Spaces:
Paused
Paused
| # -*- coding: utf-8 -*- | |
| """ | |
| # File name: crop.py | |
| # Time : 2021/9/30 21:20 | |
| # Author: xyguoo@163.com | |
| # Description: | |
| """ | |
| import os | |
| import PIL.Image | |
| import PIL.ImageFile | |
| import numpy as np | |
| import scipy.ndimage | |
| import cv2 | |
| PIL.ImageFile.LOAD_TRUNCATED_IMAGES = True # avoid "Decompressed Data Too Large" error | |
| def recreate_aligned_images(img, lm_68, output_size=1024, transform_size=4096, enable_padding=True): | |
| pil_img = PIL.Image.fromarray(img) | |
| lm_chin = lm_68[0: 17] # left-right | |
| lm_eyebrow_left = lm_68[17: 22] # left-right | |
| lm_eyebrow_right = lm_68[22: 27] # left-right | |
| lm_nose = lm_68[27: 31] # top-down | |
| lm_nostrils = lm_68[31: 36] # top-down | |
| lm_eye_left = lm_68[36: 42] # left-clockwise | |
| lm_eye_right = lm_68[42: 48] # left-clockwise | |
| lm_mouth_outer = lm_68[48: 60] # left-clockwise | |
| lm_mouth_inner = lm_68[60: 68] # left-clockwise | |
| # Calculate auxiliary vectors. | |
| eye_left = np.mean(lm_eye_left, axis=0) | |
| eye_right = np.mean(lm_eye_right, axis=0) | |
| eye_avg = (eye_left + eye_right) * 0.5 | |
| eye_to_eye = eye_right - eye_left | |
| mouth_left = lm_mouth_outer[0] | |
| mouth_right = lm_mouth_outer[6] | |
| mouth_avg = (mouth_left + mouth_right) * 0.5 | |
| eye_to_mouth = mouth_avg - eye_avg | |
| # Choose oriented crop rectangle. | |
| x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1] | |
| x /= np.hypot(*x) | |
| x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8) | |
| y = np.flipud(x) * [-1, 1] | |
| c = eye_avg + eye_to_mouth * 0.1 | |
| quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y]) | |
| qsize = np.hypot(*x) * 2 | |
| # Load in-the-wild image. | |
| img = pil_img | |
| trans_points = lm_68 | |
| # Shrink. | |
| shrink = int(np.floor(qsize / output_size * 0.5)) | |
| if shrink > 1: | |
| rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink))) | |
| img = img.resize(rsize, PIL.Image.ANTIALIAS) | |
| quad /= shrink | |
| qsize /= shrink | |
| trans_points = trans_points / shrink | |
| # Crop. | |
| border = max(int(np.rint(qsize * 0.1)), 3) | |
| crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))), | |
| int(np.ceil(max(quad[:, 1])))) | |
| crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]), | |
| min(crop[3] + border, img.size[1])) | |
| if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]: | |
| img = img.crop(crop) | |
| quad -= crop[0:2] | |
| trans_points = trans_points - np.array([crop[0], crop[1]]) | |
| # Pad. | |
| pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))), | |
| int(np.ceil(max(quad[:, 1])))) | |
| pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0), | |
| max(pad[3] - img.size[1] + border, 0)) | |
| if enable_padding and max(pad) > border - 4: | |
| pad = np.maximum(pad, int(np.rint(qsize * 0.3))) | |
| img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect') | |
| trans_points = trans_points + np.array([pad[0], pad[1]]) | |
| h, w, _ = img.shape | |
| y, x, _ = np.ogrid[:h, :w, :1] | |
| mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]), | |
| 1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3])) | |
| blur = qsize * 0.02 | |
| img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0) | |
| img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0) | |
| img = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB') | |
| quad += pad[:2] | |
| # Transform. | |
| trans_data = (quad + 0.5) | |
| img = img.transform((transform_size, transform_size), PIL.Image.QUAD, trans_data.flatten(), PIL.Image.BILINEAR) | |
| if output_size < transform_size: | |
| img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS) | |
| projective_matrix = cv2.getPerspectiveTransform(trans_data.astype('float32'), | |
| np.array([[0, 0], [0, 1], [1, 1], [1, 0]], dtype='float32')) | |
| augmented_lm = projective_matrix @ np.concatenate([trans_points, np.ones([trans_points.shape[0], 1])], axis=1).T | |
| trans_points = augmented_lm[:2, :] / augmented_lm[2] * output_size | |
| trans_points = trans_points.T | |
| trans_points = (trans_points + 0.5).astype('int32') | |
| return img, trans_points[:68] | |
| def draw_landmarks(landmarks, img_np, font_size=1.0): | |
| font = cv2.FONT_HERSHEY_SIMPLEX | |
| for idx, point in enumerate(landmarks): | |
| pos = (point[0], point[1]) | |
| cv2.circle(img_np, pos, 2, color=(139, 0, 0)) | |
| cv2.putText(img_np, str(idx + 1), pos, font, font_size, (0, 0, 255), 1, cv2.LINE_AA) | |
| return img_np | |