in lib/data/EvalDataset.py [0:0]
def get_item(self, index):
img_path = self.img_files[index]
rect_path = self.img_files[index].replace('.%s' % (self.img_files[index].split('.')[-1]), '_rect.txt')
# Name
img_name = os.path.splitext(os.path.basename(img_path))[0]
im = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)
if im.shape[2] == 4:
im = im / 255.0
im[:,:,:3] /= im[:,:,3:] + 1e-8
im = im[:,:,3:] * im[:,:,:3] + 0.5 * (1.0 - im[:,:,3:])
im = (255.0 * im).astype(np.uint8)
h, w = im.shape[:2]
intrinsic = np.identity(4)
trans_mat = np.identity(4)
rects = np.loadtxt(rect_path, dtype=np.int32)
if len(rects.shape) == 1:
rects = rects[None]
pid = min(rects.shape[0]-1, self.person_id)
rect = rects[pid].tolist()
im = crop_image(im, rect)
scale_im2ndc = 1.0 / float(w // 2)
scale = w / rect[2]
trans_mat *= scale
trans_mat[3,3] = 1.0
trans_mat[0, 3] = -scale*(rect[0] + rect[2]//2 - w//2) * scale_im2ndc
trans_mat[1, 3] = scale*(rect[1] + rect[3]//2 - h//2) * scale_im2ndc
intrinsic = np.matmul(trans_mat, intrinsic)
im_512 = cv2.resize(im, (512, 512))
im = cv2.resize(im, (self.load_size, self.load_size))
image_512 = Image.fromarray(im_512[:,:,::-1]).convert('RGB')
image = Image.fromarray(im[:,:,::-1]).convert('RGB')
B_MIN = np.array([-1, -1, -1])
B_MAX = np.array([1, 1, 1])
projection_matrix = np.identity(4)
projection_matrix[1, 1] = -1
calib = torch.Tensor(projection_matrix).float()
calib_world = torch.Tensor(intrinsic).float()
# image
image_512 = self.to_tensor(image_512)
image = self.to_tensor(image)
return {
'name': img_name,
'img': image.unsqueeze(0),
'img_512': image_512.unsqueeze(0),
'calib': calib.unsqueeze(0),
'calib_world': calib_world.unsqueeze(0),
'b_min': B_MIN,
'b_max': B_MAX,
}