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@rocking5566
Created March 22, 2018 02:57
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Tensorflow Object Detection API in WebCam
import sys
sys.path.append("/opt/tf_model/research")
sys.path.append("/opt/tf_model/research/object_detection")
import os
import cv2
import numpy as np
import tensorflow as tf
from utils import label_map_util
from utils import visualization_utils as vis_util
if tf.__version__ < '1.4.0':
raise ImportError('Please upgrade your tensorflow installation to v1.4.* or later!')
print("OpenCV version : {0}".format(cv2.__version__))
# Should run under docker container from tensorflow_object_detection
ROOT = '/opt/tf_model/research/object_detection/'
# Download pre-train SSD-MobileNet model from
# http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2017_11_17.tar.gz
MODEL_ROOT = '/datasets/tf_model/'
MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'
PATH_TO_CKPT = os.path.join(MODEL_ROOT, 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(ROOT, 'data', 'mscoco_label_map.pbtxt')
NUM_CLASSES = 90
def detect_objects(image_np, sess, detection_graph):
# 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 = detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
boxes = 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 = detection_graph.get_tensor_by_name('detection_scores:0')
classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
t1 = cv2.getTickCount()
# Actual detection.
(boxes, scores, classes, num_detections) = sess.run(
[boxes, scores, classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
t2 = cv2.getTickCount()
print((t2 - t1) / cv2.getTickFrequency())
# 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)
return image_np
if __name__ == '__main__':
# This is needed since the notebook is stored in the object_detection folder.
video_capture = cv2.VideoCapture(0)
if not video_capture.isOpened():
print('No video camera found')
exit()
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='')
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)
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
# Definite input and output Tensors for detection_graph
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# 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 represent how level of confidence for each of the objects.
# 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')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
while True:
ret, frame = video_capture.read()
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
result_rgb = detect_objects(frame_rgb, sess, detection_graph)
result_bgr = cv2.cvtColor(result_rgb, cv2.COLOR_RGB2BGR)
cv2.imshow('Video', result_bgr)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
video_capture.release()
cv2.destroyAllWindows()
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