diff --git a/posegait/src/runworking.py b/posegait/src/runworking.py
new file mode 100644
index 0000000..6c01eee
--- /dev/null
+++ b/posegait/src/runworking.py
@@ -0,0 +1,126 @@
+import argparse
+import logging
+import time
+import os
+import ast
+
+import common
+import cv2
+import numpy as np
+from estimator import TfPoseEstimator
+from networks import get_graph_path, model_wh
+import matplotlib.pyplot as plt
+from lifting.prob_model import Prob3dPose
+from lifting.draw import plot_pose
+
+logger = logging.getLogger('TfPoseEstimator')
+logger.setLevel(logging.DEBUG)
+ch = logging.StreamHandler()
+ch.setLevel(logging.DEBUG)
+formatter = logging.Formatter('[%(asctime)s] [%(name)s] [%(levelname)s] %(message)s')
+ch.setFormatter(formatter)
+logger.addHandler(ch)
+
+
+if __name__ == '__main__':
+    #print(os.getcwd())
+    #os.chdir('..')
+    #print(os.getcwd())
+    #import sys
+    #sys.exit(0)
+    #os.chdir('..')
+    parser = argparse.ArgumentParser(description='tf-pose-estimation run')
+    parser.add_argument('--image', type=str, default='dhoni.jpg')
+    parser.add_argument('--resolution', type=str, default='432x368', help='network input resolution. default=432x368')
+    parser.add_argument('--model', type=str, default='mobilenet_thin', help='cmu / mobilenet_thin')
+    parser.add_argument('--scales', type=str, default='[None]', help='for multiple scales, eg. [1.0, (1.1, 0.05)]')
+    args = parser.parse_args()
+    scales = ast.literal_eval(args.scales)
+
+    w, h = model_wh(args.resolution)
+    e = TfPoseEstimator(get_graph_path(args.model), target_size=(w, h))
+
+    # estimate human poses from a single image !
+    image = common.read_imgfile(args.image, None, None)
+    # image = cv2.fastNlMeansDenoisingColored(image, None, 10, 10, 7, 21)
+    t = time.time()
+    humans = e.inference(image, scales=scales)
+    elapsed = time.time() - t
+
+    logger.info('inference image: %s in %.4f seconds.' % (args.image, elapsed))
+
+    image = TfPoseEstimator.draw_humans(image, humans, imgcopy=False)
+    cv2.imshow('tf-pose-estimation result', image)
+    #cv2.waitKey()
+
+    fig1 = plt.figure(1)
+    a = fig1.add_subplot(2, 2, 1)
+    a.set_title('Result')
+    plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+
+    bgimg = cv2.cvtColor(image.astype(np.uint8), cv2.COLOR_BGR2RGB)
+    bgimg = cv2.resize(bgimg, (e.heatMat.shape[1], e.heatMat.shape[0]), interpolation=cv2.INTER_AREA)
+
+    # show network output
+    a = fig1.add_subplot(2, 2, 2)
+    plt.imshow(bgimg, alpha=0.5)
+    tmp = np.amax(e.heatMat[:, :, :-1], axis=2)
+    plt.imshow(tmp, cmap=plt.cm.gray, alpha=0.5)
+    plt.colorbar()
+
+    tmp2 = e.pafMat.transpose((2, 0, 1))
+    tmp2_odd = np.amax(np.absolute(tmp2[::2, :, :]), axis=0)
+    tmp2_even = np.amax(np.absolute(tmp2[1::2, :, :]), axis=0)
+
+    a = fig1.add_subplot(2, 2, 3)
+    a.set_title('Vectormap-x')
+    #plt.imshow(CocoPose.get_bgimg(inp, target_size=(vectmap.shape[1], vectmap.shape[0])), alpha=0.5)
+    #plt.imshow(tmp2_odd, cmap=plt.cm.gray, alpha=0.5)
+    #plt.colorbar()
+
+    a = fig1.add_subplot(2, 2, 4)
+    a.set_title('Vectormap-y')
+    #plt.imshow(CocoPose.get_bgimg(inp, target_size=(vectmap.shape[1], vectmap.shape[0])), alpha=0.5)
+    plt.imshow(tmp2_even, cmap=plt.cm.gray, alpha=0.5)
+    plt.colorbar()
+    #plt.show()
+
+    #import sys
+    #sys.exit(0)
+
+    logger.info('3d lifting initialization.')
+    poseLifting = Prob3dPose('./lifting/models/prob_model_params.mat')
+
+    image_h, image_w = image.shape[:2]
+    standard_w = 640
+    standard_h = 480
+
+    fig2 = plt.figure(2)   
+    pose_2d_mpiis = []
+    visibilities = []
+    for human in humans:
+        pose_2d_mpii, visibility = common.MPIIPart.from_coco(human)
+        pose_2d_mpiis.append([(int(x * standard_w + 0.5), int(y * standard_h + 0.5)) for x, y in pose_2d_mpii])
+        visibilities.append(visibility)
+
+    pose_2d_mpiis = np.array(pose_2d_mpiis)
+    visibilities = np.array(visibilities)
+    transformed_pose2d, weights = poseLifting.transform_joints(pose_2d_mpiis, visibilities)
+    pose_3d = poseLifting.compute_3d(transformed_pose2d, weights)
+    lis_3d = pose_3d.tolist()
+    print('list_3d', lis_3d)
+    with open('C:\\Users\\carti\\Desktop\\tf-pose\\src\\img_3d.txt', 'a') as f:
+        for item in lis_3d:
+            f.write("%s\n" % item)
+    #print(pose_3d)
+    
+    pose_3dqt = np.array(pose_3d[0]).transpose()
+    
+    for point in pose_3dqt:
+        print(point)
+
+    for i, single_3d in enumerate(pose_3d):
+        plot_pose(single_3d)
+        plt.show()
+
+    pass