Python Code for object detection using TensorFlow

This is the code for the video shown in this link. For this program to run properly, tensorflow must be installed on your computer with all related dependencies

Follow this guide to install tensorflow

Dependencies needed for this code to run

• Tensorflow (CPU or GPU version)

• opencv

• pillow

• lxml

• matplotlib

You can follow this excellent tutorial to install all dependencies.

The label file was downloaded from tensorflow official github repo, link can be found here

If you don’t find it, I’ve kept a copy in my one drive for code, frozen graph as well as the label file which can be found here

Code

import numpy as npimport osimport six.moves.urllib as urllibimport sysimport tarfileimport tensorflow as tfimport zipfilefrom collections import defaultdictfrom io import StringIOfrom matplotlib import pyplot as pltfrom PIL import Imageimport cv2cap = cv2.VideoCapture(0)# This is needed since the notebook is stored in the object_detection folder.sys.path.append("..")# ## Object detection imports# Here are the imports from the object detection module.# In[3]:from object_detection.utils import label_map_utilfrom object_detection.utils import visualization_utils as vis_util# # Model preparation # ## Variables# # 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.# In[4]:# 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 = 'C:/Users/Automation/Documents/TensorFlow/workspace/tfdemo/frozen_inference_graph.pb'# List of the strings that is used to add correct label for each box.PATH_TO_LABELS = 'C:/Users/Automation/Documents/TensorFlow/workspace/tfdemo/mscoco_label_map.pbtxt'NUM_CLASSES = 90# ## Download Model# In[5]:'''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())'''# 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 finelabel_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)# Helper codedef load_image_into_numpy_array(image): (im_width, im_height) = image.size return np.array(image.getdata()).reshape( (im_height, im_width, 3)).astype(np.uint8)# Detectionwith detection_graph.as_default(): with tf.Session(graph=detection_graph) as sess: while True: # Read frame from camera ret, image_np = cap.read() # Expand dimensions since the model expects images to have shape: [1, None, None, 3] image_np_expanded = np.expand_dims(image_np, axis=0) # Extract image tensor image_tensor = detection_graph.get_tensor_by_name('image_tensor:0') # Extract detection boxes boxes = detection_graph.get_tensor_by_name('detection_boxes:0') # Extract detection scores scores = detection_graph.get_tensor_by_name('detection_scores:0') # Extract detection classes classes = detection_graph.get_tensor_by_name('detection_classes:0') # Extract number of detectionsd num_detections = detection_graph.get_tensor_by_name( 'num_detections:0') # 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(classes).astype(np.int32), np.squeeze(scores), category_index, use_normalized_coordinates=True, line_thickness=8) 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 mytxt = "No of Objects:" + str(count) cv2.putText(image_np,mytxt,(0,130), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2, cv2.LINE_AA) # Display output cv2.imshow('object detection', cv2.resize(image_np, (800, 600))) if cv2.waitKey(25) & 0xFF == ord('q'): cap.release() cv2.destroyAllWindows() break