inference_usbCam_face.py

#!/usr/bin/python
# -*- coding: utf-8 -*-
# pylint: disable=C0103
# pylint: disable=E1101

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

from utils import label_map_util
from utils import visualization_utils_color as vis_util

# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = './model/frozen_inference_graph_face.pb'

# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = './protos/face_label_map.pbtxt'

NUM_CLASSES = 2

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)

class TensoflowFaceDector(object):
    def __init__(self, PATH_TO_CKPT):
        """Tensorflow detector
        """

        self.detection_graph = tf.Graph()
        with self.detection_graph.as_default():
            od_graph_def = tf.compat.v1.GraphDef()
            with tf.io.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='')


        with self.detection_graph.as_default():
            config = tf.compat.v1.ConfigProto()
            config.gpu_options.allow_growth = True
            self.sess = tf.compat.v1.Session(graph=self.detection_graph, config=config)
            self.windowNotSet = True


    def run(self, image):
        """image: bgr image
        return (boxes, scores, classes, num_detections)
        """

        image_np = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

        # the array based representation of the image will be used later in order to prepare the
        # result image with boxes and labels on it.
        # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
        image_np_expanded = np.expand_dims(image_np, axis=0)
        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.
        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.
        scores = self.detection_graph.get_tensor_by_name('detection_scores:0')
        classes = self.detection_graph.get_tensor_by_name('detection_classes:0')
        num_detections = self.detection_graph.get_tensor_by_name('num_detections:0')
        # Actual detection.
        start_time = time.time()
        (boxes, scores, classes, num_detections) = self.sess.run(
            [boxes, scores, classes, num_detections],
            feed_dict={image_tensor: image_np_expanded})
        elapsed_time = time.time() - start_time
        print('inference time cost: {}'.format(elapsed_time))

        return (boxes, scores, classes, num_detections)


if __name__ == "__main__":
    import sys
    if len(sys.argv) != 2:
        print ("usage:%s (cameraID | filename) Detect faces\
 in the video example:%s 0"%(sys.argv[0], sys.argv[0]))
        exit(1)

    try:
    	camID = int(sys.argv[1])
    except:
    	camID = sys.argv[1]
    
    tDetector = TensoflowFaceDector(PATH_TO_CKPT)

    cap = cv2.VideoCapture(camID)
    windowNotSet = True
    while True:
        ret, image = cap.read()
        if ret == 0:
            break

        [h, w] = image.shape[:2]
        print (h, w)
        image = cv2.flip(image, 1)

        (boxes, scores, classes, num_detections) = tDetector.run(image)

        vis_util.visualize_boxes_and_labels_on_image_array(
            image,
            np.squeeze(boxes),
            np.squeeze(classes).astype(np.int32),
            np.squeeze(scores),
            category_index,
            use_normalized_coordinates=True,
            line_thickness=4)

        if windowNotSet is True:
            cv2.namedWindow("tensorflow based (%d, %d)" % (w, h), cv2.WINDOW_NORMAL)
            windowNotSet = False

        cv2.imshow("tensorflow based (%d, %d)" % (w, h), image)
        k = cv2.waitKey(1) & 0xff
        if k == ord('q') or k == 27:
            break

    cap.release()