| import cv2
|
| import numpy as np
|
| import random
|
| from utils.box_utils import matrix_iof
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|
|
|
|
| def _crop(image, boxes, labels, landm, img_dim):
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| height, width, _ = image.shape
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| pad_image_flag = True
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|
|
| for _ in range(250):
|
| """
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| if random.uniform(0, 1) <= 0.2:
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| scale = 1.0
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| else:
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| scale = random.uniform(0.3, 1.0)
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| """
|
| PRE_SCALES = [0.3, 0.45, 0.6, 0.8, 1.0]
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| scale = random.choice(PRE_SCALES)
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| short_side = min(width, height)
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| w = int(scale * short_side)
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| h = w
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|
|
| if width == w:
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| l = 0
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| else:
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| l = random.randrange(width - w)
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| if height == h:
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| t = 0
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| else:
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| t = random.randrange(height - h)
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| roi = np.array((l, t, l + w, t + h))
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|
|
| value = matrix_iof(boxes, roi[np.newaxis])
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| flag = (value >= 1)
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| if not flag.any():
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| continue
|
|
|
| centers = (boxes[:, :2] + boxes[:, 2:]) / 2
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| mask_a = np.logical_and(roi[:2] < centers, centers < roi[2:]).all(axis=1)
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| boxes_t = boxes[mask_a].copy()
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| labels_t = labels[mask_a].copy()
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| landms_t = landm[mask_a].copy()
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| landms_t = landms_t.reshape([-1, 5, 2])
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|
|
| if boxes_t.shape[0] == 0:
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| continue
|
|
|
| image_t = image[roi[1]:roi[3], roi[0]:roi[2]]
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|
|
| boxes_t[:, :2] = np.maximum(boxes_t[:, :2], roi[:2])
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| boxes_t[:, :2] -= roi[:2]
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| boxes_t[:, 2:] = np.minimum(boxes_t[:, 2:], roi[2:])
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| boxes_t[:, 2:] -= roi[:2]
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|
|
|
|
| landms_t[:, :, :2] = landms_t[:, :, :2] - roi[:2]
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| landms_t[:, :, :2] = np.maximum(landms_t[:, :, :2], np.array([0, 0]))
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| landms_t[:, :, :2] = np.minimum(landms_t[:, :, :2], roi[2:] - roi[:2])
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| landms_t = landms_t.reshape([-1, 10])
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|
|
|
|
|
|
| b_w_t = (boxes_t[:, 2] - boxes_t[:, 0] + 1) / w * img_dim
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| b_h_t = (boxes_t[:, 3] - boxes_t[:, 1] + 1) / h * img_dim
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| mask_b = np.minimum(b_w_t, b_h_t) > 0.0
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| boxes_t = boxes_t[mask_b]
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| labels_t = labels_t[mask_b]
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| landms_t = landms_t[mask_b]
|
|
|
| if boxes_t.shape[0] == 0:
|
| continue
|
|
|
| pad_image_flag = False
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|
|
| return image_t, boxes_t, labels_t, landms_t, pad_image_flag
|
| return image, boxes, labels, landm, pad_image_flag
|
|
|
|
|
| def _distort(image):
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|
|
| def _convert(image, alpha=1, beta=0):
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| tmp = image.astype(float) * alpha + beta
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| tmp[tmp < 0] = 0
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| tmp[tmp > 255] = 255
|
| image[:] = tmp
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|
|
| image = image.copy()
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|
|
| if random.randrange(2):
|
|
|
|
|
| if random.randrange(2):
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| _convert(image, beta=random.uniform(-32, 32))
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|
|
|
|
| if random.randrange(2):
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| _convert(image, alpha=random.uniform(0.5, 1.5))
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|
|
| image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
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|
|
|
|
| if random.randrange(2):
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| _convert(image[:, :, 1], alpha=random.uniform(0.5, 1.5))
|
|
|
|
|
| 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)
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|
|
| else:
|
|
|
|
|
| if random.randrange(2):
|
| _convert(image, beta=random.uniform(-32, 32))
|
|
|
| image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
|
|
|
|
|
| if random.randrange(2):
|
| _convert(image[:, :, 1], alpha=random.uniform(0.5, 1.5))
|
|
|
|
|
| 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)
|
|
|
|
|
| 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]
|
|
|
|
|
| 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
|
|
|
