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on
L40S
Running
on
L40S
| import os | |
| import numpy as np | |
| import random | |
| from PIL import Image | |
| import torch | |
| from torch.utils.data import Dataset | |
| import torchvision.transforms as transforms | |
| from transformers import CLIPImageProcessor | |
| # import librosa | |
| import os | |
| import cv2 | |
| mean_face_lm5p_256 = np.array([ | |
| [(30.2946+8)*2+16, 51.6963*2], | |
| [(65.5318+8)*2+16, 51.5014*2], | |
| [(48.0252+8)*2+16, 71.7366*2], | |
| [(33.5493+8)*2+16, 92.3655*2], | |
| [(62.7299+8)*2+16, 92.2041*2], | |
| ], dtype=np.float32) | |
| def get_affine_transform(target_face_lm5p, mean_lm5p): | |
| mat_warp = np.zeros((2,3)) | |
| A = np.zeros((4,4)) | |
| B = np.zeros((4)) | |
| for i in range(5): | |
| A[0][0] += target_face_lm5p[i][0] * target_face_lm5p[i][0] + target_face_lm5p[i][1] * target_face_lm5p[i][1] | |
| A[0][2] += target_face_lm5p[i][0] | |
| A[0][3] += target_face_lm5p[i][1] | |
| B[0] += target_face_lm5p[i][0] * mean_lm5p[i][0] + target_face_lm5p[i][1] * mean_lm5p[i][1] #sb[1] += a[i].x*b[i].y - a[i].y*b[i].x; | |
| B[1] += target_face_lm5p[i][0] * mean_lm5p[i][1] - target_face_lm5p[i][1] * mean_lm5p[i][0] | |
| B[2] += mean_lm5p[i][0] | |
| B[3] += mean_lm5p[i][1] | |
| A[1][1] = A[0][0] | |
| A[2][1] = A[1][2] = -A[0][3] | |
| A[3][1] = A[1][3] = A[2][0] = A[0][2] | |
| A[2][2] = A[3][3] = 5 | |
| A[3][0] = A[0][3] | |
| _, mat23 = cv2.solve(A, B, flags=cv2.DECOMP_SVD) | |
| mat_warp[0][0] = mat23[0] | |
| mat_warp[1][1] = mat23[0] | |
| mat_warp[0][1] = -mat23[1] | |
| mat_warp[1][0] = mat23[1] | |
| mat_warp[0][2] = mat23[2] | |
| mat_warp[1][2] = mat23[3] | |
| return mat_warp | |
| def get_union_bbox(bboxes): | |
| bboxes = np.array(bboxes) | |
| min_x = np.min(bboxes[:, 0]) | |
| min_y = np.min(bboxes[:, 1]) | |
| max_x = np.max(bboxes[:, 2]) | |
| max_y = np.max(bboxes[:, 3]) | |
| return np.array([min_x, min_y, max_x, max_y]) | |
| def process_bbox(bbox, expand_radio, height, width): | |
| def expand(bbox, ratio, height, width): | |
| bbox_h = bbox[3] - bbox[1] | |
| bbox_w = bbox[2] - bbox[0] | |
| expand_x1 = max(bbox[0] - ratio * bbox_w, 0) | |
| expand_y1 = max(bbox[1] - ratio * bbox_h, 0) | |
| expand_x2 = min(bbox[2] + ratio * bbox_w, width) | |
| expand_y2 = min(bbox[3] + ratio * bbox_h, height) | |
| return [expand_x1,expand_y1,expand_x2,expand_y2] | |
| def to_square(bbox_src, bbox_expend, height, width): | |
| h = bbox_expend[3] - bbox_expend[1] | |
| w = bbox_expend[2] - bbox_expend[0] | |
| c_h = (bbox_expend[1] + bbox_expend[3]) / 2 | |
| c_w = (bbox_expend[0] + bbox_expend[2]) / 2 | |
| c = min(h, w) / 2 | |
| c_src_h = (bbox_src[1] + bbox_src[3]) / 2 | |
| c_src_w = (bbox_src[0] + bbox_src[2]) / 2 | |
| s_h, s_w = 0, 0 | |
| if w < h: | |
| d = abs((h - w) / 2) | |
| s_h = min(d, abs(c_src_h-c_h)) | |
| s_h = s_h if c_src_h > c_h else s_h * (-1) | |
| else: | |
| d = abs((h - w) / 2) | |
| s_w = min(d, abs(c_src_w-c_w)) | |
| s_w = s_w if c_src_w > c_w else s_w * (-1) | |
| c_h = (bbox_expend[1] + bbox_expend[3]) / 2 + s_h | |
| c_w = (bbox_expend[0] + bbox_expend[2]) / 2 + s_w | |
| square_x1 = c_w - c | |
| square_y1 = c_h - c | |
| square_x2 = c_w + c | |
| square_y2 = c_h + c | |
| return [round(square_x1), round(square_y1), round(square_x2), round(square_y2)] | |
| bbox_expend = expand(bbox, expand_radio, height=height, width=width) | |
| processed_bbox = to_square(bbox, bbox_expend, height=height, width=width) | |
| return processed_bbox | |
| def crop_resize_img(img, bbox): | |
| x1, y1, x2, y2 = bbox | |
| img = img.crop((x1, y1, x2, y2)) | |
| return img | |