def infer_fg(self, img):
"""
img: BGR image of shape (H, W, C)
returns: binary mask image of shape (H, W), 255 for fg, 0 for bg
"""
ori_h, ori_w = img.shape[0:2]
new_h, new_w = self.get_working_size(ori_h, ori_w)
img = cv2.resize(img, (new_w, new_h))
# Get results of original image
multiplier = get_multiplier(img)
with torch.no_grad():
orig_paf, orig_heat = get_outputs(multiplier, img, self.model,
'rtpose')
# Get results of flipped image
swapped_img = img[:, ::-1, :]
flipped_paf, flipped_heat = get_outputs(multiplier, swapped_img,
self.model, 'rtpose')
# compute averaged heatmap and paf
paf, heatmap = handle_paf_and_heat(orig_heat, flipped_heat,
orig_paf, flipped_paf)
param = {'thre1': 0.1, 'thre2': 0.05, 'thre3': 0.5}
to_plot, canvas, candidate, subset = decode_pose_fg(
img, param, heatmap, paf)
canvas = cv2.resize(canvas, (ori_w, ori_h))
fg_map = canvas > 128
canvas[fg_map] = 255
canvas[~fg_map] = 0
return canvas[:, :, 0]
def process(model, oriImg, process_speed):
# Get results of original image
multiplier = get_multiplier(oriImg, process_speed)
with torch.no_grad():
orig_paf, orig_heat = get_outputs(multiplier, oriImg, model, 'rtpose')
# Get results of flipped image
swapped_img = oriImg[:, ::-1, :]
flipped_paf, flipped_heat = get_outputs(multiplier, swapped_img, model,
'rtpose')
# compute averaged heatmap and paf
paf, heatmap = handle_paf_and_heat(orig_heat, flipped_heat, orig_paf,
flipped_paf)
param = {'thre1': 0.1, 'thre2': 0.05, 'thre3': 0.5}
to_plot, canvas, joint_list, person_to_joint_assoc = decode_pose(
oriImg, param, heatmap, paf)
return to_plot, canvas, joint_list, person_to_joint_assoc
model = get_model(trunk='vgg19')
model = torch.nn.DataParallel(model).cuda()
model.load_state_dict(torch.load(weight_name))
model.float()
model.eval()
model = model.cuda()
test_image = 'kids.jpg'
oriImg = cv2.imread(test_image) # B,G,R order
shape_dst = np.min(oriImg.shape[0:2])
# Get results of original image
multiplier = get_multiplier(oriImg)
with torch.no_grad():
orig_paf, orig_heat = get_outputs(multiplier, oriImg, model, 'rtpose')
# Get results of flipped image
swapped_img = oriImg[:, ::-1, :]
flipped_paf, flipped_heat = get_outputs(multiplier, swapped_img, model,
'rtpose')
# compute averaged heatmap and paf
paf, heatmap = handle_paf_and_heat(orig_heat, flipped_heat, orig_paf,
flipped_paf)
param = {'thre1': 0.1, 'thre2': 0.05, 'thre3': 0.5}
canvas, to_plot, candidate, subset = decode_pose(oriImg, param, heatmap, paf)
cv2.imwrite('result_photo.png', to_plot)
test_img_path = test.joinpath('test_set')
test_img_name = "image%0d.jpg" % idx
test_img_path = test_img_path.joinpath(test_img_name)
test_image = cv2.resize( cv2.imread(str(test_img_path)), (512, 512))
test_multiplier = get_multiplier(test_image)
with torch.no_grad():
train_paf, train_heatmap = get_outputs(train_multiplier, train_image, model, 'rtpose')
test_paf, test_heatmap = get_outputs(test_multiplier, test_image, model, 'rtpose')
# use [::-1] to reverse!
train_swapped_img = train_image[:, ::-1, :]
test_swapped_img = test_image[:, ::-1, :]
train_flipped_paf, train_flipped_heat = get_outputs(train_multiplier, train_swapped_img, model, 'rtpose')
test_flipped_paf, test_flipped_heat = get_outputs(test_multiplier, test_swapped_img, model, 'rtpose')
train_paf, train_heatmap = handle_paf_and_heat(train_heatmap, train_flipped_heat, train_paf, train_flipped_paf)
test_paf, test_heatmap = handle_paf_and_heat(test_heatmap, test_flipped_heat, test_paf, test_flipped_paf)
param = {'thre1': 0.1, 'thre2': 0.05, 'thre3': 0.5}
train_pose = get_pose(param, train_heatmap, train_paf)
test_pose = get_pose(param, test_heatmap, test_paf)
pose_name = "pose%0d.jpg" % idx
cv2.imwrite(str(test_pose_dir.joinpath(pose_name)), test_pose)
cv2.imwrite(str(train_pose_dir.joinpath(pose_name)), train_pose)
