - hm[i,j] = dr[border: -border, border: -border].copy() - hm[i,j] *= origin_max / np.max(hm[i,j]) + hm[i, j] = dr[border: -border, border: -border].copy() + hm[i, j] *= origin_max / np.max(hm[i, j]) return hm -def get_final_preds(hm,realsize): +def get_final_preds(hm, realsize): # base:https://github.com/ilovepose/DarkPose coords, maxvals = get_max_preds(hm) - + # post-processing hm = gaussian_blur(hm, kernel_size) hm = np.maximum(hm, 1e-10) hm = np.log(hm) for n in range(coords.shape[0]): for p in range(coords.shape[1]): - coords[n,p] = taylor(hm[n][p], coords[n][p]) - + coords[n, p] = taylor(hm[n][p], coords[n][p]) + preds = coords.copy() - preds=(preds/heatmap_size)*realsize#input_size - + preds = (preds / heatmap_size) * realsize # input_size + # Transform back # for i in range(coords.shape[0]): # preds[i] = transform_preds( # coords[i], center[i], scale[i], [heatmap_width, heatmap_height] # ) - + return preds, maxvals + def resize_with_pad(image: np.array, new_shape: Tuple[int, int], padding_color: Tuple[int] = (255, 255, 255)) -> np.array: @@ -120,16 +126,54 @@ def resize_with_pad(image: np.array, image: Resized image with padding """ original_shape = (image.shape[1], image.shape[0]) - ratio = float(max(new_shape))/max(original_shape) - new_size = tuple([int(x*ratio) for x in original_shape]) + ratio = float(max(new_shape)) / max(original_shape) + new_size = tuple([int(x * ratio) for x in original_shape]) image = cv2.resize(image, new_size) delta_w = new_shape[0] - new_size[0] delta_h = new_shape[1] - new_size[1] - top, bottom = delta_h//2, delta_h-(delta_h//2) - left, right = delta_w//2, delta_w-(delta_w//2) + top, bottom = delta_h // 2, delta_h - (delta_h // 2) + left, right = delta_w // 2, delta_w - (delta_w // 2) image = cv2.copyMakeBorder(image, top, bottom, left, right, cv2.BORDER_CONSTANT, value=padding_color) return image + +# Better Eye aspEct Ratio +class BEER(object): + def __init__(self): + self.ear_th = 0.2 + self.ear_min = 0.05 + self.ear_max = 0.2 + self.p03_med = FastMedian(k=256) + self.prev_ear = 0.5 + # todo https://peerj.com/articles/cs-943/ + + def ear(self, pred): + p15 = np.linalg.norm(pred[1]-pred[5]) + p24 = np.linalg.norm(pred[2]-pred[4]) + p03 = np.linalg.norm(pred[0]-pred[3]) + self.p03_med+p03 + if p03 > self.p03_med.median()*1.5: + return self.prev_ear + ear = (p15+p24)/(2*self.p03_med.median()) + self.ear_minmax(ear) + norm_ear = self.ear_norm(ear) + self.prev_ear = norm_ear.copy() + return norm_ear + def ear_minmax(self,ear): + + if ear < self.ear_min: + self.ear_min = ear.copy() + if ear > self.ear_max: + self.ear_max = ear.copy() + + def ear_norm(self,ear): + return (ear-self.ear_min)/(self.ear_max-self.ear_min) # todo:It is better to add very small values to avoid zero division. + + +# +# loopnum = 0 +# + # Deep leArning lanDmark Detection for eYes class DADDY_cls(object): def __init__(self): @@ -139,13 +183,34 @@ class DADDY_cls(object): options.intra_op_num_threads = 4 # This number should be changed accordingly options.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL - - ort_session = onnxruntime.InferenceSession(model_file, sess_options=options) + + ort_session = onnxruntime.InferenceSession(model_file, sess_options=options, providers=["CPUExecutionProvider"]) ort_session.set_providers(['CPUExecutionProvider']) # only cpu mode self.ort_session = ort_session self.input_name = ort_session.get_inputs()[0].name self.output_name = ort_session.get_outputs()[0].name + + min_cutoff = 0.0004 + beta = 0.9 + input_point = np.zeros((11, 2)) # np.array([1, 1]) + self.one_euro_filter = OneEuroFilter( + input_point, + min_cutoff=min_cutoff, + beta=beta + ) + # self.ear_oef = OneEuroFilter( + # np.zeros(1), + # min_cutoff=min_cutoff, + # beta=beta + # ) # memo: Parameters need tuning + + self.beer = BEER() + + # filepath = 'test.mp4' + # codec = cv2.VideoWriter_fourcc(*"mp4v") + # video = cv2.VideoWriter(filepath, codec, 60.0, (200, 150), 0) # (60, 60)) # (150, 200)) + # self.video = video def open_video(self, video_path): # Temporary implementation to run @@ -169,21 +234,23 @@ class DADDY_cls(object): def single_run(self): # Temporary implementation to run - gray_frame = self.current_image_gray - + + # todo: If it's the left hand eye, flip the image left to right. + + gray_frame = self.current_image_gray.copy() + # frame_resize=resize_with_pad(gray_frame,(input_size,input_size)) # or frame_resize = cv2.resize(gray_frame, (input_size, input_size)) imgs = np.divide(frame_resize[np.newaxis, np.newaxis], 255, dtype=np.float32) # input/255.0 - + pred_heatmap = self.ort_session.run(None, {self.input_name: imgs})[0] # .reshape((-1, 2)) # if imshow_enable: # heatmap = pred_heatmap.reshape((-1, heatmap_size, heatmap_size)) # for i in range(heatmap.shape[0]): # cv2.imshow("heatmap_{}".format(i + 1), heatmap[i]) - - pred, max_val = get_final_preds(pred_heatmap,(self.current_image_gray.shape[1],self.current_image_gray.shape[0])) + pred, max_val = get_final_preds(pred_heatmap, (self.current_image_gray.shape[1], self.current_image_gray.shape[0])) pred = pred.reshape((-1, 2)) # or # pred, max_val = get_final_preds(pred_heatmap, input_size) @@ -193,28 +260,49 @@ class DADDY_cls(object): # scale_y = input_size / height # pred[:, 0] *= scale_x # pred[:, 1] *= scale_y - + + pred = self.one_euro_filter(pred) kps = pred.astype(np.int32) - # if save_video or imshow_enable: - # for i in range(kps.shape[0]): - # if i < 6: - # color = (0, 0, 255) - # elif i == 6: - # color = 128 #(0, 255, 0) - # else: - # color = (255, 0, 0) - # cv2.circle(gray_frame, (kps[i, 0], kps[i, 1]), 1, color, 2) - # cv2.putText(gray_frame, str(i), (kps[i, 0] - 10, kps[i, 1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 0, 255), 1) - # cv2.imshow("gray_frame", gray_frame) - # if cv2.waitKey(1) & 0xFF == ord("q"): - # pass + # eyecenter = kps[:6].mean(axis=0).astype(int) + # temp_eyecenter = pred[:6].mean(axis=0) + ear = self.beer.ear(pred) + # ear=self.ear_oef(ear[np.newaxis])#memo: Parameters need tuning + + if save_video or imshow_enable: + for i in range(kps.shape[0]): + if i < 6: + color = (0, 0, 255) + elif i == 6: + color = 128 # (0, 255, 0) + else: + color = (255, 0, 0) + cv2.circle(gray_frame, (kps[i, 0], kps[i, 1]), 1, color, 2) + cv2.putText(gray_frame, str(i), (kps[i, 0] - 10, kps[i, 1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 0, 255), 1) + # cv2.circle(gray_frame, (eyecenter.tolist()), 1, (128, 128, 0), 2) + # cv2.circle(gray_frame, (iriscenter.tolist()), 1, (0, 128, 128), 2) + # cv2.circle(gray_frame,(eyecenter.tolist()),int(self.center_dist_med.median()),(0, 0, 255),1) + # cv2.ellipse(gray_frame, (eyecenter.tolist(), (int(self.iris_med_w.median()*2.5), int(self.iris_med_h.median()*2.5)), np.rad2deg(self.eye_med_rad.median())), (255, 0, 0), thickness=1) + cv2.putText(gray_frame, "EAR: "+str(ear), (self.current_image_gray.shape[1]//10, self.current_image_gray.shape[0]//10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (255,0,0), 1) + cv2.imshow("gray_frame", gray_frame) + if cv2.waitKey(1) & 0xFF == ord("q"): + pass + # global loopnum + # if loopnum < 1350*2: + # # self.video.write(cv2.resize(gray_frame.copy(), (200, 150), None)) + # loopnum += 1 + # else: + # # self.video.release() + # cv2.destroyAllWindows() + # sys.exit() + # if w_video: + # video.release() # kps[i, :] = (x, y) # i == [0:6] = Inner and outer corners of eyes and eyelids # i == [6] = pupil # i == [7:] = iris - return kps[6, :] + return ear class External_Run_DADDY(object): @@ -231,5 +319,4 @@ if __name__ == "__main__": daddy = DADDY_cls() daddy.open_video(video_path) while daddy.read_frame(): - _ = daddy.single_run() - \ No newline at end of file + _ = daddy.single_run() \ No newline at end of file diff --git a/EyeTrackApp/eye_processor.py b/EyeTrackApp/eye_processor.py index 5ffb2b4..68fe493 100644 --- a/EyeTrackApp/eye_processor.py +++ b/EyeTrackApp/eye_processor.py @@ -309,6 +309,7 @@ class EyeProcessor: def ALGOSELECT(self): + # self.DADDYM() if self.failed == 0 and self.firstalgo != None: self.firstalgo() else: diff --git a/EyeTrackApp/utils/misc_utils.py b/EyeTrackApp/utils/misc_utils.py index 0d99d66..c205fe7 100644 --- a/EyeTrackApp/utils/misc_utils.py +++ b/EyeTrackApp/utils/misc_utils.py @@ -1,4 +1,6 @@ import os +import typing + is_nt = True if os.name == "nt" else False def PlaySound(*args, **kwargs): pass @@ -12,3 +14,47 @@ if is_nt: def clamp(x, low, high): return max(low, min(x, high)) + + +def lst_median(lst, ordered=False): + # https://github.com/emilianavt/OpenSeeFace/blob/6f24efc4f58eb7cca47ec2146d934eabcc207e46/remedian.py + assert lst, "median needs a non-empty list" + n = len(lst) + p = q = n // 2 + if n < 3: + p, q = 0, n - 1 + else: + lst = lst if ordered else sorted(lst) + if not n % 2: # for even-length lists, use mean of mid 2 nums + q = p - 1 + return lst[p] if p == q else (lst[p] + lst[q]) / 2 + + +class FastMedian: + # https://github.com/emilianavt/OpenSeeFace/blob/6f24efc4f58eb7cca47ec2146d934eabcc207e46/remedian.py + # Initialization + def __init__(self, inits: typing.Optional[typing.Sequence] = [], k=64): # after some experimentation, 64 works ok + self.all, self.k = [], k + self.more, self.__median = None, None + if inits is not None: + [self + x for x in inits] + + # When full, push the median of current values to next list, then reset. + def __add__(self, x): + self.__median = None + self.all.append(x) # It would be faster to pre-allocate an array and assign it by index. + if len(self.all) == self.k: + self.more = self.more or FastMedian(k=self.k) + self.more + self.__medianPrim(self.all) + # It's going to be slower because of the re-allocation. + self.all = [] # reset + + # If there is a next list, ask its median. Else, work it out locally. + def median(self): + return self.more.median() if self.more else self.__medianPrim(self.all) + + # Only recompute median if we do not know it already. + def __medianPrim(self, all): + if self.__median is None: + self.__median = lst_median(all, ordered=False) + return self.__median \ No newline at end of file