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| import json | |
| import cv2 | |
| import numpy as np | |
| import torch | |
| from skimage.transform import resize, rotate | |
| from torchvision.transforms import Normalize | |
| from .constants import IMG_NORM_MEAN, IMG_NORM_STD, IMG_RES | |
| def get_transform(center, scale, res, rot=0): | |
| """Generate transformation matrix.""" | |
| h = 200 * scale | |
| t = np.zeros((3, 3)) | |
| t[0, 0] = float(res[1]) / h | |
| t[1, 1] = float(res[0]) / h | |
| t[0, 2] = res[1] * (-float(center[0]) / h + 0.5) | |
| t[1, 2] = res[0] * (-float(center[1]) / h + 0.5) | |
| t[2, 2] = 1 | |
| if not rot == 0: | |
| rot = -rot # To match direction of rotation from cropping | |
| rot_mat = np.zeros((3, 3)) | |
| rot_rad = rot * np.pi / 180 | |
| sn, cs = np.sin(rot_rad), np.cos(rot_rad) | |
| rot_mat[0, :2] = [cs, -sn] | |
| rot_mat[1, :2] = [sn, cs] | |
| rot_mat[2, 2] = 1 | |
| # Need to rotate around center | |
| t_mat = np.eye(3) | |
| t_mat[0, 2] = -res[1] / 2 | |
| t_mat[1, 2] = -res[0] / 2 | |
| t_inv = t_mat.copy() | |
| t_inv[:2, 2] *= -1 | |
| t = np.dot(t_inv, np.dot(rot_mat, np.dot(t_mat, t))) | |
| return t | |
| def transform(pt, center, scale, res, invert=0, rot=0): | |
| """Transform pixel location to different reference.""" | |
| t = get_transform(center, scale, res, rot=rot) | |
| if invert: | |
| t = np.linalg.inv(t) | |
| new_pt = np.array([pt[0] - 1, pt[1] - 1, 1.0]).T | |
| new_pt = np.dot(t, new_pt) | |
| return new_pt[:2].astype(int) + 1 | |
| def crop(img, center, scale, res, rot=0): | |
| """Crop image according to the supplied bounding box.""" | |
| # Upper left point | |
| ul = np.array(transform([1, 1], center, scale, res, invert=1)) - 1 | |
| # Bottom right point | |
| br = np.array(transform([res[0] + 1, res[1] + 1], center, scale, res, invert=1)) - 1 | |
| # Padding so that when rotated proper amount of context is included | |
| pad = int(np.linalg.norm(br - ul) / 2 - float(br[1] - ul[1]) / 2) | |
| if not rot == 0: | |
| ul -= pad | |
| br += pad | |
| new_shape = [br[1] - ul[1], br[0] - ul[0]] | |
| if len(img.shape) > 2: | |
| new_shape += [img.shape[2]] | |
| new_img = np.zeros(new_shape) | |
| # Range to fill new array | |
| new_x = max(0, -ul[0]), min(br[0], len(img[0])) - ul[0] | |
| new_y = max(0, -ul[1]), min(br[1], len(img)) - ul[1] | |
| # Range to sample from original image | |
| old_x = max(0, ul[0]), min(len(img[0]), br[0]) | |
| old_y = max(0, ul[1]), min(len(img), br[1]) | |
| new_img[new_y[0] : new_y[1], new_x[0] : new_x[1]] = img[ | |
| old_y[0] : old_y[1], old_x[0] : old_x[1] | |
| ] | |
| if not rot == 0: | |
| # Remove padding | |
| new_img = rotate(new_img, rot) | |
| new_img = new_img[pad:-pad, pad:-pad] | |
| new_img = resize(new_img, res) | |
| return new_img | |
| def bbox_from_openpose(openpose_file, rescale=1.2, detection_thresh=0.2): | |
| """Get center and scale for bounding box from openpose detections.""" | |
| with open(openpose_file, "r") as f: | |
| keypoints = json.load(f)["people"][0]["pose_keypoints_2d"] | |
| keypoints = np.reshape(np.array(keypoints), (-1, 3)) | |
| valid = keypoints[:, -1] > detection_thresh | |
| valid_keypoints = keypoints[valid][:, :-1] | |
| center = valid_keypoints.mean(axis=0) | |
| bbox_size = (valid_keypoints.max(axis=0) - valid_keypoints.min(axis=0)).max() | |
| # adjust bounding box tightness | |
| scale = bbox_size / 200.0 | |
| scale *= rescale | |
| return center, scale | |
| def bbox_from_json(bbox_file): | |
| """Get center and scale of bounding box from bounding box annotations. | |
| The expected format is [top_left(x), top_left(y), width, height]. | |
| """ | |
| with open(bbox_file, "r") as f: | |
| bbox = np.array(json.load(f)["bbox"]).astype(np.float32) | |
| ul_corner = bbox[:2] | |
| center = ul_corner + 0.5 * bbox[2:] | |
| width = max(bbox[2], bbox[3]) | |
| scale = width / 200.0 | |
| # make sure the bounding box is rectangular | |
| return center, scale | |
| def process_image(img_file, bbox_file=None, openpose_file=None, input_res=IMG_RES): | |
| """Read image, do preprocessing and possibly crop it according to the bounding box. | |
| If there are bounding box annotations, use them to crop the image. | |
| If no bounding box is specified but openpose detections are available, use them to get the bounding box. | |
| """ | |
| img_file = str(img_file) | |
| normalize_img = Normalize(mean=IMG_NORM_MEAN, std=IMG_NORM_STD) | |
| img = cv2.imread(img_file)[ | |
| :, :, ::-1 | |
| ].copy() # PyTorch does not support negative stride at the moment | |
| if bbox_file is None and openpose_file is None: | |
| # Assume that the person is centerered in the image | |
| height = img.shape[0] | |
| width = img.shape[1] | |
| center = np.array([width // 2, height // 2]) | |
| scale = max(height, width) / 200 | |
| else: | |
| if bbox_file is not None: | |
| center, scale = bbox_from_json(bbox_file) | |
| elif openpose_file is not None: | |
| center, scale = bbox_from_openpose(openpose_file) | |
| img = crop(img, center, scale, (input_res, input_res)) | |
| img = img.astype(np.float32) / 255.0 | |
| img = torch.from_numpy(img).permute(2, 0, 1) | |
| norm_img = normalize_img(img.clone()) | |
| return img, norm_img | |