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| import cv2 # OpenCV 라이브러리 | |
| import numpy as np | |
| from skimage import transform as tf # 이미지 변환 모듈 | |
| # -- Landmark interpolation: | |
| def linear_interpolate(landmarks, start_idx, stop_idx): | |
| start_landmarks = landmarks[start_idx] # 랜드마크 시작 | |
| stop_landmarks = landmarks[stop_idx] # 랜드마크 끝 | |
| delta = stop_landmarks - start_landmarks # 랜드마크 값 차이 | |
| for idx in range(1, stop_idx-start_idx): | |
| landmarks[start_idx+idx] = start_landmarks + idx/float(stop_idx-start_idx) * delta # 랜드마크 업데이트(보간) | |
| return landmarks | |
| # -- Face Transformation | |
| # src: 입력 영상, dst: 출력/결과 영상 | |
| def warp_img(src, dst, img, std_size): | |
| tform = tf.estimate_transform('similarity', src, dst) # find the transformation matrix # 변환 행렬 구하기 | |
| warped = tf.warp(img, inverse_map=tform.inverse, output_shape=std_size) # wrap the frame image # 주어진 좌표 변환에 따라 프레임 이미지 왜곡 | |
| warped = warped * 255 # note output from wrap is double image (value range [0,1]) | |
| warped = warped.astype('uint8') # numpy 데이터 타입 uint8 으로 변경 | |
| return warped, tform | |
| def apply_transform(transform, img, std_size): | |
| warped = tf.warp(img, inverse_map=transform.inverse, output_shape=std_size) # wrap the frame image # 주어진 좌표 변환에 따라 프레임 이미지 왜곡 | |
| warped = warped * 255 # note output from wrap is double image (value range [0,1]) | |
| warped = warped.astype('uint8') # numpy 데이터 타입 uint8 으로 변경 | |
| return warped | |
| # -- Crop | |
| def cut_patch(img, landmarks, height, width, threshold=5): | |
| center_x, center_y = np.mean(landmarks, axis=0) # 각 그룹의 같은 원소끼리 평균 | |
| # 좌표 처리 | |
| if center_y - height < 0: | |
| center_y = height | |
| if center_y - height < 0 - threshold: | |
| raise Exception('too much bias in height') | |
| if center_x - width < 0: | |
| center_x = width | |
| if center_x - width < 0 - threshold: | |
| raise Exception('too much bias in width') | |
| if center_y + height > img.shape[0]: | |
| center_y = img.shape[0] - height | |
| if center_y + height > img.shape[0] + threshold: | |
| raise Exception('too much bias in height') | |
| if center_x + width > img.shape[1]: | |
| center_x = img.shape[1] - width | |
| if center_x + width > img.shape[1] + threshold: | |
| raise Exception('too much bias in width') | |
| # 배열 복사 | |
| cutted_img = np.copy(img[ int(round(center_y) - round(height)): int(round(center_y) + round(height)), | |
| int(round(center_x) - round(width)): int(round(center_x) + round(width))]) | |
| return cutted_img | |
| # -- RGB to GRAY | |
| def convert_bgr2gray(data): | |
| # np.stack(배열_1, 배열_2, axis=0): 지정한 axis를 완전히 새로운 axis로 생각 | |
| return np.stack([cv2.cvtColor(_, cv2.COLOR_BGR2GRAY) for _ in data], axis=0) # gray 변환 | |