camera.py

# Import packages
import os
import cv2
import numpy as np
import tensorflow as tf
import sys

# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")

# Import utilites
from utils import label_map_util
from utils import visualization_utils as vis_util

class VideoCamera(object):
    def __init__(self):
        # Using OpenCV to capture from device 0. If you have trouble capturing
        # from a webcam, comment the line below out and use a video file
        # instead.
        self.video = cv2.VideoCapture(1)
        # If you decide to use video.mp4, you must have this file in the folder
        # as the main.py.
        # self.video = cv2.VideoCapture('isis.mp4')
        # Name of the directory containing the object detection module we're using
        MODEL_NAME = 'inference_graph'

        # 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,'training','labelmap.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 our convolution
        # network predicts `5`, we know that this corresponds to `king`.
        # 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)
        self.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='')

            self.sess = tf.Session(graph=detection_graph)


        # Define input and output tensors (i.e. data) for the object detection classifier

        # Input tensor is the image
        self.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
        self.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.
        self.detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
        self.detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')

        # Number of objects detected
        self.num_detections = detection_graph.get_tensor_by_name('num_detections:0')
        
            
    
    def __del__(self):
        self.video.release()
    
    def get_frame(self):
        # 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

        ret, frame = self.video.read()

        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) = self.sess.run(
            [self.detection_boxes, self.detection_scores, self.detection_classes, self.num_detections],
            feed_dict={self.image_tensor: frame_expanded})

        # Draw the results of the detection (aka 'visulaize the results')
        vis_util.visualize_boxes_and_labels_on_image_array(
            frame,
            np.squeeze(boxes),
            np.squeeze(classes).astype(np.int32),
            np.squeeze(scores),
            self.category_index,
            use_normalized_coordinates=True,
            line_thickness=8,
            min_score_thresh=0.60)

        # We are using Motion JPEG, but OpenCV defaults to capture raw images,
        # so we must encode it into JPEG in order to correctly display the
        # video stream.
        ret, jpeg = cv2.imencode('.jpg', frame)
        return jpeg.tobytes()