TensorFlowPeopleTracker.py

# Code adapted from Tensorflow Object Detection Framework
# https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb
# Tensorflow Object Detection Detector

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

from imutils.video import FPS

import UtilsIO
import config
import VizionFTrackers


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():
            od_graph_def = tf.GraphDef()
            with tf.gfile.GFile(self.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.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 = UtilsIO.TRAINED_MODEL_TENSORFLOW
    odapi = DetectorAPI(path_to_ckpt=model_path)
    threshold = 0.7
    cap = cv2.VideoCapture(UtilsIO.SAMPLE_FILE_NAME_2)

    totalFrames = 0
    initBB = None

    while True:
        r, img = cap.read()
        img = cv2.resize(img, (1280, 720))

        if totalFrames % 10 == 0:

            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)

                    tracker = VizionFTrackers.createTracker(VizionFTrackers.MOSSE_TRACKER)
                    if initBB is not None:
                        # grab the new bounding box coordinates of the object
                        (success, box) = tracker.update(img)
                        print("success", success)
                        # check to see if the tracking was a success
                        if success:
                            (x, y, w, h) = [int(v) for v in box]
                            cv2.rectangle(img, (x, y), (x + w, y + h),
                                          (0, 255, 0), 2)

                        # update the FPS counter
                        fps.update()
                        fps.stop()
                        VizionFTrackers.setFrameInfo(VizionFTrackers.MOSSE_TRACKER, success, fps, img)
        cv2.imshow("preview", img)
        key = cv2.waitKey(1)
        if key & 0xFF == ord('q'):
            break

        if key == ord("s"):
            # select the bounding box of the object we want to track (make
            # sure you press ENTER or SPACE after selecting the ROI)
            initBB = cv2.selectROI("Frame", img, fromCenter=False,
                                   showCrosshair=False)
            # start OpenCV object tracker using the supplied bounding box
            # coordinates, then start the FPS throughput estimator as well
            tracker.init(img, initBB)
            fps = FPS().start()

        elif key == ord("q"):
            break

        totalFrames += 1