Create Object_counting.py

Author: vineeth2628

Object_counting.py

@@ -0,0 +1,155 @@
+######## Object counting using tensorflow on picamera #########
+# Author: Vineeth Rajendran
+# Date: 4/1/19
+# Description: 
+# This program uses a TensorFlow classifier to perform object detection and counting.
+# It draws boxes and scores around the objects of interest in each frame from the picamera.
+# IT also uses a variable for counting the no. of objects in the frame.
+
+## Some of the code is copied from Evan Juras example at
+## https://github.com/EdjeElectronics/TensorFlow-Object-Detection-on-the-Raspberry-Pi/blob/master/Object_detection_picamera.py
+
+
+# Import packages
+import os
+import cv2
+import numpy as np
+from picamera.array import PiRGBArray
+from picamera import PiCamera
+import tensorflow as tf
+import argparse
+import sys
+
+# Set up camera constants
+IM_WIDTH = 1280
+IM_HEIGHT = 720
+#IM_WIDTH = 640    Use smaller resolution for
+#IM_HEIGHT = 480   slightly faster framerate
+
+# Select camera type (if user enters --usbcam when calling this script,
+# a USB webcam will be used)
+camera_type = 'picamera'
+parser = argparse.ArgumentParser()
+parser.add_argument('--usbcam', help='Use a USB webcam instead of picamera',
+                    action='store_true')
+args = parser.parse_args()
+if args.usbcam:
+    camera_type = 'usb'
+
+# This is needed since the working directory is the object_detection folder.
+sys.path.append('..')
+
+# Import utilites
+from utils import label_map_util
+from utils import visualization_utils as vis_util
+
+# Name of the directory containing the object detection module we're using
+MODEL_NAME = 'ssdlite_mobilenet_v2_coco_2018_05_09'
+
+# Grab path to current working directory
+CWD_PATH = os.getcwd()
+
+# Path to frozen detection graph .pb file, which contains the model that is used
+# for object detection.
+PATH_TO_CKPT = os.path.join(CWD_PATH,MODEL_NAME,'frozen_inference_graph.pb')
+
+# Path to label map file
+PATH_TO_LABELS = os.path.join(CWD_PATH,'data','mscoco_label_map.pbtxt')
+
+# Number of classes the object detector can identify
+NUM_CLASSES = 90
+
+## Load the label map.
+# Label maps map indices to category names, so that when the 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)
+
+# Load the 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='')
+
+    sess = tf.Session(graph=detection_graph)
+
+
+# Define input and output tensors (i.e. data) for the object detection classifier
+
+# Input tensor is the image
+image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
+
+# Output tensors are the detection boxes, scores, and classes
+# Each box represents a part of the image where a particular object was detected
+detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
+
+# Each score represents level of confidence for each of the objects.
+# The score is shown on the result image, together with the class label.
+detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
+detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
+
+# Number of objects detected
+num_detections = detection_graph.get_tensor_by_name('num_detections:0')
+
+# creating a fucntion 
+def object_counting()
+    # Initialize Picamera and grab reference to the raw capture
+    camera = PiCamera()
+    camera.resolution = (IM_WIDTH,IM_HEIGHT)
+    camera.framerate = 10
+    rawCapture = PiRGBArray(camera, size=(IM_WIDTH,IM_HEIGHT))
+    rawCapture.truncate(0)
+
+    for frame1 in camera.capture_continuous(rawCapture, format="bgr",use_video_port=True):
+
+        totalcount=0
+        
+        # Acquire frame and expand frame dimensions to have shape: [1, None, None, 3]
+        # i.e. a single-column array, where each item in the column has the pixel RGB value
+        frame = np.copy(frame1.array)
+        frame.setflags(write=1)
+        frame_expanded = np.expand_dims(frame, axis=0)
+
+        # Perform the actual detection by running the model with the image as input
+        (boxes, scores, classes, num) = sess.run(
+            [detection_boxes, detection_scores, detection_classes, num_detections],
+            feed_dict={image_tensor: frame_expanded})
+
+        # Visualizing the results of the detection
+        vis_util.visualize_boxes_and_labels_on_image_array(
+            frame,
+            np.squeeze(boxes),
+            np.squeeze(classes).astype(np.int32),
+            np.squeeze(scores),
+            category_index,
+            use_normalized_coordinates=True,
+            line_thickness=8,
+            min_score_thresh=0.40)
+
+	      # Updating totalcount
+        totalcount=totalcount+num
+        
+	      # Displaying the number of objects detected using the totalcount variable
+        cv2.putText(frame,"Count"+str(totalcount) ,(10, 35),font,0.8,(0, 0xFF, 0xFF),2,cv2.FONT_HERSHEY_SIMPLEX,)
+
+        # All the results have been drawn on the frame, so it's time to display it.
+        cv2.imshow('Object detector', frame)
+
+        # Press 'q' to quit
+        if cv2.waitKey(1) == ord('q'):
+            break
+
+        rawCapture.truncate(0)
+
+    camera.close()
+
+cv2.destroyAllWindows()
+
+object_counting()
+