Humandetection.py

import numpy as np
import tensorflow as tf
import cv2
import time


class DetectorApi:
    def __init__(self, path_to_ckpt):
        self.path_to_ckpt = path_to_ckpt
        self.detection_graph = tf.Graph()
        with self.detection_graph.as_default():
            oldgaph = tf.Graph()
            with tf.io.gfile.GFile(self.path_to_ckpt, 'rb') as fid:
                serialized_graph = fid.read()
                tf.import_graph_def(oldgaph, name='')

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

        # Definite input and output Tensors for detection_graph
        self.image_tensor = self.detection_graph.get_tensor_by_name('image_tensor:0')
        # Each box represents a part of the image where a particular object was detected.
        self.detection_boxes = self.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.
        self.detection_scores = self.detection_graph.get_tensor_by_name('detection_scores:0')
        self.detection_classes = self.detection_graph.get_tensor_by_name('detection_classes:0')
        self.num_detections = self.detection_graph.get_tensor_by_name('num_detections:0')

    def processFrame(self, image):
        # Expand dimensions since the trained_model expects images to have shape: [1, None, None, 3]
        image_np_expanded = np.expand_dims(image, axis=0)
        # Actual detection.
        start_time = time.time()
        (boxes, scores, classes, num) = self.sess.run(
            [self.detection_boxes, self.detection_scores, self.detection_classes, self.num_detections],
            feed_dict={self.image_tensor: image_np_expanded})
        end_time = time.time()

        print("Elapsed Time:", end_time - start_time)

        im_height, im_width, _ = image.shape
        boxes_list = [None for i in range(boxes.shape[1])]
        for i in range(boxes.shape[1]):
            boxes_list[i] = (int(boxes[0, i, 0] * im_height),
                             int(boxes[0, i, 1] * im_width),
                             int(boxes[0, i, 2] * im_height),
                             int(boxes[0, i, 3] * im_width))

        return boxes_list, scores[0].tolist(), [int(x) for x in classes[0].tolist()], int(num[0])

    def close(self):
        self.sess.close()
        self.default_graph.close()

if __name__ == "__main__":
        model_path = '/home/frozen_inference_graph.pb'
        odapi = DetectorApi(path_to_ckpt=model_path)
        threshold = 0.7
        cap = cv2.VideoCapture('video/Stupid Robbery Fails Compilation 2019.mp4')

        while True:
            r, img = cap.read()
            try:
                img = cv2.resize(img, (1280, 720), interpolation=cv2.INTER_AREA)
                print(img.shape)
            except:
                break
            height, width, layers = img.shape
            size = (width, height)
            print(size)

            boxes, scores, classes, num = odapi.processFrame(img)

            # Visualization of the results of a detection.

            for i in range(len(boxes)):
                # Class 1 represents human
                if classes[i] == 1 and scores[i] > threshold:
                    box = boxes[i]
                    cv2.rectangle(img, (box[1], box[0]), (box[3], box[2]), (255, 0, 0), 2)

            cv2.imshow(img)
            key = cv2.waitKey(1)
            if key & 0xFF == ord('q'):
                break