| import cv2
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| import numpy as np
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| from skimage import transform as trans
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|
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| def get_keypts(face):
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|
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| if len(face['keypoints']) == 0:
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| return []
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|
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| leye = np.array(face['keypoints']['left_eye'], dtype=np.int).reshape(-1, 2)
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| reye = np.array(face['keypoints']['right_eye'],
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| dtype=np.int).reshape(-1, 2)
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| nose = np.array(face['keypoints']['nose'], dtype=np.int).reshape(-1, 2)
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| lmouth = np.array(face['keypoints']['mouth_left'],
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| dtype=np.int).reshape(-1, 2)
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| rmouth = np.array(face['keypoints']['mouth_right'],
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| dtype=np.int).reshape(-1, 2)
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|
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| pts = np.concatenate([leye, reye, nose, lmouth, rmouth], axis=0)
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|
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| return pts
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|
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| def img_align_crop(img, landmark=None, outsize=None, scale=1.3, mask=None):
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| """ align and crop the face according to the given bbox and landmarks
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| landmark: 5 key points
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| """
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|
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| M = None
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| target_size = [112, 112]
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|
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| dst = np.array([
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| [30.2946, 51.6963],
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| [65.5318, 51.5014],
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| [48.0252, 71.7366],
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| [33.5493, 92.3655],
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| [62.7299, 92.2041]], dtype=np.float32)
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|
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| if target_size[1] == 112:
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| dst[:, 0] += 8.0
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|
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| dst[:, 0] = dst[:, 0] * outsize[0] / target_size[0]
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| dst[:, 1] = dst[:, 1] * outsize[1] / target_size[1]
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|
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| target_size = outsize
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|
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| margin_rate = scale - 1
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| x_margin = target_size[0] * margin_rate / 2.
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| y_margin = target_size[1] * margin_rate / 2.
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| dst[:, 0] += x_margin
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| dst[:, 1] += y_margin
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| dst[:, 0] *= target_size[0] / (target_size[0] + 2 * x_margin)
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| dst[:, 1] *= target_size[1] / (target_size[1] + 2 * y_margin)
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|
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| src = landmark.astype(np.float32)
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| tform = trans.SimilarityTransform()
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| tform.estimate(src, dst)
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| M = tform.params[0:2, :]
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| img = cv2.warpAffine(img, M, (target_size[1], target_size[0]))
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|
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| if outsize is not None:
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| img = cv2.resize(img, (outsize[1], outsize[0]))
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|
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| if mask is not None:
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| mask = cv2.warpAffine(mask, M, (target_size[1], target_size[0]))
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| mask = cv2.resize(mask, (outsize[1], outsize[0]))
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| return img, mask
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| else:
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| return img
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|
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|
|
| def expand_bbox(bbox, width, height, scale=1.3, minsize=None):
|
| """
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| Expand original boundingbox by scale.
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| :param bbx: original boundingbox
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| :param width: frame width
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| :param height: frame height
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| :param scale: bounding box size multiplier to get a bigger face region
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| :param minsize: set minimum bounding box size
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| :return: expanded bbox
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| """
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| x, y, w, h = bbox
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|
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| cx = int(x + w / 2)
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| cy = int(y + h / 2)
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| new_size = max(int(w * scale), int(h * scale))
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| new_x = max(0, int(cx - new_size / 2))
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| new_y = max(0, int(cy - new_size / 2))
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| new_size = min(width - new_x, new_size)
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| new_size = min(height - new_size, new_size)
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|
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| return new_x, new_y, new_size, new_size
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|
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|
|
| def extract_face_MTCNN(face_detector, image, expand_scale=1.3, res=256):
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|
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| height, width = image.shape[:2]
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|
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| rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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|
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| faces = face_detector.detect_faces(rgb)
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| if len(faces):
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|
|
| face = None
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| bbox = None
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| max_region = 0
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| for ff in faces:
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| if max_region == 0:
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| face = ff
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| bbox = face['box']
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| max_region = bbox[2]*bbox[3]
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| else:
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| bb = ff['box']
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| region = bb[2]*bb[3]
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| if region > max_rigion:
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| max_rigion = region
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| face = ff
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| bbox = face['box']
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| print(max_region)
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|
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| x, y, w, h = expand_bbox(bbox, width, height, scale=expand_scale)
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| cropped_face = rgb[y:y+h, x:x+w]
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|
|
| cropped_face = cv2.resize(
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| cropped_face, (res, res), interpolation=cv2.INTER_CUBIC)
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| cropped_face = cv2.cvtColor(cropped_face, cv2.COLOR_RGB2BGR)
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| return cropped_face
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|
|
| return None
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|
|
|
|
| def extract_aligned_face_MTCNN(face_detector, image, expand_scale=1.3, res=256, mask=None):
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|
|
| height, width = image.shape[:2]
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|
|
|
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| rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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|
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|
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| faces = face_detector.detect_faces(rgb)
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| if len(faces):
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|
|
| face = None
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| bbox = None
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| max_region = 0
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| for i, ff in enumerate(faces):
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| if max_region == 0:
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| face = ff
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| bbox = face['box']
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| max_region = bbox[2]*bbox[3]
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| else:
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| bb = ff['box']
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| region = bb[2]*bb[3]
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| if region > max_region:
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| max_region = region
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| face = ff
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| bbox = face['box']
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|
|
|
|
|
|
| landmarks = get_keypts(face)
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|
|
|
|
|
|
| if mask is not None:
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| cropped_face, cropped_mask = img_align_crop(rgb, landmarks, outsize=[
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| res, res], scale=expand_scale, mask=mask)
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| cropped_face = cv2.cvtColor(cropped_face, cv2.COLOR_RGB2BGR)
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| cropped_mask = cv2.cvtColor(cropped_mask, cv2.COLOR_RGB2GRAY)
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| return cropped_face, cropped_mask
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| else:
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| cropped_face = img_align_crop(rgb, landmarks, outsize=[
|
| res, res], scale=expand_scale)
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| cropped_face = cv2.cvtColor(cropped_face, cv2.COLOR_RGB2BGR)
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| return cropped_face
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|
|
| return None
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|
|
|
|
| def extract_face_DLIB(face_detector, image, expand_scale=1.3, res=256):
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|
|
| height, width = image.shape[:2]
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|
|
|
|
| gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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|
|
|
|
| faces = face_detector(gray, 1)
|
| if len(faces):
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|
|
| face = faces[0]
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|
|
| x1 = face.left()
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| y1 = face.top()
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| x2 = face.right()
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| y2 = face.bottom()
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| bbox = (x1, y1, x2-x1, y2-y1)
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|
|
|
|
|
|
| x, y, w, h = expand_bbox(bbox, width, height, scale=expand_scale)
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| cropped_face = image[y:y+h, x:x+w]
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|
|
| cropped_face = cv2.resize(
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| cropped_face, (res, res), interpolation=cv2.INTER_CUBIC)
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|
|
| return cropped_face
|
|
|
| return None
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|
|
