import cv2 import numpy as np import random from face_detect.utils.box_utils import matrix_iof def _crop(image, boxes, labels, landm, img_dim): height, width, _ = image.shape pad_image_flag = True for _ in range(250): """ if random.uniform(0, 1) <= 0.2: scale = 1.0 else: scale = random.uniform(0.3, 1.0) """ PRE_SCALES = [0.3, 0.45, 0.6, 0.8, 1.0] scale = random.choice(PRE_SCALES) short_side = min(width, height) w = int(scale * short_side) h = w if width == w: l = 0 else: l = random.randrange(width - w) if height == h: t = 0 else: t = random.randrange(height - h) roi = np.array((l, t, l + w, t + h)) value = matrix_iof(boxes, roi[np.newaxis]) flag = (value >= 1) if not flag.any(): continue centers = (boxes[:, :2] + boxes[:, 2:]) / 2 mask_a = np.logical_and(roi[:2] < centers, centers < roi[2:]).all(axis=1) boxes_t = boxes[mask_a].copy() labels_t = labels[mask_a].copy() landms_t = landm[mask_a].copy() landms_t = landms_t.reshape([-1, 5, 2]) if boxes_t.shape[0] == 0: continue image_t = image[roi[1]:roi[3], roi[0]:roi[2]] boxes_t[:, :2] = np.maximum(boxes_t[:, :2], roi[:2]) boxes_t[:, :2] -= roi[:2] boxes_t[:, 2:] = np.minimum(boxes_t[:, 2:], roi[2:]) boxes_t[:, 2:] -= roi[:2] # landm landms_t[:, :, :2] = landms_t[:, :, :2] - roi[:2] landms_t[:, :, :2] = np.maximum(landms_t[:, :, :2], np.array([0, 0])) landms_t[:, :, :2] = np.minimum(landms_t[:, :, :2], roi[2:] - roi[:2]) landms_t = landms_t.reshape([-1, 10]) # make sure that the cropped image contains at least one face > 16 pixel at training image scale b_w_t = (boxes_t[:, 2] - boxes_t[:, 0] + 1) / w * img_dim b_h_t = (boxes_t[:, 3] - boxes_t[:, 1] + 1) / h * img_dim mask_b = np.minimum(b_w_t, b_h_t) > 0.0 boxes_t = boxes_t[mask_b] labels_t = labels_t[mask_b] landms_t = landms_t[mask_b] if boxes_t.shape[0] == 0: continue pad_image_flag = False return image_t, boxes_t, labels_t, landms_t, pad_image_flag return image, boxes, labels, landm, pad_image_flag def _distort(image): def _convert(image, alpha=1, beta=0): tmp = image.astype(float) * alpha + beta tmp[tmp < 0] = 0 tmp[tmp > 255] = 255 image[:] = tmp image = image.copy() if random.randrange(2): #brightness distortion if random.randrange(2): _convert(image, beta=random.uniform(-32, 32)) #contrast distortion if random.randrange(2): _convert(image, alpha=random.uniform(0.5, 1.5)) image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) #saturation distortion if random.randrange(2): _convert(image[:, :, 1], alpha=random.uniform(0.5, 1.5)) #hue distortion if random.randrange(2): tmp = image[:, :, 0].astype(int) + random.randint(-18, 18) tmp %= 180 image[:, :, 0] = tmp image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR) else: #brightness distortion if random.randrange(2): _convert(image, beta=random.uniform(-32, 32)) image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) #saturation distortion if random.randrange(2): _convert(image[:, :, 1], alpha=random.uniform(0.5, 1.5)) #hue distortion if random.randrange(2): tmp = image[:, :, 0].astype(int) + random.randint(-18, 18) tmp %= 180 image[:, :, 0] = tmp image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR) #contrast distortion if random.randrange(2): _convert(image, alpha=random.uniform(0.5, 1.5)) return image def _expand(image, boxes, fill, p): if random.randrange(2): return image, boxes height, width, depth = image.shape scale = random.uniform(1, p) w = int(scale * width) h = int(scale * height) left = random.randint(0, w - width) top = random.randint(0, h - height) boxes_t = boxes.copy() boxes_t[:, :2] += (left, top) boxes_t[:, 2:] += (left, top) expand_image = np.empty( (h, w, depth), dtype=image.dtype) expand_image[:, :] = fill expand_image[top:top + height, left:left + width] = image image = expand_image return image, boxes_t def _mirror(image, boxes, landms): _, width, _ = image.shape if random.randrange(2): image = image[:, ::-1] boxes = boxes.copy() boxes[:, 0::2] = width - boxes[:, 2::-2] # landm landms = landms.copy() landms = landms.reshape([-1, 5, 2]) landms[:, :, 0] = width - landms[:, :, 0] tmp = landms[:, 1, :].copy() landms[:, 1, :] = landms[:, 0, :] landms[:, 0, :] = tmp tmp1 = landms[:, 4, :].copy() landms[:, 4, :] = landms[:, 3, :] landms[:, 3, :] = tmp1 landms = landms.reshape([-1, 10]) return image, boxes, landms def _pad_to_square(image, rgb_mean, pad_image_flag): if not pad_image_flag: return image height, width, _ = image.shape long_side = max(width, height) image_t = np.empty((long_side, long_side, 3), dtype=image.dtype) image_t[:, :] = rgb_mean image_t[0:0 + height, 0:0 + width] = image return image_t def _resize_subtract_mean(image, insize, rgb_mean): interp_methods = [cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_NEAREST, cv2.INTER_LANCZOS4] interp_method = interp_methods[random.randrange(5)] image = cv2.resize(image, (insize, insize), interpolation=interp_method) image = image.astype(np.float32) image -= rgb_mean return image.transpose(2, 0, 1) class preproc(object): def __init__(self, img_dim, rgb_means): self.img_dim = img_dim self.rgb_means = rgb_means def __call__(self, image, targets): assert targets.shape[0] > 0, "this image does not have gt" boxes = targets[:, :4].copy() labels = targets[:, -1].copy() landm = targets[:, 4:-1].copy() image_t, boxes_t, labels_t, landm_t, pad_image_flag = _crop(image, boxes, labels, landm, self.img_dim) image_t = _distort(image_t) image_t = _pad_to_square(image_t,self.rgb_means, pad_image_flag) image_t, boxes_t, landm_t = _mirror(image_t, boxes_t, landm_t) height, width, _ = image_t.shape image_t = _resize_subtract_mean(image_t, self.img_dim, self.rgb_means) boxes_t[:, 0::2] /= width boxes_t[:, 1::2] /= height landm_t[:, 0::2] /= width landm_t[:, 1::2] /= height labels_t = np.expand_dims(labels_t, 1) targets_t = np.hstack((boxes_t, landm_t, labels_t)) return image_t, targets_t