inference.py

import os
import cv2
import argparse
import numpy as np
import tensorflow as tf
import sys

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

# Define and parse input arguments
parser = argparse.ArgumentParser()
parser.add_argument('--modeldir', help='Folder the frozen_inference_graph.pb file is located in',
                    required=True)
parser.add_argument('--graph', help='Name of the frozen_inference_graph.pb file, if different than '
                                    'frozen_inference_graph.pb',
                    default='frozen_inference_graph.pb')
parser.add_argument('--label', help='Name of the labelmap file, if different than label.pbtxt',
                    default='label.pbtxt')
parser.add_argument('--threshold', help='Minimum confidence threshold for displaying detected objects',
                    default=0.5)
parser.add_argument('--video', help='Name of the video file',
                    default='test.mp4')
parser.add_argument("--num_classes", type=int, default=2,
                    help="Number of classes")

args = parser.parse_args()

MODEL_NAME = args.modeldir
GRAPH_NAME = args.graph
LABELMAP_NAME = args.label
VIDEO_NAME = args.video
min_conf_threshold = float(args.threshold)

# Grab path to current working directory
CWD_PATH = os.getcwd()

# Path to video file
PATH_TO_VIDEO = os.path.join(CWD_PATH, VIDEO_NAME)

# Path to .tflite file, which contains the model that is used for object detection
PATH_TO_CKPT = os.path.join(CWD_PATH, MODEL_NAME, GRAPH_NAME)

# Path to label map file
PATH_TO_LABELS = os.path.join(CWD_PATH, MODEL_NAME, LABELMAP_NAME)

# Number of classes the object detector can identify
NUM_CLASSES = args.num_classes

# 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)

print("Label Loading")
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)
print("Label Loaded")
# 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')

# Open video file
video = cv2.VideoCapture(PATH_TO_VIDEO)

while(video.isOpened()):

    # 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 = video.read()
    frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    frame_expanded = np.expand_dims(frame_rgb, 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})

    # 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.uint8),
        np.squeeze(scores),
        category_index,
        use_normalized_coordinates=True,
        line_thickness=8,
        min_score_thresh=min_conf_threshold)

    # 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

# Clean up
video.release()
cv2.destroyAllWindows()