Traceback (most recent call last):
File “/home/pi/tensorflow1/models/research/object_detection/Object_detection_webcam.py”, line 57, in
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
File “/home/pi/tensorflow1/models/research/object_detection/utils/label_map_util.py”, line 137, in load_labelmap
with tf.gfile.GFile(path, ‘r’) as fid:
Code:
######## Webcam Object Detection Using Tensorflow-trained Classifier #########
Author: Evan Juras
Date: 1/20/18
Description:
This program uses a TensorFlow-trained classifier to perform object detection.
It loads the classifier uses it to perform object detection on a webcam feed.
It draws boxes and scores around the objects of interest in each frame from
the webcam.
Some of the code is copied from Google’s example at
https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb
and some is copied from Dat Tran’s example at
https://github.com/datitran/object_detector_app/blob/master/object_detection_app.py
but I changed it to make it more understandable to me.
Import packages
import cv2
import numpy as np
import tensorflow as tf
import sys
import os
This is needed since the notebook is stored in the object_detection folder.
sys.path.append("…")
Import utilites
from utils import label_map_util
from utils import visualization_utils as vis_util
Name of the directory containing the object detection module we’re using
MODEL_NAME = ‘inference_graph’
Grab path to current working directory
CWD_PATH = os.getcwd()
Path to frozen detection graph .pb file, which contains the model that is used
for object detection.
PATH_TO_CKPT = os.path.join(CWD_PATH,MODEL_NAME,‘frozen_inference_graph.pb’)
Path to label map file
PATH_TO_LABELS = os.path.join(CWD_PATH,‘training’,‘labelmap.pbtxt’)
Number of classes the object detector can identify
NUM_CLASSES = 6
Load the label map.
Label maps map indices to category names, so that when our convolution
network predicts 5, we know that this corresponds to king.
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)
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)
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’)
Initialize webcam feed
video = cv2.VideoCapture(0)
while(True):
# 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_expanded = np.expand_dims(frame, 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})
objects = []
threshold = 0.89
for index, value in enumerate(classes[0]):
object_dict = {}
if scores[0,index] > threshold:
object_dict[category_index.get(value).get('name')] = scores[0,index]
avbn = (category_index.get(value).get('name'))
print(avbn)
# 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.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=8,
min_score_thresh=0.85)
# 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()
