import os import numpy as np import math import cv2 import onnxruntime import time import queue import threading import copy from retinaface import RetinaFaceDetector from remedian import remedian def resolve(name): f = os.path.join(os.path.dirname(__file__), name) return f def clamp_to_im(pt, w, h): x = pt[0] y = pt[1] if x < 0: x = 0 if y < 0: y = 0 if x >= w: x = w-1 if y >= h: y = h-1 return (int(x), int(y+1)) def rotate(origin, point, a): a = -a ox, oy = origin px, py = point qx = ox + math.cos(a) * (px - ox) - math.sin(a) * (py - oy) qy = oy + math.sin(a) * (px - ox) + math.cos(a) * (py - oy) return qx, qy def angle(p1, p2): p1 = np.array(p1) p2 = np.array(p2) a = np.arctan2(*(p2 - p1)[::-1]) return (a % (2 * np.pi)) def compensate(p1, p2): a = angle(p1, p2) return rotate(p1, p2, a), a def rotate_image(image, a, center): (h, w) = image.shape[:2] a = np.rad2deg(a) M = cv2.getRotationMatrix2D((center[0], center[1]), a, 1.0) rotated = cv2.warpAffine(image, M, (w, h)) return rotated def intersects(r1, r2, amount=0.3): area1 = r1[2] * r1[3] area2 = r2[2] * r2[3] inter = 0.0 total = area1 + area2 r1_x1, r1_y1, w, h = r1 r1_x2 = r1_x1 + w r1_y2 = r1_y1 + h r2_x1, r2_y1, w, h = r2 r2_x2 = r2_x1 + w r2_y2 = r2_y1 + h left = max(r1_x1, r2_x1) right = min(r1_x2, r2_x2) top = max(r1_y1, r2_y1) bottom = min(r1_y2, r2_y2) if left < right and top < bottom: inter = (right - left) * (bottom - top) total -= inter if inter / total >= amount: return True return False #return not (r1_x1 > r2_x2 or r1_x2 < r2_x1 or r1_y1 > r2_y2 or r1_y2 < r2_y1) def group_rects(rects): rect_groups = {} for rect in rects: rect_groups[str(rect)] = [-1, -1, []] group_id = 0 for i, rect in enumerate(rects): name = str(rect) group = group_id group_id += 1 if rect_groups[name][0] < 0: rect_groups[name] = [group, -1, []] else: group = rect_groups[name][0] for j, other_rect in enumerate(rects): if i == j: continue; inter = intersects(rect, other_rect) if intersects(rect, other_rect): rect_groups[str(other_rect)] = [group, -1, []] return rect_groups def logit(p, factor=16.0): if p >= 1.0: p = 0.9999999 if p <= 0.0: p = 0.0000001 p = p/(1-p) return float(np.log(p)) / float(factor) def matrix_to_quaternion(m): t = 0.0 q = [0.0, 0.0, 0, 0.0] if m[2,2] < 0: if m[0,0] > m[1,1]: t = 1 + m[0,0] - m[1,1] - m[2,2] q = [t, m[0,1]+m[1,0], m[2,0]+m[0,2], m[1,2]-m[2,1]] else: t = 1 - m[0,0] + m[1,1] - m[2,2] q = [m[0,1]+m[1,0], t, m[1,2]+m[2,1], m[2,0]-m[0,2]] else: if m[0,0] < -m[1,1]: t = 1 - m[0,0] - m[1,1] + m[2,2] q = [m[2,0]+m[0,2], m[1,2]+m[2,1], t, m[0,1]-m[1,0]] else: t = 1 + m[0,0] + m[1,1] + m[2,2] q = [m[1,2]-m[2,1], m[2,0]-m[0,2], m[0,1]-m[1,0], t] q = np.array(q, np.float32) * 0.5 / np.sqrt(t) return q def worker_thread(session, input, crop_info, queue, input_name, idx, tracker): output = session.run([], {input_name: input})[0] conf, lms = tracker.landmarks(output[0], crop_info) queue.put((session, conf, lms, crop_info, idx)) class Feature(): def __init__(self, threshold=0.15, alpha=0.2, hard_factor=0.15, decay=0.001): self.median = remedian() self.min = None self.max = None self.hard_min = None self.hard_max = None self.threshold = threshold self.alpha = alpha self.hard_factor = hard_factor self.decay = decay self.last = 0 def update(self, x): new = self.update_state(x) filtered = self.last * self.alpha + new * (1 - self.alpha) self.last = filtered return filtered def update_state(self, x): self.median + x median = self.median.median() if self.min is None: if x < median and (median - x) / median > self.threshold: self.min = x self.hard_min = self.min + self.hard_factor * (median - self.min) return -1 return 0 else: if x < self.min: self.min = x self.hard_min = self.min + self.hard_factor * (median - self.min) return -1 if self.max is None: if x > median and (x - median) / median > self.threshold: self.max = x self.hard_max = self.max - self.hard_factor * (self.max - median) return 1 return 0 else: if x > self.max: self.max = x self.hard_max = self.max - self.hard_factor * (self.max - median) return 1 if self.min < self.hard_min: self.min = self.hard_min * self.decay + self.min * (1 - self.decay) if self.max > self.hard_max: self.max = self.hard_max * self.decay + self.max * (1 - self.decay) if x < median: return - (1 - (x - self.min) / (median - self.min)) elif x > median: return (x - median) / (self.max - median) return 0 class FeatureExtractor(): def __init__(self): self.eye_l = Feature() self.eye_r = Feature() self.eyebrow_updown_l = Feature() self.eyebrow_updown_r = Feature() self.eyebrow_quirk_l = Feature(threshold=0.05) self.eyebrow_quirk_r = Feature(threshold=0.05) self.eyebrow_steepness_l = Feature(threshold=0.05) self.eyebrow_steepness_r = Feature(threshold=0.05) self.mouth_corner_updown_l = Feature() self.mouth_corner_updown_r = Feature() self.mouth_corner_inout_l = Feature(threshold=0.02) self.mouth_corner_inout_r = Feature(threshold=0.02) self.mouth_open = Feature() self.mouth_wide = Feature(threshold=0.02) def align_points(self, a, b, pts): a = tuple(a) b = tuple(b) alpha = angle(a, b) alpha = np.rad2deg(alpha) if alpha >= 90: alpha = - (alpha - 180) if alpha <= -90: alpha = - (alpha + 180) alpha = np.deg2rad(alpha) aligned_pts = [] for pt in pts: aligned_pts.append(np.array(rotate(a, pt, alpha))) return alpha, np.array(aligned_pts) def update(self, pts): features = {} norm_distance_x = np.mean([pts[0, 0] - pts[16, 0], pts[1, 0] - pts[15, 0]]) norm_distance_y = np.mean([pts[27, 1] - pts[28, 1], pts[28, 1] - pts[29, 1], pts[29, 1] - pts[30, 1]]) a1, f_pts = self.align_points(pts[42], pts[45], pts[[43, 44, 47, 46]]) f = np.clip((np.mean([f_pts[0,1], f_pts[1,1]]) - np.mean([f_pts[2,1], f_pts[3,1]])) / norm_distance_y, 0, None) features["eye_l"] = self.eye_l.update(f) a2, f_pts = self.align_points(pts[36], pts[39], pts[[37, 38, 41, 40]]) f = np.clip((np.mean([f_pts[0,1], f_pts[1,1]]) - np.mean([f_pts[2,1], f_pts[3,1]])) / norm_distance_y, 0, None) features["eye_r"] = self.eye_r.update(f) a3, _ = self.align_points(pts[0], pts[16], []) a4, _ = self.align_points(pts[31], pts[35], []) norm_angle = np.mean(list(map(np.rad2deg, [a1, a2, a3, a4]))) a, f_pts = self.align_points(pts[22], pts[26], pts[[22, 23, 24, 25, 26]]) features["eyebrow_steepness_l"] = self.eyebrow_steepness_l.update(-np.rad2deg(a) - norm_angle) f = (np.mean([pts[22, 1], pts[26, 1]]) - pts[27, 1]) / norm_distance_y features["eyebrow_updown_l"] = self.eyebrow_updown_l.update(f) f = np.max(np.abs(np.array(f_pts[1:4]) - f_pts[0, 1])) / norm_distance_y features["eyebrow_quirk_l"] = self.eyebrow_quirk_l.update(f) a, f_pts = self.align_points(pts[17], pts[21], pts[[17, 18, 19, 20, 21]]) features["eyebrow_steepness_r"] = self.eyebrow_steepness_r.update(np.rad2deg(a) - norm_angle) f = (np.mean([pts[17, 1], pts[21, 1]]) - pts[27, 1]) / norm_distance_y features["eyebrow_updown_r"] = self.eyebrow_updown_r.update(f) f = np.max(np.abs(np.array(f_pts[1:4]) - f_pts[0, 1])) / norm_distance_y features["eyebrow_quirk_r"] = self.eyebrow_quirk_r.update(f) upper_mouth_line = np.mean([pts[49, 1], pts[50, 1], pts[51, 1]]) center_line = np.mean([pts[50, 0], pts[60, 0], pts[27, 0], pts[30, 0], pts[64, 0], pts[55, 0]]) f = (upper_mouth_line - pts[62, 1]) / norm_distance_y features["mouth_corner_updown_l"] = self.mouth_corner_updown_l.update(f) f = abs(center_line - pts[62, 0]) / norm_distance_x features["mouth_corner_inout_l"] = self.mouth_corner_inout_l.update(f) f = (upper_mouth_line - pts[58, 1]) / norm_distance_y features["mouth_corner_updown_r"] = self.mouth_corner_updown_r.update(f) f = abs(center_line - pts[58, 0]) / norm_distance_x features["mouth_corner_inout_r"] = self.mouth_corner_inout_r.update(f) f = (np.mean([pts[59, 1], pts[60, 1], pts[61, 1]]) - np.mean([pts[65, 1], pts[64, 1], pts[63, 1]])) / norm_distance_y features["mouth_open"] = self.mouth_open.update(f) f = abs(pts[58, 0] - pts[62, 0]) / norm_distance_x features["mouth_wide"] = self.mouth_wide.update(f) return features class FaceInfo(): def __init__(self, id, tracker): self.id = id self.frame_count = -1 self.tracker = tracker self.contour_pts = [0,1,8,15,16,27,28,29,30,31,32,33,34,35,36,39,42,45] self.face_3d = copy.copy(self.tracker.face_3d) self.reset() self.alive = False self.coord = None self.limit_3d_adjustment = True self.update_count_delta = 75. self.update_count_max = 7500. def reset(self): self.alive = False self.conf = None self.lms = None self.eye_state = None self.rotation = None self.translation = None self.success = None self.quaternion = None self.euler = None self.pnp_error = None self.pts_3d = None self.eye_blink = None self.bbox = None self.pnp_error = 0 self.features = FeatureExtractor() self.current_features = {} self.contour = np.zeros((21,3)) self.update_counts = np.zeros((66,2)) self.update_contour() def update(self, result, coord, frame_count): self.frame_count = frame_count if result is None: self.reset() else: self.conf, (self.lms, self.eye_state) = result self.coord = coord self.alive = True def update_contour(self): self.contour = np.array(self.face_3d[self.contour_pts]) def normalize_pts3d(self, pts_3d): # Calculate angle using nose pts_3d[:, 0:2] -= pts_3d[30, 0:2] alpha = angle(pts_3d[30, 0:2], pts_3d[27, 0:2]) alpha -= np.deg2rad(90) R = np.matrix([[np.cos(alpha), -np.sin(alpha)], [np.sin(alpha), np.cos(alpha)]]) pts_3d[:, 0:2] = (pts_3d - pts_3d[30])[:, 0:2].dot(R) + pts_3d[30, 0:2] # Vertical scale pts_3d[:, 1] /= np.mean((pts_3d[27:30, 1] - pts_3d[28:31, 1]) / (self.tracker.face_3d[27:30, 1] - self.tracker.face_3d[28:31, 1])) # Horizontal scale pts_3d[:, 0] /= np.mean(np.abs(pts_3d[[0, 36, 42], 0] - pts_3d[[16, 39, 45], 0]) / np.abs(self.tracker.face_3d[[0, 36, 42], 0] - self.tracker.face_3d[[16, 39, 45], 0])) return pts_3d def adjust_3d(self): if self.conf < 0.4 or self.pnp_error > 300: return max_runs = 1 eligible = np.delete(np.arange(0, 66), [30]) changed_any = False update_type = -1 d_o = np.ones((66,)) d_c = np.ones((66,)) for runs in range(max_runs): r = 1.0 + np.random.random_sample((66,3)) * 0.02 - 0.01 r[30, :] = 1.0 if self.euler[0] > -165 and self.euler[0] < 145: continue elif self.euler[1] > -10 and self.euler[1] < 20: r[:, 2] = 1.0 update_type = 0 else: r[:, 0:2] = 1.0 if self.euler[2] > 120 or self.euler[2] < 60: continue # Enable only one side of the points, depending on direction elif self.euler[1] < -10: update_type = 1 r[[0, 1, 2, 3, 4, 5, 6, 7, 17, 18, 19, 20, 21, 31, 32, 36, 37, 38, 39, 40, 41, 48, 49, 56, 57, 58, 59, 65], 2] = 1.0 eligible = [8, 9, 10, 11, 12, 13, 14, 15, 16, 22, 23, 24, 25, 26, 27, 28, 29, 33, 34, 35, 42, 43, 44, 45, 46, 47, 50, 51, 52, 53, 54, 55, 60, 61, 62, 63, 64] else: update_type = 1 r[[9, 10, 11, 12, 13, 14, 15, 16, 22, 23, 24, 25, 26, 34, 35, 42, 43, 44, 45, 46, 47, 51, 52, 53, 54, 61, 62, 63], 2] = 1.0 eligible = [0, 1, 2, 3, 4, 5, 6, 7, 8, 17, 18, 19, 20, 21, 27, 28, 29, 31, 32, 33, 36, 37, 38, 39, 40, 41, 48, 49, 50, 55, 56, 57, 58, 59, 60, 64, 65] if self.limit_3d_adjustment: eligible = np.nonzero(self.update_counts[:, update_type] < self.update_counts[:, abs(update_type - 1)] + self.update_count_delta)[0] if eligible.shape[0] <= 0: break if runs == 0: updated = copy.copy(self.face_3d[0:66]) o_projected = np.ones((66,2)) o_projected[eligible] = np.squeeze(np.array(cv2.projectPoints(self.face_3d[eligible], self.rotation, self.translation, self.tracker.camera, self.tracker.dist_coeffs)[0]), 1) c = updated * r c_projected = np.zeros((66,2)) c_projected[eligible] = np.squeeze(np.array(cv2.projectPoints(c[eligible], self.rotation, self.translation, self.tracker.camera, self.tracker.dist_coeffs)[0]), 1) changed = False d_o[eligible] = np.linalg.norm(o_projected[eligible] - self.lms[eligible, 0:2], axis=1) d_c[eligible] = np.linalg.norm(c_projected[eligible] - self.lms[eligible, 0:2], axis=1) indices = np.nonzero(d_c < d_o)[0] if indices.shape[0] > 0: if self.limit_3d_adjustment: indices = np.intersect1d(indices, eligible) if indices.shape[0] > 0: self.update_counts[indices, update_type] += 1 updated[indices] = c[indices] o_projected[indices] = c_projected[indices] changed = True changed_any = changed_any or changed if not changed: break if changed_any: # Update weighted by point confidence weights = np.zeros((66,3)) weights[:, :] = self.lms[0:66, 2:3] weights[weights > 0.7] = 1.0 weights = 1.0 - weights update_indices = np.arange(0, 66) if self.limit_3d_adjustment: update_indices = np.nonzero(self.update_counts[:, update_type] <= self.update_count_max)[0] self.face_3d[update_indices] = self.face_3d[update_indices] * weights[update_indices] + updated[update_indices] * (1. - weights[update_indices]) self.update_contour() self.pts_3d = self.normalize_pts3d(self.pts_3d) self.current_features = self.features.update(self.pts_3d[:, 0:2]) self.eye_blink = [] self.eye_blink.append(1 - min(max(0, -self.current_features["eye_r"]), 1)) self.eye_blink.append(1 - min(max(0, -self.current_features["eye_l"]), 1)) class Tracker(): def __init__(self, width, height, model_type=3, threshold=0.6, max_faces=1, discard_after=5, scan_every=3, bbox_growth=0.0, max_threads=4, silent=False, model_dir=None, no_gaze=False, use_retinaface=False): options = onnxruntime.SessionOptions() options.inter_op_num_threads = 1 options.intra_op_num_threads = max(max_threads,4) options.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL options.log_severity_level = 3 self.model_type = model_type self.models = [ "snv2_opt_fast.onnx", "mnv3_opt_fast.onnx", "snv2_opt_b.onnx", "mnv3_opt_b.onnx" ] model = self.models[self.model_type] model_base_path = resolve(os.path.join("models")) if model_dir is None: if not os.path.exists(model_base_path): model_base_path = resolve(os.path.join("..", "models")) else: model_base_path = model_dir self.retinaface = RetinaFaceDetector(model_path=os.path.join(model_base_path, "retinaface_640x640_opt.onnx"), json_path=os.path.join(model_base_path, "priorbox_640x640.json"), threads=max(max_threads,4), top_k=max_faces, res=(640, 640)) self.retinaface_scan = RetinaFaceDetector(model_path=os.path.join(model_base_path, "retinaface_640x640_opt.onnx"), json_path=os.path.join(model_base_path, "priorbox_640x640.json"), threads=2, top_k=max_faces, res=(640, 640)) self.use_retinaface = use_retinaface # Single face instance with multiple threads self.session = onnxruntime.InferenceSession(os.path.join(model_base_path, model), sess_options=options) # Multiple faces with single threads self.sessions = [] self.max_workers = min(max_threads, max_faces) extra_threads = max_threads % self.max_workers for i in range(self.max_workers): options = onnxruntime.SessionOptions() options.inter_op_num_threads = 1 options.intra_op_num_threads = max_threads // self.max_workers if options.intra_op_num_threads < 1: options.intra_op_num_threads = 1 elif i < extra_threads: options.intra_op_num_threads += 1 options.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL self.sessions.append(onnxruntime.InferenceSession(os.path.join(model_base_path, model), sess_options=options)) self.input_name = self.session.get_inputs()[0].name options = onnxruntime.SessionOptions() options.inter_op_num_threads = 1 options.intra_op_num_threads = max(max_threads,4) options.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL options.log_severity_level = 3 self.gaze_model = onnxruntime.InferenceSession(os.path.join(model_base_path, "mnv3_gaze32_split_opt.onnx"), sess_options=options) self.detection = onnxruntime.InferenceSession(os.path.join(model_base_path, "mnv3_detection_opt.onnx"), sess_options=options) self.faces = [] # Image normalization constants self.mean = np.float32(np.array([0.485, 0.456, 0.406])) self.std = np.float32(np.array([0.229, 0.224, 0.225])) self.mean = self.mean / self.std self.std = self.std * 255.0 # PnP solving self.face_3d = np.array([ [ 0.4551769692672 , 0.300895790030204, -0.764429433974752], [ 0.448998827123556, 0.166995837790733, -0.765143004071253], [ 0.437431554952677, 0.022655479179981, -0.739267175112735], [ 0.415033422928434, -0.088941454648772, -0.747947437846473], [ 0.389123587370091, -0.232380029794684, -0.704788385327458], [ 0.334630113904382, -0.361265387599081, -0.615587579236862], [ 0.263725112132858, -0.460009725616771, -0.491479221041573], [ 0.16241621322721 , -0.558037146073869, -0.339445180872282], [ 0. , -0.621079019321682, -0.287294770748887], [-0.16241621322721 , -0.558037146073869, -0.339445180872282], [-0.263725112132858, -0.460009725616771, -0.491479221041573], [-0.334630113904382, -0.361265387599081, -0.615587579236862], [-0.389123587370091, -0.232380029794684, -0.704788385327458], [-0.415033422928434, -0.088941454648772, -0.747947437846473], [-0.437431554952677, 0.022655479179981, -0.739267175112735], [-0.448998827123556, 0.166995837790733, -0.765143004071253], [-0.4551769692672 , 0.300895790030204, -0.764429433974752], [ 0.385529968662985, 0.402800553948697, -0.310031082540741], [ 0.322196658344302, 0.464439136821772, -0.250558059367669], [ 0.25409760441282 , 0.46420381416882 , -0.208177722146526], [ 0.186875436782135, 0.44706071961879 , -0.145299823706503], [ 0.120880983543622, 0.423566314072968, -0.110757158774771], [-0.120880983543622, 0.423566314072968, -0.110757158774771], [-0.186875436782135, 0.44706071961879 , -0.145299823706503], [-0.25409760441282 , 0.46420381416882 , -0.208177722146526], [-0.322196658344302, 0.464439136821772, -0.250558059367669], [-0.385529968662985, 0.402800553948697, -0.310031082540741], [ 0. , 0.293332603215811, -0.137582088779393], [ 0. , 0.194828701837823, -0.069158109325951], [ 0. , 0.103844017393155, -0.009151819844964], [ 0. , 0. , 0. ], [ 0.080626352317973, -0.041276068128093, -0.134161035564826], [ 0.046439347377934, -0.057675223874769, -0.102990627164664], [ 0. , -0.068753126205604, -0.090545348482397], [-0.046439347377934, -0.057675223874769, -0.102990627164664], [-0.080626352317973, -0.041276068128093, -0.134161035564826], [ 0.315905195966084, 0.298337502555443, -0.285107407636464], [ 0.275252345439353, 0.312721904921771, -0.244558251170671], [ 0.176394511553111, 0.311907184376107, -0.219205360345231], [ 0.131229723798772, 0.284447361805627, -0.234239149487417], [ 0.184124948330084, 0.260179585304867, -0.226590776513707], [ 0.279433549294448, 0.267363071770222, -0.248441437111633], [-0.131229723798772, 0.284447361805627, -0.234239149487417], [-0.176394511553111, 0.311907184376107, -0.219205360345231], [-0.275252345439353, 0.312721904921771, -0.244558251170671], [-0.315905195966084, 0.298337502555443, -0.285107407636464], [-0.279433549294448, 0.267363071770222, -0.248441437111633], [-0.184124948330084, 0.260179585304867, -0.226590776513707], [ 0.121155252430729, -0.208988660580347, -0.160606287940521], [ 0.041356305910044, -0.194484199722098, -0.096159882202821], [ 0. , -0.205180167345702, -0.083299217789729], [-0.041356305910044, -0.194484199722098, -0.096159882202821], [-0.121155252430729, -0.208988660580347, -0.160606287940521], [-0.132325402795928, -0.290857984604968, -0.187067868218105], [-0.064137791831655, -0.325377847425684, -0.158924039726607], [ 0. , -0.343742581679188, -0.113925986025684], [ 0.064137791831655, -0.325377847425684, -0.158924039726607], [ 0.132325402795928, -0.290857984604968, -0.187067868218105], [ 0.181481567104525, -0.243239316141725, -0.231284988892766], [ 0.083999507750469, -0.239717753728704, -0.155256465640701], [ 0. , -0.256058040176369, -0.0950619498899 ], [-0.083999507750469, -0.239717753728704, -0.155256465640701], [-0.181481567104525, -0.243239316141725, -0.231284988892766], [-0.074036069749345, -0.250689938345682, -0.177346470406188], [ 0. , -0.264945854681568, -0.112349967428413], [ 0.074036069749345, -0.250689938345682, -0.177346470406188], # Pupils and eyeball centers [ 0.257990002632141, 0.276080012321472, -0.219998998939991], [-0.257990002632141, 0.276080012321472, -0.219998998939991], [ 0.257990002632141, 0.276080012321472, -0.324570998549461], [-0.257990002632141, 0.276080012321472, -0.324570998549461] ], np.float32) self.camera = np.array([[width, 0, width/2], [0, width, height/2], [0, 0, 1]], np.float32) self.inverse_camera = np.linalg.inv(self.camera) self.dist_coeffs = np.zeros((4,1)) self.frame_count = 0 self.width = width self.height = height self.threshold = threshold self.max_faces = max_faces self.max_threads = max_threads self.discard = 0 self.discard_after = discard_after self.detected = 0 self.wait_count = 0 self.scan_every = scan_every self.bbox_growth = bbox_growth self.silent = silent self.res = 224. if self.model_type != 0 else 112. self.res_i = int(self.res) self.no_gaze = no_gaze self.debug_gaze = False self.face_info = [FaceInfo(id, self) for id in range(max_faces)] self.fail_count = 0 def detect_faces(self, frame): im = cv2.resize(frame, (224, 224), interpolation=cv2.INTER_LINEAR)[:,:,::-1] / self.std - self.mean im = np.expand_dims(im, 0) im = np.transpose(im, (0,3,1,2)) outputs, maxpool = self.detection.run([], {'input': im}) outputs = np.array(outputs) maxpool = np.array(maxpool) outputs[0, 0, outputs[0, 0] != maxpool[0, 0]] = 0 detections = np.flip(np.argsort(outputs[0,0].flatten())) results = [] for det in detections[0:self.max_faces]: y, x = det // 56, det % 56 c = outputs[0, 0, y, x] r = outputs[0, 1, y, x] * 112. x *= 4 y *= 4 r *= 1.0 if c < self.threshold: break results.append((x - r, y - r, 2 * r, 2 * r * 1.0)) results = np.array(results).astype(np.float32) if results.shape[0] > 0: results[:, [0,2]] *= frame.shape[1] / 224. results[:, [1,3]] *= frame.shape[0] / 224. return results def landmarks(self, tensor, crop_info): crop_x1, crop_y1, scale_x, scale_y, _ = crop_info avg_conf = 0 lms = [] res = self.res - 1 if self.model_type == 3 else self.res for i in range(0, 66): m = int(tensor[i].argmax()) x = m // 28 y = m % 28 conf = float(tensor[i][x,y]) avg_conf = avg_conf + conf off_x = res * ((1. * logit(tensor[66 + i][x, y])) - 0.0) off_y = res * ((1. * logit(tensor[66 * 2 + i][x, y])) - 0.0) off_x = math.floor(off_x + 0.5) off_y = math.floor(off_y + 0.5) lm_x = crop_y1 + scale_y * (res * (float(x) / 27.) + off_x) lm_y = crop_x1 + scale_x * (res * (float(y) / 27.) + off_y) lms.append((lm_x,lm_y,conf)) avg_conf = avg_conf / 66. return (avg_conf, np.array(lms)) def estimate_depth(self, face_info): lms = np.concatenate((face_info.lms, np.array([[face_info.eye_state[0][1], face_info.eye_state[0][2], face_info.eye_state[0][3]], [face_info.eye_state[1][1], face_info.eye_state[1][2], face_info.eye_state[1][3]]], np.float)), 0) image_pts = np.array(lms)[face_info.contour_pts, 0:2] success = False if not face_info.rotation is None: success, face_info.rotation, face_info.translation = cv2.solvePnP(face_info.contour, image_pts, self.camera, self.dist_coeffs, useExtrinsicGuess=True, rvec=np.transpose(face_info.rotation), tvec=np.transpose(face_info.translation), flags=cv2.SOLVEPNP_ITERATIVE) else: rvec = np.array([0, 0, 0], np.float32) tvec = np.array([0, 0, 0], np.float32) success, face_info.rotation, face_info.translation = cv2.solvePnP(face_info.contour, image_pts, self.camera, self.dist_coeffs, useExtrinsicGuess=True, rvec=rvec, tvec=tvec, flags=cv2.SOLVEPNP_ITERATIVE) rotation = face_info.rotation translation = face_info.translation pts_3d = np.zeros((70,3), np.float32) if not success: face_info.rotation = np.array([0.0, 0.0, 0.0], np.float32) face_info.translation = np.array([0.0, 0.0, 0.0], np.float32) return False, np.zeros(4), np.zeros(3), 99999., pts_3d, lms else: face_info.rotation = np.transpose(face_info.rotation) face_info.translation = np.transpose(face_info.translation) rmat, _ = cv2.Rodrigues(rotation) inverse_rotation = np.linalg.inv(rmat) pnp_error = 0.0 for i, pt in enumerate(face_info.face_3d): if i == 68: # Right eyeball # Eyeballs have an average diameter of 12.5mm and and the distance between eye corners is 30-35mm, so a conversion factor of 0.385 can be applied eye_center = (pts_3d[36] + pts_3d[39]) / 2.0 d_corner = np.linalg.norm(pts_3d[36] - pts_3d[39]) depth = 0.385 * d_corner pt_3d = np.array([eye_center[0], eye_center[1], eye_center[2] - depth]) pts_3d[i] = pt_3d continue if i == 69: # Left eyeball eye_center = (pts_3d[42] + pts_3d[45]) / 2.0 d_corner = np.linalg.norm(pts_3d[42] - pts_3d[45]) depth = 0.385 * d_corner pt_3d = np.array([eye_center[0], eye_center[1], eye_center[2] - depth]) pts_3d[i] = pt_3d continue if i == 66: d1 = np.linalg.norm(lms[i,0:2] - lms[36,0:2]) d2 = np.linalg.norm(lms[i,0:2] - lms[39,0:2]) d = d1 + d2 pt = (pts_3d[36] * d1 + pts_3d[39] * d2) / d if i == 67: d1 = np.linalg.norm(lms[i,0:2] - lms[42,0:2]) d2 = np.linalg.norm(lms[i,0:2] - lms[45,0:2]) d = d1 + d2 pt = (pts_3d[42] * d1 + pts_3d[45] * d2) / d reference = rmat.dot(pt) reference = reference + face_info.translation reference = self.camera.dot(reference) depth = reference[2] if i < 17 or i == 30: reference = reference / depth e1 = lms[i][0] - reference[0] e2 = lms[i][1] - reference[1] pnp_error += e1*e1 + e2*e2 pt_3d = np.array([lms[i][0] * depth, lms[i][1] * depth, depth], np.float32) pt_3d = self.inverse_camera.dot(pt_3d) pt_3d = pt_3d - face_info.translation pt_3d = inverse_rotation.dot(pt_3d) pts_3d[i,:] = pt_3d[:] pnp_error = np.sqrt(pnp_error / (2.0 * image_pts.shape[0])) if pnp_error > 300: self.fail_count += 1 if self.fail_count > 5: # Something went wrong with adjusting the 3D model if not self.silent: print(f"Detected anomaly when 3D fitting face {face_info.id}. Resetting.") face_info.face_3d = copy.copy(self.face_3d) face_info.rotation = None face_info.translation = np.array([0.0, 0.0, 0.0], np.float32) face_info.update_counts = np.zeros((66,2)) face_info.update_contour() else: self.fail_count = 0 euler = cv2.RQDecomp3x3(rmat)[0] return True, matrix_to_quaternion(rmat), euler, pnp_error, pts_3d, lms def preprocess(self, im, crop): x1, y1, x2, y2 = crop im = np.float32(im[y1:y2, x1:x2,::-1]) # Crop and BGR to RGB im = cv2.resize(im, (self.res_i, self.res_i), interpolation=cv2.INTER_LINEAR) / self.std - self.mean #im = cv2.resize(im, (224, 224), interpolation=cv2.INTER_LINEAR) / 255.0 #im = (im - mean) / std im = np.expand_dims(im, 0) im = np.transpose(im, (0,3,1,2)) if self.model_type == 0: im = im.mean(1) im = np.expand_dims(im, 1) return im def equalize(self, im): im_yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV) im_yuv[:,:,0] = cv2.equalizeHist(im_yuv[:,:,0]) return cv2.cvtColor(im_yuv, cv2.COLOR_YUV2BGR) def corners_to_eye(self, corners, w, h, flip): ((cx1, cy1), (cx2, cy2)) = corners c1 = np.array([cx1, cy1]) c2 = np.array([cx2, cy2]) c2, a = compensate(c1, c2) center = (c1 + c2) / 2.0 radius = np.linalg.norm(c1 - c2) / 2.0 radius = np.array([radius * 1.4, radius * 1.2]) upper_left = clamp_to_im(center - radius, w, h) lower_right = clamp_to_im(center + radius, w, h) return upper_left, lower_right, center, radius, c1, a def prepare_eye(self, frame, full_frame, lms, flip): outer_pt = tuple(lms[0]) inner_pt = tuple(lms[1]) h, w, _ = frame.shape (x1, y1), (x2, y2), center, radius, reference, a = self.corners_to_eye((outer_pt, inner_pt), w, h, flip) im = rotate_image(frame[:, :, ::], a, reference) im = im[int(y1):int(y2), int(x1):int(x2),:] if np.prod(im.shape) < 1: return None, None, None, None, None, None if flip: im = cv2.flip(im, 1) scale = np.array([(x2 - x1), (y2 - y1)]) / 32. im = cv2.resize(im, (32, 32), interpolation=cv2.INTER_LINEAR) #im = self.equalize(im) if self.debug_gaze: if not flip: full_frame[0:32, 0:32] = im else: full_frame[0:32, 32:64] = im im = im.astype(np.float32)[:,:,::-1] / self.std - self.mean im = np.expand_dims(im, 0) im = np.transpose(im, (0,3,2,1)) return im, x1, y1, scale, reference, a def extract_face(self, frame, lms): lms = np.array(lms)[:,0:2][:,::-1] x1, y1 = tuple(lms.min(0)) x2, y2 = tuple(lms.max(0)) radius_x = 1.2 * (x2 - x1) / 2.0 radius_y = 1.2 * (y2 - y1) / 2.0 radius = np.array((radius_x, radius_y)) center = (np.array((x1, y1)) + np.array((x2, y2))) / 2.0 w, h, _ = frame.shape x1, y1 = clamp_to_im(center - radius, h, w) x2, y2 = clamp_to_im(center + radius + 1, h, w) offset = np.array((x1, y1)) lms = (lms[:, 0:2] - offset).astype(np.int) frame = frame[y1:y2, x1:x2] return frame, lms, offset def get_eye_state(self, frame, lms): if self.no_gaze: return [(1.0, 0.0, 0.0, 0.0), (1.0, 0.0, 0.0, 0.0)] lms = np.array(lms) e_x = [0,0] e_y = [0,0] scale = [0,0] reference = [None, None] angles = [0, 0] face_frame, lms, offset = self.extract_face(frame, lms) (right_eye, e_x[0], e_y[0], scale[0], reference[0], angles[0]) = self.prepare_eye(face_frame, frame, np.array([lms[36,0:2], lms[39,0:2]]), False) (left_eye, e_x[1], e_y[1], scale[1], reference[1], angles[1]) = self.prepare_eye(face_frame, frame, np.array([lms[42,0:2], lms[45,0:2]]), True) if right_eye is None or left_eye is None: return [(1.0, 0.0, 0.0, 0.0), (1.0, 0.0, 0.0, 0.0)] both_eyes = np.concatenate((right_eye, left_eye)) results = self.gaze_model.run([], {self.input_name: both_eyes}) open = [0, 0] open[0] = 1#results[1][0].argmax() open[1] = 1#results[1][1].argmax() results = np.array(results[0]) eye_state = [] for i in range(2): m = int(results[i][0].argmax()) x = m // 8 y = m % 8 conf = float(results[i][0][x,y]) off_x = 32.0 * logit(results[i][1][x, y], 8.0) off_y = 32.0 * logit(results[i][2][x, y], 8.0) if i == 1: eye_x = 32.0 * float(x) / 8.0 + off_x else: eye_x = 32.0 * float(x) / 8.0 + off_x eye_y = 32.0 * float(y) / 8.0 + off_y if self.debug_gaze: if i == 0: frame[int(eye_y), int(eye_x)] = (0, 0, 255) frame[int(eye_y+1), int(eye_x)] = (0, 0, 255) frame[int(eye_y+1), int(eye_x+1)] = (0, 0, 255) frame[int(eye_y), int(eye_x+1)] = (0, 0, 255) else: frame[int(eye_y), 32+int(eye_x)] = (0, 0, 255) frame[int(eye_y+1), 32+int(eye_x)] = (0, 0, 255) frame[int(eye_y+1), 32+int(eye_x+1)] = (0, 0, 255) frame[int(eye_y), 32+int(eye_x+1)] = (0, 0, 255) if i == 0: eye_x = e_x[i] + scale[i][0] * eye_x else: eye_x = e_x[i] + scale[i][0] * (32. - eye_x) eye_y = e_y[i] + scale[i][1] * eye_y eye_x, eye_y = rotate(reference[i], (eye_x, eye_y), -angles[i]) eye_x = eye_x + offset[0] eye_y = eye_y + offset[1] eye_state.append([open[i], eye_y, eye_x, conf]) return eye_state def assign_face_info(self, results): result_coords = [] adjusted_results = [] for conf, (lms, eye_state), conf_adjust in results: adjusted_results.append((conf - conf_adjust, (lms, eye_state))) result_coords.append(np.array(lms)[:, 0:2].mean(0)) results = adjusted_results candidates = [[]] * self.max_faces max_dist = 2 * np.linalg.norm(np.array([self.width, self.height])) for i, face_info in enumerate(self.face_info): for j, coord in enumerate(result_coords): if face_info.coord is None: candidates[i].append((max_dist, i, j)) else: candidates[i].append((np.linalg.norm(face_info.coord - coord), i, j)) for i, candidate in enumerate(candidates): candidates[i] = sorted(candidate) found = 0 target = len(results) used_results = {} used_faces = {} while found < target: min_list = min(candidates) candidate = min_list.pop(0) face_idx = candidate[1] result_idx = candidate[2] if not result_idx in used_results and not face_idx in used_faces: self.face_info[face_idx].update(results[result_idx], result_coords[result_idx], self.frame_count) min_list.clear() used_results[result_idx] = True used_faces[face_idx] = True found += 1 if len(min_list) == 0: min_list.append((2 * max_dist, face_idx, result_idx)) for face_info in self.face_info: if face_info.frame_count != self.frame_count: face_info.update(None, None, self.frame_count) def predict(self, frame, additional_faces=[]): self.frame_count += 1 start = time.perf_counter() im = frame duration_fd = 0.0 duration_pp = 0.0 duration_model = 0.0 duration_pnp = 0.0 new_faces = [] new_faces.extend(self.faces) bonus_cutoff = len(self.faces) new_faces.extend(additional_faces) self.wait_count += 1 if self.detected == 0: start_fd = time.perf_counter() if self.use_retinaface > 0: retinaface_detections = self.retinaface.detect_retina(frame) new_faces.extend(retinaface_detections) else: new_faces.extend(self.detect_faces(frame)) duration_fd = 1000 * (time.perf_counter() - start_fd) self.wait_count = 0 elif self.detected < self.max_faces: if self.use_retinaface > 0: new_faces.extend(self.retinaface_scan.get_results()) if self.wait_count >= self.scan_every: if self.use_retinaface > 0: self.retinaface_scan.background_detect(frame) else: start_fd = time.perf_counter() new_faces.extend(self.detect_faces(frame)) duration_fd = 1000 * (time.perf_counter() - start_fd) self.wait_count = 0 else: self.wait_count = 0 if len(new_faces) < 1: duration = (time.perf_counter() - start) * 1000 if not self.silent: print(f"Took {duration:.2f}ms") return [] crops = [] crop_info = [] num_crops = 0 for j, (x,y,w,h) in enumerate(new_faces): crop_x1 = x - int(w * 0.1) crop_y1 = y - int(h * 0.125) crop_x2 = x + w + int(w * 0.1) crop_y2 = y + h + int(h * 0.125) crop_x1, crop_y1 = clamp_to_im((crop_x1, crop_y1), self.width, self.height) crop_x2, crop_y2 = clamp_to_im((crop_x2, crop_y2), self.width, self.height) scale_x = float(crop_x2 - crop_x1) / self.res scale_y = float(crop_y2 - crop_y1) / self.res if crop_x2 - crop_x1 < 4 or crop_y2 - crop_y1 < 4: continue start_pp = time.perf_counter() crop = self.preprocess(im, (crop_x1, crop_y1, crop_x2, crop_y2)) duration_pp += 1000 * (time.perf_counter() - start_pp) crops.append(crop) crop_info.append((crop_x1, crop_y1, scale_x, scale_y, 0.0 if j >= bonus_cutoff else 0.1)) num_crops += 1 start_model = time.perf_counter() outputs = {} if num_crops == 1: output = self.session.run([], {self.input_name: crops[0]})[0] conf, lms = self.landmarks(output[0], crop_info[0]) outputs[crop_info[0]] = (conf, lms, 0) else: started = 0 results = queue.Queue() for i in range(min(num_crops, self.max_workers)): thread = threading.Thread(target=worker_thread, args=(self.sessions[started], crops[started], crop_info[started], results, self.input_name, started, self)) started += 1 thread.start() returned = 0 while returned < num_crops: session, conf, lms, sample_crop_info, idx = results.get(True) outputs[sample_crop_info] = (conf, lms, idx) returned += 1 if started < num_crops: thread = threading.Thread(target=worker_thread, args=(session, crops[started], crop_info[started], results, self.input_name, started, self)) started += 1 thread.start() actual_faces = [] good_crops = [] for crop in crop_info: conf, lms, i = outputs[crop] if conf <= self.threshold: continue x1, y1, _ = lms.min(0) x2, y2, _ = lms.max(0) bb = (x1, y1, x2 - x1, y2 - y1) outputs[crop] = (conf, lms, i, bb) actual_faces.append(bb) good_crops.append(crop) groups = group_rects(actual_faces) best_results = {} for crop in good_crops: conf, lms, i, bb = outputs[crop] if conf < self.threshold: continue; eye_state = self.get_eye_state(frame, lms) group_id = groups[str(bb)][0] if not group_id in best_results: best_results[group_id] = [-1, [], 0] if conf > self.threshold and best_results[group_id][0] < conf + crop[4]: best_results[group_id][0] = conf + crop[4] best_results[group_id][1] = (lms, eye_state) best_results[group_id][2] = crop[4] sorted_results = sorted(best_results.values(), key=lambda x: x[0], reverse=True)[:self.max_faces] self.assign_face_info(sorted_results) duration_model = 1000 * (time.perf_counter() - start_model) results = [] detected = [] for face_info in self.face_info: if face_info.alive and face_info.conf > self.threshold: start_pnp = time.perf_counter() face_info.success, face_info.quaternion, face_info.euler, face_info.pnp_error, face_info.pts_3d, face_info.lms = self.estimate_depth(face_info) face_info.adjust_3d() duration_pnp += 1000 * (time.perf_counter() - start_pnp) min_x = 10000 max_x = -1 min_y = 10000 max_y = -1 for (x,y,c) in face_info.lms[0:66]: if x > max_x: max_x = x if y > max_y: max_y = y if x < min_x: min_x = x if y < min_y: min_y = y w = max_x - min_x y = max_y - min_y bbox = (min_y, min_x, max_y - min_y, max_x - min_x) detected.append(bbox) face_info.bbox = bbox #face_info.eye_blink = (eye_state[0][0], eye_state[1][0]) results.append(face_info) if len(detected) > 0: self.detected = len(detected) self.faces = detected self.discard = 0 else: self.detected = 0 self.discard += 1 if self.discard > self.discard_after: self.faces = [] else: if self.bbox_growth > 0: faces = [] for (x,y,w,h) in self.faces: x -= w * self.bbox_growth y -= h * self.bbox_growth w += 2 * w * self.bbox_growth h += 2 * h * self.bbox_growth faces.append((x,y,w,h)) self.faces = faces duration = (time.perf_counter() - start) * 1000 if not self.silent: print(f"Took {duration:.2f}ms (detect: {duration_fd:.2f}ms, crop: {duration_pp:.2f}, track: {duration_model:.2f}ms, 3D points: {duration_pnp:.2f}ms)") results = sorted(results, key=lambda x: x.id) return results