diff --git a/README.md b/README.md
index ca0c55f..036566d 100755
--- a/README.md
+++ b/README.md
@@ -18,6 +18,8 @@ write back to a pickle file which is used in a TensorFlow model as initial weigh
 from the authors' repo.
 
 2. Convert the model to a pickle file by:
+
+#before running the below command make a blank mat2tf.pkl file
 ```
 python matconvnet_hr101_to_pickle.py 
         --matlab_model_path /path/to/pretrained_model 
@@ -36,6 +38,13 @@ python tiny_face_eval.py
   --data_dir /path/to/input_image_directory
   --output_dir /path/to/output_directory
 ```
+## Tesing Tiny Face Detector in TensorFlow with webcam
+
+Run:
+```
+python tiny_face_eval_webcam.py
+  --weight_file_path /path/to/pickle_file
+```
 
 # Neural network diagram
 
diff --git a/tiny_face_eval_webcam.py b/tiny_face_eval_webcam.py
new file mode 100644
index 0000000..6deda54
--- /dev/null
+++ b/tiny_face_eval_webcam.py
@@ -0,0 +1,219 @@
+# -*- coding: utf-8 -*-
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+import tiny_face_model
+import util
+from argparse import ArgumentParser
+import cv2
+import scipy.io
+import numpy as np
+import matplotlib.pyplot as plt
+import cv2
+import pickle
+import sys 
+
+import pylab as pl
+import time
+import os
+import sys
+from scipy.special import expit
+import glob
+
+MAX_INPUT_DIM = 5000.0
+
+def overlay_bounding_boxes(raw_img, refined_bboxes, lw):
+  """Overlay bounding boxes of face on images.
+    Args:
+      raw_img:
+        A target image.
+      refined_bboxes:
+        Bounding boxes of detected faces.
+      lw: 
+        Line width of bounding boxes. If zero specified,
+        this is determined based on confidence of each detection.
+    Returns:
+      None.
+  """
+
+  # Overlay bounding boxes on an image with the color based on the confidence.
+  for r in refined_bboxes:
+    _score = expit(r[4])
+    cm_idx = int(np.ceil(_score * 255))
+    rect_color = [int(np.ceil(x * 255)) for x in util.cm_data[cm_idx]]  # parula
+    _lw = lw
+    if lw == 0:  # line width of each bounding box is adaptively determined.
+      bw, bh = r[2] - r[0] + 1, r[3] - r[0] + 1
+      _lw = 1 if min(bw, bh) <= 20 else max(2, min(3, min(bh / 20, bw / 20)))
+      _lw = int(np.ceil(_lw * _score))
+
+    _r = [int(x) for x in r[:4]]
+    cv2.rectangle(raw_img, (_r[0], _r[1]), (_r[2], _r[3]), rect_color, _lw)
+    
+    
+def evaluate(weight_file_path,prob_thresh=0.5, nms_thresh=0.1, lw=3, display=False):
+  """Detect faces in images.
+  Args:
+    prob_thresh:
+        The threshold of detection confidence.
+    nms_thresh:
+        The overlap threshold of non maximum suppression
+    weight_file_path: 
+        A pretrained weight file in the pickle format 
+        generated by matconvnet_hr101_to_tf.py.
+    data_dir: 
+        A directory which contains images.
+    output_dir: 
+        A directory into which images with detected faces are output.
+    lw: 
+        Line width of bounding boxes. If zero specified,
+        this is determined based on confidence of each detection.
+    display:
+        Display tiny face images on window.
+  Returns:
+    None.
+  """
+
+  # placeholder of input images. Currently batch size of one is supported.
+  x = tf.placeholder(tf.float32, [1, None, None, 3]) # n, h, w, c
+
+  # Create the tiny face model which weights are loaded from a pretrained model.
+  model = tiny_face_model.Model(weight_file_path)
+  score_final = model.tiny_face(x)
+
+  
+  with open(weight_file_path, "rb") as f:
+    _, mat_params_dict = pickle.load(f)
+
+  average_image = model.get_data_by_key("average_image")
+  clusters = model.get_data_by_key("clusters")
+  clusters_h = clusters[:, 3] - clusters[:, 1] + 1
+  clusters_w = clusters[:, 2] - clusters[:, 0] + 1
+  normal_idx = np.where(clusters[:, 4] == 1)
+
+  # main
+  with tf.Session() as sess:
+    sess.run(tf.global_variables_initializer())
+
+    #for filename in filenames:
+      #fname = filename.split(os.sep)[-1]
+    video_capture = cv2.VideoCapture(0)
+    while True:
+      # Capture frame-by-frame
+      ret, frame = video_capture.read()
+      #raw_img = cv2.imread(filename)
+      raw_img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+      raw_img_f = raw_img.astype(np.float32)
+
+      def _calc_scales():
+        raw_h, raw_w = raw_img.shape[0], raw_img.shape[1]
+        min_scale = min(np.floor(np.log2(np.max(clusters_w[normal_idx] / raw_w))),
+                        np.floor(np.log2(np.max(clusters_h[normal_idx] / raw_h))))
+        max_scale = min(1.0, -np.log2(max(raw_h, raw_w) / MAX_INPUT_DIM))
+        scales_down = pl.frange(min_scale, 0, 1.)
+        scales_up = pl.frange(0.5, max_scale, 0.5)
+        scales_pow = np.hstack((scales_down, scales_up))
+        scales = np.power(2.0, scales_pow)
+        return scales
+
+      scales = _calc_scales()
+      start = time.time()
+
+      # initialize output
+      bboxes = np.empty(shape=(0, 5))
+
+      # process input at different scales
+      for s in scales[:2]:
+        #print("Processing {} at scale {:.4f}".format(fname, s))
+        img = cv2.resize(raw_img_f, (0, 0), fx=s, fy=s, interpolation=cv2.INTER_LINEAR)
+        img = img - average_image
+        img = img[np.newaxis, :]
+
+        # we don't run every template on every scale ids of templates to ignore
+        tids = list(range(4, 12)) + ([] if s <= 1.0 else list(range(18, 25)))
+        ignoredTids = list(set(range(0, clusters.shape[0])) - set(tids))
+
+        # run through the net
+        score_final_tf = sess.run(score_final, feed_dict={x: img})
+
+        # collect scores
+        score_cls_tf, score_reg_tf = score_final_tf[:, :, :, :25], score_final_tf[:, :, :, 25:125]
+        prob_cls_tf = expit(score_cls_tf)
+        prob_cls_tf[0, :, :, ignoredTids] = 0.0
+
+        def _calc_bounding_boxes():
+          # threshold for detection
+          _, fy, fx, fc = np.where(prob_cls_tf > prob_thresh)
+
+          # interpret heatmap into bounding boxes
+          cy = fy * 8 - 1
+          cx = fx * 8 - 1
+          ch = clusters[fc, 3] - clusters[fc, 1] + 1
+          cw = clusters[fc, 2] - clusters[fc, 0] + 1
+
+          # extract bounding box refinement
+          Nt = clusters.shape[0]
+          tx = score_reg_tf[0, :, :, 0:Nt]
+          ty = score_reg_tf[0, :, :, Nt:2*Nt]
+          tw = score_reg_tf[0, :, :, 2*Nt:3*Nt]
+          th = score_reg_tf[0, :, :, 3*Nt:4*Nt]
+
+          # refine bounding boxes
+          dcx = cw * tx[fy, fx, fc]
+          dcy = ch * ty[fy, fx, fc]
+          rcx = cx + dcx
+          rcy = cy + dcy
+          rcw = cw * np.exp(tw[fy, fx, fc])
+          rch = ch * np.exp(th[fy, fx, fc])
+
+          scores = score_cls_tf[0, fy, fx, fc]
+          tmp_bboxes = np.vstack((rcx - rcw / 2, rcy - rch / 2, rcx + rcw / 2, rcy + rch / 2))
+          tmp_bboxes = np.vstack((tmp_bboxes / s, scores))
+          tmp_bboxes = tmp_bboxes.transpose()
+          return tmp_bboxes
+
+        tmp_bboxes = _calc_bounding_boxes()
+        bboxes = np.vstack((bboxes, tmp_bboxes)) # <class 'tuple'>: (5265, 5)
+
+
+      refind_idx = tf.image.non_max_suppression(tf.convert_to_tensor(bboxes[:, :4], dtype=tf.float32),
+                                                   tf.convert_to_tensor(bboxes[:, 4], dtype=tf.float32),
+                                                   max_output_size=bboxes.shape[0], iou_threshold=nms_thresh)
+      refind_idx = sess.run(refind_idx)
+      refined_bboxes = bboxes[refind_idx]
+      overlay_bounding_boxes(raw_img, refined_bboxes, lw)
+
+      
+      # save image with bounding boxes
+      raw_img = cv2.cvtColor(raw_img, cv2.COLOR_RGB2BGR)
+      # Display the resulting frame
+      cv2.imshow('Video', raw_img)
+
+      if cv2.waitKey(1) & 0xFF == ord('q'):
+          break
+     
+    # When everything is done, release the capture
+    video_capture.release()
+    cv2.destroyAllWindows()
+
+#weight_file_path='/Users/apple/Downloads/Tiny_Faces_in_Tensorflow-master/mat2tf.pkl'
+#evaluate(weight_file_path)
+def main():
+
+  argparse = ArgumentParser()
+  argparse.add_argument('--weight_file_path', type=str, help='Pretrained weight file.', default="/path/to/mat2tf.pkl")
+
+  args = argparse.parse_args()
+
+  # check arguments
+  assert os.path.exists(args.weight_file_path), "weight file: " + args.weight_file_path + " not found."
+
+  with tf.Graph().as_default():
+    evaluate(
+      weight_file_path=args.weight_file_path)
+      
+
+if __name__ == '__main__':
+  main()