Create Using object detection code to only detect person

Using object detection code to only detect person

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+import numpy as np
+import os
+import six.moves.urllib as urllib
+import sys
+import tarfile
+import tensorflow as tf
+import zipfile
+
+from collections import defaultdict
+from io import StringIO
+from matplotlib import pyplot as plt
+from PIL import Image
+
+
+from utils import label_map_util
+
+from utils import visualization_utils as vis_util
+
+
+# # Model preparation 
+# Any model exported using the `export_inference_graph.py` tool can be loaded here simply by changing `PATH_TO_CKPT` to point to a new .pb file.  
+# By default we use an "SSD with Mobilenet" model here. See the [detection model zoo](https://github.com/tensorflow/models/blob/master/object_detection/g3doc/detection_model_zoo.md) for a list of other models that can be run out-of-the-box with varying speeds and accuracies.
+
+# What model to download.
+MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017'
+MODEL_FILE = MODEL_NAME + '.tar.gz'
+DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'
+
+# Path to frozen detection graph. This is the actual model that is used for the object detection.
+PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'
+
+# List of the strings that is used to add correct label for each box.
+PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')
+
+NUM_CLASSES = 90
+
+
+# ## Download Model
+
+if not os.path.exists(MODEL_NAME + '/frozen_inference_graph.pb'):
+    print ('Downloading the model')
+    opener = urllib.request.URLopener()
+    opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)
+    tar_file = tarfile.open(MODEL_FILE)
+    for file in tar_file.getmembers():
+      file_name = os.path.basename(file.name)
+      if 'frozen_inference_graph.pb' in file_name:
+        tar_file.extract(file, os.getcwd())
+    print ('Download complete')
+else:
+    print ('Model already exists')
+
+# ## Load a (frozen) Tensorflow model into memory.
+
+detection_graph = tf.Graph()
+with detection_graph.as_default():
+  od_graph_def = tf.GraphDef()
+  with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
+    serialized_graph = fid.read()
+    od_graph_def.ParseFromString(serialized_graph)
+    tf.import_graph_def(od_graph_def, name='')
+
+
+# ## Loading label map
+# Label maps map indices to category names, so that when our convolution network predicts `5`, we know that this corresponds to `airplane`.  Here we use internal utility functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine
+
+label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
+categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
+category_index = label_map_util.create_category_index(categories)
+
+#intializing the web camera device
+
+import cv2
+cap = cv2.VideoCapture(0)
+
+# Running the tensorflow session
+with detection_graph.as_default():
+  with tf.Session(graph=detection_graph) as sess:
+   ret = True
+   while (ret):
+      ret,image_np = cap.read()
+      image_np = cv2.resize(image_np,(600,400))
+      # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
+      image_np_expanded = np.expand_dims(image_np, axis=0)
+      image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
+      # Each box represents a part of the image where a particular object was detected.
+      boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
+      # Each score represent how level of confidence for each of the objects.
+      # Score is shown on the result image, together with the class label.
+      scores = detection_graph.get_tensor_by_name('detection_scores:0')
+      classes = detection_graph.get_tensor_by_name('detection_classes:0')
+      num_detections = detection_graph.get_tensor_by_name('num_detections:0')
+
+      b = [x for x in classes if x == 1]
+      # Actual detection.
+      (boxes, scores, classes, num_detections) = sess.run(
+          [boxes, scores, classes, num_detections],
+          feed_dict={image_tensor: image_np_expanded})
+      # Visualization of the results of a detection.
+
+      vis_util.visualize_boxes_and_labels_on_image_array(
+          image_np,
+          np.squeeze(boxes),
+          np.squeeze(b).astype(np.int32),
+          np.squeeze(scores),
+          category_index,
+          use_normalized_coordinates=True,
+          line_thickness=8)
+
+      #print (len(boxes.shape))
+
+      #print (classes)
+
+      final_score = np.squeeze(scores)    
+      count = 0
+      for i in range(100):
+          if scores is None or final_score[i] > 0.5:
+                  count = count + 1
+                  print (count, ' object(s) detected...')
+
+#      plt.figure(figsize=IMAGE_SIZE)
+#      plt.imshow(image_np)
+      cv2.imshow('image',image_np)
+      if cv2.waitKey(200) & 0xFF == ord('q'):
+          cv2.destroyAllWindows()
+          cap.release()
+          break