import numpy as np from PIL import Image import math def findEuclideanDistance(source_representation, test_representation): euclidean_distance = source_representation - test_representation euclidean_distance = np.sum(np.multiply(euclidean_distance, euclidean_distance)) euclidean_distance = np.sqrt(euclidean_distance) return euclidean_distance #this function copied from the deepface repository: https://github.com/serengil/deepface/blob/master/deepface/commons/functions.py def alignment_procedure(img, left_eye, right_eye, nose): #this function aligns given face in img based on left and right eye coordinates left_eye_x, left_eye_y = left_eye right_eye_x, right_eye_y = right_eye #----------------------- upside_down = False if nose[1] < left_eye[1] or nose[1] < right_eye[1]: upside_down = True #----------------------- #find rotation direction if left_eye_y > right_eye_y: point_3rd = (right_eye_x, left_eye_y) direction = -1 #rotate same direction to clock else: point_3rd = (left_eye_x, right_eye_y) direction = 1 #rotate inverse direction of clock #----------------------- #find length of triangle edges a = findEuclideanDistance(np.array(left_eye), np.array(point_3rd)) b = findEuclideanDistance(np.array(right_eye), np.array(point_3rd)) c = findEuclideanDistance(np.array(right_eye), np.array(left_eye)) #----------------------- #apply cosine rule if b != 0 and c != 0: #this multiplication causes division by zero in cos_a calculation cos_a = (b*b + c*c - a*a)/(2*b*c) #PR15: While mathematically cos_a must be within the closed range [-1.0, 1.0], floating point errors would produce cases violating this #In fact, we did come across a case where cos_a took the value 1.0000000169176173, which lead to a NaN from the following np.arccos step cos_a = min(1.0, max(-1.0, cos_a)) angle = np.arccos(cos_a) #angle in radian angle = (angle * 180) / math.pi #radian to degree #----------------------- #rotate base image if direction == -1: angle = 90 - angle if upside_down == True: angle = angle + 90 img = Image.fromarray(img) img = np.array(img.rotate(direction * angle)) #----------------------- return img #return img anyway #this function is copied from the following code snippet: https://github.com/StanislasBertrand/RetinaFace-tf2/blob/master/retinaface.py def bbox_pred(boxes, box_deltas): if boxes.shape[0] == 0: return np.zeros((0, box_deltas.shape[1])) boxes = boxes.astype(np.float, copy=False) widths = boxes[:, 2] - boxes[:, 0] + 1.0 heights = boxes[:, 3] - boxes[:, 1] + 1.0 ctr_x = boxes[:, 0] + 0.5 * (widths - 1.0) ctr_y = boxes[:, 1] + 0.5 * (heights - 1.0) dx = box_deltas[:, 0:1] dy = box_deltas[:, 1:2] dw = box_deltas[:, 2:3] dh = box_deltas[:, 3:4] pred_ctr_x = dx * widths[:, np.newaxis] + ctr_x[:, np.newaxis] pred_ctr_y = dy * heights[:, np.newaxis] + ctr_y[:, np.newaxis] pred_w = np.exp(dw) * widths[:, np.newaxis] pred_h = np.exp(dh) * heights[:, np.newaxis] pred_boxes = np.zeros(box_deltas.shape) # x1 pred_boxes[:, 0:1] = pred_ctr_x - 0.5 * (pred_w - 1.0) # y1 pred_boxes[:, 1:2] = pred_ctr_y - 0.5 * (pred_h - 1.0) # x2 pred_boxes[:, 2:3] = pred_ctr_x + 0.5 * (pred_w - 1.0) # y2 pred_boxes[:, 3:4] = pred_ctr_y + 0.5 * (pred_h - 1.0) if box_deltas.shape[1]>4: pred_boxes[:,4:] = box_deltas[:,4:] return pred_boxes # This function copied from the following code snippet: https://github.com/StanislasBertrand/RetinaFace-tf2/blob/master/retinaface.py def landmark_pred(boxes, landmark_deltas): if boxes.shape[0] == 0: return np.zeros((0, landmark_deltas.shape[1])) boxes = boxes.astype(np.float, copy=False) widths = boxes[:, 2] - boxes[:, 0] + 1.0 heights = boxes[:, 3] - boxes[:, 1] + 1.0 ctr_x = boxes[:, 0] + 0.5 * (widths - 1.0) ctr_y = boxes[:, 1] + 0.5 * (heights - 1.0) pred = landmark_deltas.copy() for i in range(5): pred[:,i,0] = landmark_deltas[:,i,0]*widths + ctr_x pred[:,i,1] = landmark_deltas[:,i,1]*heights + ctr_y return pred # This function copied from rcnn module of retinaface-tf2 project: https://github.com/StanislasBertrand/RetinaFace-tf2/blob/master/rcnn/processing/bbox_transform.py def clip_boxes(boxes, im_shape): # x1 >= 0 boxes[:, 0::4] = np.maximum(np.minimum(boxes[:, 0::4], im_shape[1] - 1), 0) # y1 >= 0 boxes[:, 1::4] = np.maximum(np.minimum(boxes[:, 1::4], im_shape[0] - 1), 0) # x2 < im_shape[1] boxes[:, 2::4] = np.maximum(np.minimum(boxes[:, 2::4], im_shape[1] - 1), 0) # y2 < im_shape[0] boxes[:, 3::4] = np.maximum(np.minimum(boxes[:, 3::4], im_shape[0] - 1), 0) return boxes #this function is mainly based on the following code snippet: https://github.com/StanislasBertrand/RetinaFace-tf2/blob/master/rcnn/cython/anchors.pyx def anchors_plane(height, width, stride, base_anchors): A = base_anchors.shape[0] c_0_2 = np.tile(np.arange(0, width)[np.newaxis, :, np.newaxis, np.newaxis], (height, 1, A, 1)) c_1_3 = np.tile(np.arange(0, height)[:, np.newaxis, np.newaxis, np.newaxis], (1, width, A, 1)) all_anchors = np.concatenate([c_0_2, c_1_3, c_0_2, c_1_3], axis=-1) * stride + np.tile(base_anchors[np.newaxis, np.newaxis, :, :], (height, width, 1, 1)) return all_anchors #this function is mainly based on the following code snippet: https://github.com/StanislasBertrand/RetinaFace-tf2/blob/master/rcnn/cython/cpu_nms.pyx #Fast R-CNN by Ross Girshick def cpu_nms(dets, threshold): x1 = dets[:, 0] y1 = dets[:, 1] x2 = dets[:, 2] y2 = dets[:, 3] scores = dets[:, 4] areas = (x2 - x1 + 1) * (y2 - y1 + 1) order = scores.argsort()[::-1] ndets = dets.shape[0] suppressed = np.zeros((ndets), dtype=np.int) keep = [] for _i in range(ndets): i = order[_i] if suppressed[i] == 1: continue keep.append(i) ix1 = x1[i]; iy1 = y1[i]; ix2 = x2[i]; iy2 = y2[i] iarea = areas[i] for _j in range(_i + 1, ndets): j = order[_j] if suppressed[j] == 1: continue xx1 = max(ix1, x1[j]); yy1 = max(iy1, y1[j]); xx2 = min(ix2, x2[j]); yy2 = min(iy2, y2[j]) w = max(0.0, xx2 - xx1 + 1); h = max(0.0, yy2 - yy1 + 1) inter = w * h ovr = inter / (iarea + areas[j] - inter) if ovr >= threshold: suppressed[j] = 1 return keep