import numpy as np
-import os
-import six.moves.urllib as urllib
-import sys
-import tarfile
-import tensorflow as tf
-import zipfile
-import cv2
-
-from collections import defaultdict
-from io import StringIO
-from matplotlib import pyplot as plt
-from PIL import Image
-from object_detection.utils import label_map_util
-from object_detection.utils import visualization_utils as vis_util
-
-# Define the video stream
-cap = cv2.VideoCapture(0) # Change only if you have more than one webcams
-
-# What model to download.
-# Models can bee found here: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
-MODEL_NAME = 'ssd_inception_v2_coco_2017_11_17'
-MODEL_FILE = MODEL_NAME + '.tar.gz'
-DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'
-
-# Path to frozen detection graph. This is the actual model that is used for the object detection.
-PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'
-
-# List of the strings that is used to add correct label for each box.
-PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')
-
-# Number of classes to detect
-NUM_CLASSES = 90
-
-# Download Model
-if not os.path.exists(os.path.join(os.getcwd(), MODEL_FILE)):
- print("Downloading model")
- opener = urllib.request.URLopener()
- opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)
- tar_file = tarfile.open(MODEL_FILE)
- for file in tar_file.getmembers():
- file_name = os.path.basename(file.name)
- if 'frozen_inference_graph.pb' in file_name:
- tar_file.extract(file, os.getcwd())
-
-
-# Load a (frozen) Tensorflow model into memory.
-detection_graph = tf.Graph()
-with 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='')
-
-
-# Loading label map
-# Label maps map indices to category names, so that when our convolution network predicts `5`, we know that this corresponds to `airplane`. 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)
-category_index = label_map_util.create_category_index(categories)
-
-
-# Helper code
-def load_image_into_numpy_array(image):
- (im_width, im_height) = image.size
- return np.array(image.getdata()).reshape(
- (im_height, im_width, 3)).astype(np.uint8)
-
-
-# Detection
-with detection_graph.as_default():
- with tf.compat.v1.Session(graph=detection_graph) as sess:
- while True:
- # Read frame from camera
- ret, image_np = cap.read()
- # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
- image_np_expanded = np.expand_dims(image_np, axis=0)
- # Extract image tensor
- image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
- # Extract detection boxes
- boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
- # Extract detection scores
- scores = detection_graph.get_tensor_by_name('detection_scores:0')
- # Extract detection classes
- classes = detection_graph.get_tensor_by_name('detection_classes:0')
- # Extract number of detectionsd
- num_detections = detection_graph.get_tensor_by_name(
- 'num_detections:0')
- # Actual detection.
- (boxes, scores, classes, num_detections) = sess.run(
- [boxes, scores, classes, num_detections],
- feed_dict={image_tensor: image_np_expanded})
- # Visualization of the results of a detection.
- vis_util.visualize_boxes_and_labels_on_image_array(
- image_np,
- np.squeeze(boxes),
- np.squeeze(classes).astype(np.int32),
- np.squeeze(scores),
- category_index,
- use_normalized_coordinates=True,
- line_thickness=8)
-
- # Display output
- cv2.imshow('object detection', cv2.resize(image_np, (800, 600)))
-
- if cv2.waitKey(25) & 0xFF == ord('q'):
- cv2.destroyAllWindows()
- break
-