def evaluate(self, preds):
print()
print('Evaluation start...')
gts = self.datalist
preds_joint_coord, preds_rel_root_depth, preds_hand_type, inv_trans = preds['joint_coord'], preds['rel_root_depth'], preds['hand_type'], preds['inv_trans']
assert len(gts) == len(preds_joint_coord)
sample_num = len(gts)
mpjpe = [[] for _ in range(self.joint_num)] # treat right and left hand identical
acc_hand_cls = 0
for n in range(sample_num):
data = gts[n]
bbox, cam_param, joint, gt_hand_type = data['bbox'], data['cam_param'], data['joint'], data['hand_type']
focal = cam_param['focal']
princpt = cam_param['princpt']
gt_joint_coord = joint['cam_coord']
joint_valid = joint['valid']
# restore coordinates to original space
pred_joint_coord_img = preds_joint_coord[n].copy()
pred_joint_coord_img[:,0] = pred_joint_coord_img[:,0]/cfg.output_hm_shape[2]*cfg.input_img_shape[1]
pred_joint_coord_img[:,1] = pred_joint_coord_img[:,1]/cfg.output_hm_shape[1]*cfg.input_img_shape[0]
for j in range(self.joint_num*2):
pred_joint_coord_img[j,:2] = trans_point2d(pred_joint_coord_img[j,:2],inv_trans[n])
pred_joint_coord_img[:,2] = (pred_joint_coord_img[:,2]/cfg.output_hm_shape[0] * 2 - 1) * (cfg.bbox_3d_size/2)
pred_joint_coord_img[:,2] = pred_joint_coord_img[:,2] + data['abs_depth']
# back project to camera coordinate system
pred_joint_coord_cam = pixel2cam(pred_joint_coord_img, focal, princpt)
# root joint alignment
pred_joint_coord_cam[self.joint_type['right']] = pred_joint_coord_cam[self.joint_type['right']] - pred_joint_coord_cam[self.root_joint_idx['right'],None,:]
pred_joint_coord_cam[self.joint_type['left']] = pred_joint_coord_cam[self.joint_type['left']] - pred_joint_coord_cam[self.root_joint_idx['left'],None,:]
gt_joint_coord[self.joint_type['right']] = gt_joint_coord[self.joint_type['right']] - gt_joint_coord[self.root_joint_idx['right'],None,:]
gt_joint_coord[self.joint_type['left']] = gt_joint_coord[self.joint_type['left']] - gt_joint_coord[self.root_joint_idx['left'],None,:]
# select right or left hand using groundtruth hand type
pred_joint_coord_cam = pred_joint_coord_cam[self.joint_type[gt_hand_type]]
gt_joint_coord = gt_joint_coord[self.joint_type[gt_hand_type]]
joint_valid = joint_valid[self.joint_type[gt_hand_type]]
# mpjpe save
for j in range(self.joint_num):
if joint_valid[j]:
mpjpe[j].append(np.sqrt(np.sum((pred_joint_coord_cam[j] - gt_joint_coord[j])**2)))
if gt_hand_type == 'right' and preds_hand_type[n][0] > 0.5 and preds_hand_type[n][1] < 0.5:
acc_hand_cls += 1
elif gt_hand_type == 'left' and preds_hand_type[n][0] < 0.5 and preds_hand_type[n][1] > 0.5:
acc_hand_cls += 1
vis = False
if vis:
img_path = data['img_path']
cvimg = cv2.imread(img_path, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
_img = cvimg[:,:,::-1].transpose(2,0,1)
vis_kps = pred_joint_coord_img.copy()
vis_valid = joint_valid.copy()
filename = 'out_' + str(n) + '.jpg'
vis_keypoints(_img, vis_kps, vis_valid, self.skeleton[:self.joint_num], filename)
vis = False
if vis:
filename = 'out_' + str(n) + '_3d.png'
vis_3d_keypoints(pred_joint_coord_cam, joint_valid, self.skeleton[:self.joint_num], filename)
print('Handedness accuracy: ' + str(acc_hand_cls / sample_num))
eval_summary = 'MPJPE for each joint: \n'
for j in range(self.joint_num):
mpjpe[j] = np.mean(np.stack(mpjpe[j]))
joint_name = self.skeleton[j]['name']
eval_summary += (joint_name + ': %.2f, ' % mpjpe[j])
print(eval_summary)
print('MPJPE: %.2f' % (np.mean(mpjpe)))
def evaluate(self, preds):
print()
print('Evaluation start...')
gts = self.datalist
preds_joint_coord, preds_rel_root_depth, preds_hand_type, inv_trans = preds['joint_coord'], preds['rel_root_depth'], preds['hand_type'], preds['inv_trans']
assert len(gts) == len(preds_joint_coord)
sample_num = len(gts)
mpjpe_sh = [[] for _ in range(self.joint_num*2)]
mpjpe_ih = [[] for _ in range(self.joint_num*2)]
mrrpe = []
acc_hand_cls = 0; hand_cls_cnt = 0;
for n in range(sample_num):
data = gts[n]
bbox, cam_param, joint, gt_hand_type, hand_type_valid = data['bbox'], data['cam_param'], data['joint'], data['hand_type'], data['hand_type_valid']
focal = cam_param['focal']
princpt = cam_param['princpt']
gt_joint_coord = joint['cam_coord']
joint_valid = joint['valid']
# restore xy coordinates to original image space
pred_joint_coord_img = preds_joint_coord[n].copy()
pred_joint_coord_img[:,0] = pred_joint_coord_img[:,0]/cfg.output_hm_shape[2]*cfg.input_img_shape[1]
pred_joint_coord_img[:,1] = pred_joint_coord_img[:,1]/cfg.output_hm_shape[1]*cfg.input_img_shape[0]
for j in range(self.joint_num*2):
pred_joint_coord_img[j,:2] = trans_point2d(pred_joint_coord_img[j,:2],inv_trans[n])
# restore depth to original camera space
pred_joint_coord_img[:,2] = (pred_joint_coord_img[:,2]/cfg.output_hm_shape[0] * 2 - 1) * (cfg.bbox_3d_size/2)
# mrrpe
if gt_hand_type == 'interacting' and joint_valid[self.root_joint_idx['left']] and joint_valid[self.root_joint_idx['right']]:
pred_rel_root_depth = (preds_rel_root_depth[n]/cfg.output_root_hm_shape * 2 - 1) * (cfg.bbox_3d_size_root/2)
pred_left_root_img = pred_joint_coord_img[self.root_joint_idx['left']].copy()
pred_left_root_img[2] += data['abs_depth']['right'] + pred_rel_root_depth
pred_left_root_cam = pixel2cam(pred_left_root_img[None,:], focal, princpt)[0]
pred_right_root_img = pred_joint_coord_img[self.root_joint_idx['right']].copy()
pred_right_root_img[2] += data['abs_depth']['right']
pred_right_root_cam = pixel2cam(pred_right_root_img[None,:], focal, princpt)[0]
pred_rel_root = pred_left_root_cam - pred_right_root_cam
gt_rel_root = gt_joint_coord[self.root_joint_idx['left']] - gt_joint_coord[self.root_joint_idx['right']]
mrrpe.append(float(np.sqrt(np.sum((pred_rel_root - gt_rel_root)**2))))
# add root joint depth
pred_joint_coord_img[self.joint_type['right'],2] += data['abs_depth']['right']
pred_joint_coord_img[self.joint_type['left'],2] += data['abs_depth']['left']
# back project to camera coordinate system
pred_joint_coord_cam = pixel2cam(pred_joint_coord_img, focal, princpt)
# root joint alignment
for h in ('right', 'left'):
pred_joint_coord_cam[self.joint_type[h]] = pred_joint_coord_cam[self.joint_type[h]] - pred_joint_coord_cam[self.root_joint_idx[h],None,:]
gt_joint_coord[self.joint_type[h]] = gt_joint_coord[self.joint_type[h]] - gt_joint_coord[self.root_joint_idx[h],None,:]
# mpjpe
for j in range(self.joint_num*2):
if joint_valid[j]:
if gt_hand_type == 'right' or gt_hand_type == 'left':
mpjpe_sh[j].append(np.sqrt(np.sum((pred_joint_coord_cam[j] - gt_joint_coord[j])**2)))
else:
mpjpe_ih[j].append(np.sqrt(np.sum((pred_joint_coord_cam[j] - gt_joint_coord[j])**2)))
# handedness accuray
if hand_type_valid:
if gt_hand_type == 'right' and preds_hand_type[n][0] > 0.5 and preds_hand_type[n][1] < 0.5:
acc_hand_cls += 1
elif gt_hand_type == 'left' and preds_hand_type[n][0] < 0.5 and preds_hand_type[n][1] > 0.5:
acc_hand_cls += 1
elif gt_hand_type == 'interacting' and preds_hand_type[n][0] > 0.5 and preds_hand_type[n][1] > 0.5:
acc_hand_cls += 1
hand_cls_cnt += 1
vis = False
if vis:
img_path = data['img_path']
cvimg = cv2.imread(img_path, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
_img = cvimg[:,:,::-1].transpose(2,0,1)
vis_kps = pred_joint_coord_img.copy()
vis_valid = joint_valid.copy()
capture = str(data['capture'])
cam = str(data['cam'])
frame = str(data['frame'])
filename = 'out_' + str(n) + '_' + gt_hand_type + '.jpg'
vis_keypoints(_img, vis_kps, vis_valid, self.skeleton, filename)
vis = False
if vis:
filename = 'out_' + str(n) + '_3d.jpg'
vis_3d_keypoints(pred_joint_coord_cam, joint_valid, self.skeleton, filename)
if hand_cls_cnt > 0: print('Handedness accuracy: ' + str(acc_hand_cls / hand_cls_cnt))
if len(mrrpe) > 0: print('MRRPE: ' + str(sum(mrrpe)/len(mrrpe)))
print()
tot_err = []
eval_summary = 'MPJPE for each joint: \n'
for j in range(self.joint_num*2):
tot_err_j = np.mean(np.concatenate((np.stack(mpjpe_sh[j]), np.stack(mpjpe_ih[j]))))
joint_name = self.skeleton[j]['name']
eval_summary += (joint_name + ': %.2f, ' % tot_err_j)
tot_err.append(tot_err_j)
print(eval_summary)
print('MPJPE for all hand sequences: %.2f' % (np.mean(tot_err)))
print()
eval_summary = 'MPJPE for each joint: \n'
for j in range(self.joint_num*2):
mpjpe_sh[j] = np.mean(np.stack(mpjpe_sh[j]))
joint_name = self.skeleton[j]['name']
eval_summary += (joint_name + ': %.2f, ' % mpjpe_sh[j])
print(eval_summary)
print('MPJPE for single hand sequences: %.2f' % (np.mean(mpjpe_sh)))
print()
eval_summary = 'MPJPE for each joint: \n'
for j in range(self.joint_num*2):
mpjpe_ih[j] = np.mean(np.stack(mpjpe_ih[j]))
joint_name = self.skeleton[j]['name']
eval_summary += (joint_name + ': %.2f, ' % mpjpe_ih[j])
print(eval_summary)
print('MPJPE for interacting hand sequences: %.2f' % (np.mean(mpjpe_ih)))
def visualize(self, preds, filename, img_path, inv_trans):
joint_valid = [1.0]*21 + [1.0]*21 #change 1.0 to 0 if that hand is not resent right hand is comes first in output
focal = [1500, 1500] # x-axis, y-axis
princpt = [256/2, 256/2]
root_joint_idx = {'right': 20, 'left': 41}
preds_joint_coord, preds_rel_root_depth, preds_hand_type = preds['joint_coord'], preds['rel_root_depth'], preds['hand_type']
print(preds_hand_type)
# img = load_image(img_path)
# inv_trans = augmentation(img,
# np.array(bbox),
# preds_joint_coord,
# joint_valid,
# preds_hand_type,
# 'test',
# None)
pred_joint_coord_img = preds_joint_coord[0].copy()
pred_joint_coord_img[:,0] = pred_joint_coord_img[:,0]/cfg.output_hm_shape[2]*cfg.input_img_shape[1]
pred_joint_coord_img[:,1] = pred_joint_coord_img[:,1]/cfg.output_hm_shape[1]*cfg.input_img_shape[0]
for j in range(21*2):
pred_joint_coord_img[j,:2] = trans_point2d(pred_joint_coord_img[j,:2], inv_trans)
pred_joint_coord_img[:,2] = (pred_joint_coord_img[:,2]/cfg.output_hm_shape[0] * 2 - 1) * (cfg.bbox_3d_size/2)
# if preds_hand_type[0][0] == 0.9 and preds_hand_type[0][1] == 0.9: #change threshold to execute this parth if both handa are present
# pred_rel_root_depth = (preds_rel_root_depth[0]/cfg.output_root_hm_shape * 2 - 1) * (cfg.bbox_3d_size_root/2)
# pred_left_root_img = pred_joint_coord_img[root_joint_idx['left']].copy()
# pred_left_root_img[2] += pred_rel_root_depth
# pred_left_root_cam = pixel2cam(pred_left_root_img[None,:], focal, princpt)[0]
# pred_right_root_img = pred_joint_coord_img[root_joint_idx['right']].copy()
# pred_right_root_cam = pixel2cam(pred_right_root_img[None,:], focal, princpt)[0]
# pred_rel_root = pred_left_root_cam - pred_right_root_cam
# pred_joint_coord_cam = pixel2cam(pred_joint_coord_img, focal, princpt)
# joint_type = {'right': np.arange(0,21), 'left': np.arange(21,21*2)}
# for h in ('right', 'left'):
# pred_joint_coord_cam[joint_type[h]] = pred_joint_coord_cam[joint_type[h]] - pred_joint_coord_cam[root_joint_idx[h],None,:]
# print("^^^^^^^^^^^^^^^^^^^^^^^^^^^")
# print(pred_joint_coord_cam.shape)
print(img_path)
cvimg = cv2.imread(img_path, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
_img = cvimg[:,:,::-1].transpose(2,0,1)
vis_kps = pred_joint_coord_img.copy()
vis_valid = joint_valid.copy()
filename = img_path.replace(".jpg", "2D.jpg").replace("main/custom_data", "custom_output")
vis_keypoints(_img, vis_kps, joint_valid, self.skeleton, filename)
filename = img_path.replace(".jpg", "3D.jpg").replace("main/custom_data", "custom_output")
vis_3d_keypoints(pred_joint_coord_img, joint_valid, self.skeleton, filename)
print("Finished Processing Image!!!!" + "\n")