Method inference

datasets/INFERENCE.py:107–285

(self, outs)

        return result
        
    def inference(self, outs):
        img_paths = self.img_paths
        sample_num = len(outs)
        output = {}
        
        for out in outs:
            ann_idx = out['image_idx']
            img_cropped = mmcv.imdenormalize(
                img=(out['img'].cpu().numpy()).transpose(1, 2, 0), 
                mean=np.array([123.675, 116.28, 103.53]), 
                std=np.array([58.395, 57.12, 57.375]),
                to_bgr=True).astype(np.uint8)
            # bb2img_trans = out['bb2img_trans']
            # img2bb_trans = out['img2bb_trans']
            scores = out['scores'].clone().cpu().numpy()
            img_shape = out['img_shape'].cpu().numpy()[::-1] # w, h
            width,height = img_shape
            width += width % 2
            height += height % 2
            img_shape = np.array([width, height])
            img = cv2.imread(img_paths[ann_idx]) # h, w

                
            joint_proj = out['smplx_joint_proj'].clone().cpu().numpy()
            joint_vis = out['smplx_joint_proj'].clone().cpu().numpy()
            joint_coco = out['keypoints_coco'].clone().cpu().numpy()
            joint_coco_raw = joint_coco.copy()
            smpl_kp3d_coco, _ = convert_kps(out['smpl_kp3d'].clone().cpu().numpy(),src='smplx',dst='coco', approximate=True)
            
            
            
            body_bbox = out['body_bbox'].clone().cpu().numpy()
            lhand_bbox = out['lhand_bbox'].clone().cpu().numpy()
            rhand_bbox = out['rhand_bbox'].clone().cpu().numpy()
            face_bbox = out['face_bbox'].clone().cpu().numpy()

            if self.resolution == [720, 1280]:
                joint_proj[:, :, 0] = joint_proj[:, :, 0] / img_shape[0] * 3840
                joint_proj[:, :, 1] = joint_proj[:, :, 1] / img_shape[1] * 2160
                joint_vis[:, :, 0] = joint_vis[:, :, 0] / img_shape[0] * img.shape[1]
                joint_vis[:, :, 1] = joint_vis[:, :, 1]/ img_shape[1] * img.shape[0]        
                
                joint_coco[:, :, 0] = joint_coco[:, :, 0] / img_shape[0] * img.shape[1]
                joint_coco[:, :, 1] = joint_coco[:, :, 1]/ img_shape[1] * img.shape[0] 
                scale = np.array([
                    img.shape[1]/img_shape[0],
                    img.shape[1]/img_shape[0], 
                    img.shape[1]/img_shape[0], 
                    img.shape[1]/img_shape[0], 
                    ])
                body_bbox_raw = body_bbox.copy()
                body_bbox = body_bbox * scale
                lhand_bbox = lhand_bbox * scale
                rhand_bbox = rhand_bbox * scale
                face_bbox = face_bbox * scale
            elif self.resolution == [1200, 1600]:
                
                joint_proj[:, :, 0] = joint_proj[:, :, 0] * (1200 / 800)