from pathlib import Path import torch import argparse import os import cv2 import numpy as np from hamer.configs import CACHE_DIR_HAMER from hamer.models import HAMER, download_models, load_hamer#, DEFAULT_CHECKPOINT from hamer.utils import recursive_to from hamer.datasets.vitdet_dataset import ViTDetDataset, DEFAULT_MEAN, DEFAULT_STD from hamer.utils.renderer import Renderer, cam_crop_to_full LIGHT_BLUE=(0.65098039, 0.74117647, 0.85882353) openpose_indices = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20] gt_indices = openpose_indices from vitpose_model import ViTPoseModel import json from typing import Dict, Optional, List, Tuple import math def convert_crop_coords_to_orig_img(bbox, keypoints, crop_size): # import IPython; IPython.embed(); exit() cx, cy, h = bbox[:, 0], bbox[:, 1], bbox[:, 2] # unnormalize to crop coords # keypoints = 0.5 * crop_size * (keypoints + 1.0) # rescale to orig img crop keypoints *= h[..., None, None] / crop_size # transform into original image coords keypoints[:,:,0] = (cx - h/2)[..., None] + keypoints[:,:,0] keypoints[:,:,1] = (cy - h/2)[..., None] + keypoints[:,:,1] return keypoints def get_keypoints_rectangle(keypoints: np.array, threshold: float) -> Tuple[float, float, float]: """ Compute rectangle enclosing keypoints above the threshold. Args: keypoints (np.array): Keypoint array of shape (N, 3). threshold (float): Confidence visualization threshold. Returns: Tuple[float, float, float]: Rectangle width, height and area. """ print(keypoints.shape) valid_ind = keypoints[:, -1] > threshold if valid_ind.sum() > 0: valid_keypoints = keypoints[valid_ind][:, :-1] max_x = valid_keypoints[:,0].max() max_y = valid_keypoints[:,1].max() min_x = valid_keypoints[:,0].min() min_y = valid_keypoints[:,1].min() width = max_x - min_x height = max_y - min_y area = width * height return width, height, area else: return 0,0,0 def render_keypoints(img: np.array, keypoints: np.array, pairs: List, colors: List, thickness_circle_ratio: float, thickness_line_ratio_wrt_circle: float, pose_scales: List, threshold: float = 0.1, alpha: float = 1.0) -> np.array: """ Render keypoints on input image. Args: img (np.array): Input image of shape (H, W, 3) with pixel values in the [0,255] range. keypoints (np.array): Keypoint array of shape (N, 3). pairs (List): List of keypoint pairs per limb. colors: (List): List of colors per keypoint. thickness_circle_ratio (float): Circle thickness ratio. thickness_line_ratio_wrt_circle (float): Line thickness ratio wrt the circle. pose_scales (List): List of pose scales. threshold (float): Only visualize keypoints with confidence above the threshold. Returns: (np.array): Image of shape (H, W, 3) with keypoints drawn on top of the original image. """ img_orig = img.copy() width, height = img.shape[1], img.shape[2] area = width * height lineType = 8 shift = 0 numberColors = len(colors) thresholdRectangle = 0.1 person_width, person_height, person_area = get_keypoints_rectangle(keypoints, thresholdRectangle) if person_area > 0: ratioAreas = min(1, max(person_width / width, person_height / height)) thicknessRatio = np.maximum(np.round(math.sqrt(area) * thickness_circle_ratio * ratioAreas), 2) thicknessCircle = np.maximum(1, thicknessRatio if ratioAreas > 0.05 else -np.ones_like(thicknessRatio)) thicknessLine = np.maximum(1, np.round(thicknessRatio * thickness_line_ratio_wrt_circle)) radius = thicknessRatio / 2 img = np.ascontiguousarray(img.copy()) for i, pair in enumerate(pairs): index1, index2 = pair if keypoints[index1, -1] > threshold and keypoints[index2, -1] > threshold: thicknessLineScaled = int(round(min(thicknessLine[index1], thicknessLine[index2]) * pose_scales[0])) colorIndex = index2 color = colors[colorIndex % numberColors] keypoint1 = keypoints[index1, :-1].astype(np.int32) keypoint2 = keypoints[index2, :-1].astype(np.int32) cv2.line(img, tuple(keypoint1.tolist()), tuple(keypoint2.tolist()), tuple(color.tolist()), thicknessLineScaled, lineType, shift) for part in range(len(keypoints)): faceIndex = part if keypoints[faceIndex, -1] > threshold: radiusScaled = int(round(radius[faceIndex] * pose_scales[0])) thicknessCircleScaled = int(round(thicknessCircle[faceIndex] * pose_scales[0])) colorIndex = part color = colors[colorIndex % numberColors] center = keypoints[faceIndex, :-1].astype(np.int32) cv2.circle(img, tuple(center.tolist()), radiusScaled, tuple(color.tolist()), thicknessCircleScaled, lineType, shift) return img def render_hand_keypoints(img, right_hand_keypoints, threshold=0.1, use_confidence=False, map_fn=lambda x: np.ones_like(x), alpha=1.0): if use_confidence and map_fn is not None: #thicknessCircleRatioLeft = 1./50 * map_fn(left_hand_keypoints[:, -1]) thicknessCircleRatioRight = 1./50 * map_fn(right_hand_keypoints[:, -1]) else: #thicknessCircleRatioLeft = 1./50 * np.ones(left_hand_keypoints.shape[0]) thicknessCircleRatioRight = 1./50 * np.ones(right_hand_keypoints.shape[0]) thicknessLineRatioWRTCircle = 0.75 pairs = [0,1, 1,2, 2,3, 3,4, 0,5, 5,6, 6,7, 7,8, 0,9, 9,10, 10,11, 11,12, 0,13, 13,14, 14,15, 15,16, 0,17, 17,18, 18,19, 19,20] pairs = np.array(pairs).reshape(-1,2) colors = [100., 100., 100., 100., 0., 0., 150., 0., 0., 200., 0., 0., 255., 0., 0., 100., 100., 0., 150., 150., 0., 200., 200., 0., 255., 255., 0., 0., 100., 50., 0., 150., 75., 0., 200., 100., 0., 255., 125., 0., 50., 100., 0., 75., 150., 0., 100., 200., 0., 125., 255., 100., 0., 100., 150., 0., 150., 200., 0., 200., 255., 0., 255.] colors = np.array(colors).reshape(-1,3) #colors = np.zeros_like(colors) poseScales = [1] #img = render_keypoints(img, left_hand_keypoints, pairs, colors, thicknessCircleRatioLeft, thicknessLineRatioWRTCircle, poseScales, threshold, alpha=alpha) img = render_keypoints(img, right_hand_keypoints, pairs, colors, thicknessCircleRatioRight, thicknessLineRatioWRTCircle, poseScales, threshold, alpha=alpha) #img = render_keypoints(img, right_hand_keypoints, pairs, colors, thickness_circle_ratio, thickness_line_ratio_wrt_circle, pose_scales, 0.1) return img def render_openpose(img: np.array, hand_keypoints: np.array) -> np.array: """ Render keypoints in the OpenPose format on input image. Args: img (np.array): Input image of shape (H, W, 3) with pixel values in the [0,255] range. body_keypoints (np.array): Keypoint array of shape (N, 3); 3 <====> (x, y, confidence). Returns: (np.array): Image of shape (H, W, 3) with keypoints drawn on top of the original image. """ img = render_hand_keypoints(img, hand_keypoints) return img def main(): parser = argparse.ArgumentParser(description='HaMeR demo code') parser.add_argument('--checkpoint', type=str, default='/data/home/zy3023/code/hand/Dyn-HaMR/', help='Path to pretrained model checkpoint') parser.add_argument('--img_folder', type=str, default='images', help='Folder with input images') parser.add_argument('--out_folder', type=str, default='out_demo', help='Output folder to save rendered results') parser.add_argument('--side_view', dest='side_view', action='store_true', default=False, help='If set, render side view also') parser.add_argument('--full_frame', dest='full_frame', action='store_true', default=True, help='If set, render all people together also') parser.add_argument('--save_mesh', dest='save_mesh', action='store_true', default=False, help='If set, save meshes to disk also') parser.add_argument('--batch_size', type=int, default=1, help='Batch size for inference/fitting') parser.add_argument('--rescale_factor', type=float, default=2.0, help='Factor for padding the bbox') parser.add_argument('--body_detector', type=str, default='vitdet', choices=['vitdet', 'regnety'], help='Using regnety improves runtime and reduces memory') parser.add_argument('--file_type', nargs='+', default=['*.jpg', '*.png'], help='List of file extensions to consider') args = parser.parse_args() # Download and load checkpoints download_models(CACHE_DIR_HAMER) # CACHE_DIR_HAMER = args.checkpoint model, model_cfg = load_hamer(args.checkpoint) # Setup HaMeR model device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') model = model.to(device) model.eval() # Load detector from hamer.utils.utils_detectron2 import DefaultPredictor_Lazy if args.body_detector == 'vitdet': from detectron2.config import LazyConfig import hamer cfg_path = Path(hamer.__file__).parent/'configs'/'cascade_mask_rcnn_vitdet_h_75ep.py' detectron2_cfg = LazyConfig.load(str(cfg_path)) detectron2_cfg.train.init_checkpoint = "https://dl.fbaipublicfiles.com/detectron2/ViTDet/COCO/cascade_mask_rcnn_vitdet_h/f328730692/model_final_f05665.pkl" for i in range(3): detectron2_cfg.model.roi_heads.box_predictors[i].test_score_thresh = 0.25 detector = DefaultPredictor_Lazy(detectron2_cfg) elif args.body_detector == 'regnety': from detectron2 import model_zoo from detectron2.config import get_cfg detectron2_cfg = model_zoo.get_config('new_baselines/mask_rcnn_regnety_4gf_dds_FPN_400ep_LSJ.py', trained=True) detectron2_cfg.model.roi_heads.box_predictor.test_score_thresh = 0.5 detectron2_cfg.model.roi_heads.box_predictor.test_nms_thresh = 0.4 detector = DefaultPredictor_Lazy(detectron2_cfg) # keypoint detector cpm = ViTPoseModel(device) # Setup the renderer renderer = Renderer(model_cfg, faces=model.mano.faces) # Make output directory if it does not exist os.makedirs(args.out_folder, exist_ok=True) # Get all demo images ends with .jpg or .png img_paths = [img for end in args.file_type for img in Path(args.img_folder).glob(end)] # Iterate over all images in folder for img_path in img_paths: img_cv2 = cv2.imread(str(img_path)) # Detect humans in image det_out = detector(img_cv2) img = img_cv2.copy()[:, :, ::-1] det_instances = det_out['instances'] valid_idx = (det_instances.pred_classes==0) & (det_instances.scores > 0.5) pred_bboxes=det_instances.pred_boxes.tensor[valid_idx].cpu().numpy() pred_scores=det_instances.scores[valid_idx].cpu().numpy() # Detect human keypoints for each person vitposes_out = cpm.predict_pose( img, [np.concatenate([pred_bboxes, pred_scores[:, None]], axis=1)], ) bboxes = [] is_right = [] # Use hands based on hand keypoint detections for vitposes in vitposes_out: left_hand_keyp = vitposes['keypoints'][-42:-21] right_hand_keyp = vitposes['keypoints'][-21:] # Rejecting not confident detections keyp = left_hand_keyp valid = keyp[:,2] > 0.5 if sum(valid) > 3: bbox = [keyp[valid,0].min(), keyp[valid,1].min(), keyp[valid,0].max(), keyp[valid,1].max()] bboxes.append(bbox) is_right.append(0) keyp = right_hand_keyp valid = keyp[:,2] > 0.5 if sum(valid) > 3: bbox = [keyp[valid,0].min(), keyp[valid,1].min(), keyp[valid,0].max(), keyp[valid,1].max()] bboxes.append(bbox) is_right.append(1) if len(bboxes) == 0: continue boxes = np.stack(bboxes) right = np.stack(is_right) # Run reconstruction on all detected hands dataset = ViTDetDataset(model_cfg, img_cv2, boxes, right, rescale_factor=args.rescale_factor) dataloader = torch.utils.data.DataLoader(dataset, batch_size=8, shuffle=False, num_workers=0) all_verts = [] all_cam_t = [] all_right = [] all_pred_2d = [] all_bboxes = [] for batch in dataloader: batch = recursive_to(batch, device) with torch.no_grad(): out = model(batch) multiplier = (2*batch['right']-1) pred_cam = out['pred_cam'] pred_cam[:,1] = multiplier*pred_cam[:,1] box_center = batch["box_center"].float() box_size = batch["box_size"].float() img_size = batch["img_size"].float() multiplier = (2*batch['right']-1) scaled_focal_length = model_cfg.EXTRA.FOCAL_LENGTH / model_cfg.MODEL.IMAGE_SIZE * img_size.max() pred_cam_t_full = cam_crop_to_full(pred_cam, box_center, box_size, img_size, scaled_focal_length).detach().cpu().numpy() # Render the result batch_size = batch['img'].shape[0] for n in range(batch_size): # Get filename from path img_path img_fn, _ = os.path.splitext(os.path.basename(img_path)) person_id = int(batch['personid'][n]) white_img = (torch.ones_like(batch['img'][n]).cpu() - DEFAULT_MEAN[:,None,None]/255) / (DEFAULT_STD[:,None,None]/255) input_patch = batch['img'][n].cpu() * (DEFAULT_STD[:,None,None]/255) + (DEFAULT_MEAN[:,None,None]/255) input_patch = input_patch.permute(1,2,0).numpy() # regression_img = renderer(out['pred_vertices'][n].detach().cpu().numpy(), # out['pred_cam_t'][n].detach().cpu().numpy(), # batch['img'][n], # mesh_base_color=LIGHT_BLUE, # scene_bg_color=(1, 1, 1), # ) # if args.side_view: # side_img = renderer(out['pred_vertices'][n].detach().cpu().numpy(), # out['pred_cam_t'][n].detach().cpu().numpy(), # white_img, # mesh_base_color=LIGHT_BLUE, # scene_bg_color=(1, 1, 1), # side_view=True) # print('side_img: ', side_img.shape, regression_img.shape, input_patch.shape) # final_img = np.concatenate([input_patch, regression_img, side_img], axis=1) # else: # final_img = np.concatenate([input_patch, regression_img], axis=1) # cv2.imwrite(os.path.join(args.out_folder, f'{img_fn}_{person_id}.png'), 255*final_img[:, :, ::-1]) # Add all verts and cams to list is_right = batch['right'][n].cpu().numpy() verts = out['pred_vertices'][n].detach().cpu().numpy() pred_joints = out['pred_keypoints_2d'][n].detach().cpu().numpy() pred_joints[:,0] = (2*is_right-1)*pred_joints[:,0] bbox = batch['bbox'][n].detach().cpu().numpy() verts[:,0] = (2*is_right-1)*verts[:,0] cam_t = pred_cam_t_full[n] all_verts.append(verts) all_pred_2d.append(pred_joints) all_bboxes.append(bbox) all_cam_t.append(cam_t) all_right.append(is_right) # Save all meshes to disk if args.save_mesh: camera_translation = cam_t.copy() tmesh = renderer.vertices_to_trimesh(verts, camera_translation, LIGHT_BLUE, is_right=is_right) tmesh.export(os.path.join(args.out_folder, f'{img_fn}_{person_id}.obj')) # Render front view if args.full_frame and len(all_verts) > 0: all_pred_2d = np.stack(all_pred_2d) all_bboxes = np.stack(all_bboxes) # print('vit_2d: ', all_vit_2d.shape) print('pred_2d: ', all_pred_2d.shape) print('all_bboxes: ', all_bboxes.shape) all_pred_2d = model_cfg.MODEL.IMAGE_SIZE * (all_pred_2d + 0.5) all_pred_2d = convert_crop_coords_to_orig_img(bbox=all_bboxes, keypoints=all_pred_2d, crop_size=model_cfg.MODEL.IMAGE_SIZE) all_pred_2d = np.concatenate([all_pred_2d, np.ones((all_pred_2d.shape[0], all_pred_2d.shape[1], 1))], axis=-1) assert len(all_pred_2d.shape) == 3 assert all_pred_2d.shape[-1] == 3 all_pred_2d[:,:,-1] = 1 misc_args = dict( mesh_base_color=LIGHT_BLUE, scene_bg_color=(1, 1, 1), focal_length=scaled_focal_length, ) cam_view = renderer.render_rgba_multiple(all_verts, cam_t=all_cam_t, render_res=img_size[n], is_right=all_right, **misc_args) # Overlay image input_img = img_cv2.astype(np.float32)[:,:,::-1]/255.0 input_img = np.concatenate([input_img, np.ones_like(input_img[:,:,:1])], axis=2) # Add alpha channel input_img_overlay = input_img[:,:,:3] * (1-cam_view[:,:,3:]) + cam_view[:,:,:3] * cam_view[:,:,3:] cv2.imwrite(os.path.join(args.out_folder, f'{img_fn}_all.jpg'), 255*input_img_overlay[:, :, ::-1]) # render 2d keypoints pred_img = input_img.copy()[:,:,:-1][:,:,::-1] * 255 for i in range(len(all_verts)): body_keypoints_2d = all_pred_2d[i, :21].copy() assert (body_keypoints_2d == all_pred_2d[i, :21]).all() pred_img = render_openpose(pred_img, body_keypoints_2d) # draw 2d keypoints cv2.imwrite(os.path.join(args.out_folder, f'{img_fn}_all_2d.jpg'), pred_img) if __name__ == '__main__': main()