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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import cv2 | |
| import numpy as np | |
| def apply_bugeye_effect(img, | |
| pose_results, | |
| left_eye_index, | |
| right_eye_index, | |
| kpt_thr=0.5): | |
| """Apply bug-eye effect. | |
| Args: | |
| img (np.ndarray): Image data. | |
| pose_results (list[dict]): The pose estimation results containing: | |
| - "bbox" ([K, 4(or 5)]): detection bbox in | |
| [x1, y1, x2, y2, (score)] | |
| - "keypoints" ([K,3]): keypoint detection result in [x, y, score] | |
| left_eye_index (int): Keypoint index of left eye | |
| right_eye_index (int): Keypoint index of right eye | |
| kpt_thr (float): The score threshold of required keypoints. | |
| """ | |
| xx, yy = np.meshgrid(np.arange(img.shape[1]), np.arange(img.shape[0])) | |
| xx = xx.astype(np.float32) | |
| yy = yy.astype(np.float32) | |
| for pose in pose_results: | |
| bbox = pose['bbox'] | |
| kpts = pose['keypoints'] | |
| if kpts[left_eye_index, 2] < kpt_thr or kpts[right_eye_index, | |
| 2] < kpt_thr: | |
| continue | |
| kpt_leye = kpts[left_eye_index, :2] | |
| kpt_reye = kpts[right_eye_index, :2] | |
| for xc, yc in [kpt_leye, kpt_reye]: | |
| # distortion parameters | |
| k1 = 0.001 | |
| epe = 1e-5 | |
| scale = (bbox[2] - bbox[0])**2 + (bbox[3] - bbox[1])**2 | |
| r2 = ((xx - xc)**2 + (yy - yc)**2) | |
| r2 = (r2 + epe) / scale # normalized by bbox scale | |
| xx = (xx - xc) / (1 + k1 / r2) + xc | |
| yy = (yy - yc) / (1 + k1 / r2) + yc | |
| img = cv2.remap( | |
| img, | |
| xx, | |
| yy, | |
| interpolation=cv2.INTER_AREA, | |
| borderMode=cv2.BORDER_REPLICATE) | |
| return img | |
| def apply_sunglasses_effect(img, | |
| pose_results, | |
| sunglasses_img, | |
| left_eye_index, | |
| right_eye_index, | |
| kpt_thr=0.5): | |
| """Apply sunglasses effect. | |
| Args: | |
| img (np.ndarray): Image data. | |
| pose_results (list[dict]): The pose estimation results containing: | |
| - "keypoints" ([K,3]): keypoint detection result in [x, y, score] | |
| sunglasses_img (np.ndarray): Sunglasses image with white background. | |
| left_eye_index (int): Keypoint index of left eye | |
| right_eye_index (int): Keypoint index of right eye | |
| kpt_thr (float): The score threshold of required keypoints. | |
| """ | |
| hm, wm = sunglasses_img.shape[:2] | |
| # anchor points in the sunglasses mask | |
| pts_src = np.array([[0.3 * wm, 0.3 * hm], [0.3 * wm, 0.7 * hm], | |
| [0.7 * wm, 0.3 * hm], [0.7 * wm, 0.7 * hm]], | |
| dtype=np.float32) | |
| for pose in pose_results: | |
| kpts = pose['keypoints'] | |
| if kpts[left_eye_index, 2] < kpt_thr or kpts[right_eye_index, | |
| 2] < kpt_thr: | |
| continue | |
| kpt_leye = kpts[left_eye_index, :2] | |
| kpt_reye = kpts[right_eye_index, :2] | |
| # orthogonal vector to the left-to-right eyes | |
| vo = 0.5 * (kpt_reye - kpt_leye)[::-1] * [-1, 1] | |
| # anchor points in the image by eye positions | |
| pts_tar = np.vstack( | |
| [kpt_reye + vo, kpt_reye - vo, kpt_leye + vo, kpt_leye - vo]) | |
| h_mat, _ = cv2.findHomography(pts_src, pts_tar) | |
| patch = cv2.warpPerspective( | |
| sunglasses_img, | |
| h_mat, | |
| dsize=(img.shape[1], img.shape[0]), | |
| borderValue=(255, 255, 255)) | |
| # mask the white background area in the patch with a threshold 200 | |
| mask = cv2.cvtColor(patch, cv2.COLOR_BGR2GRAY) | |
| mask = (mask < 200).astype(np.uint8) | |
| img = cv2.copyTo(patch, mask, img) | |
| return img | |