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object_detection_tf.py
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import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
import multiprocessing
from multiprocessing import Queue
import time
import argparse
import cv2
from myutil import downloadutil
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util
arg_parser = argparse.ArgumentParser()
arg_parser.add_argument('-v', '--video', type=str, required=True,
help="video file for detection")
args = arg_parser.parse_args()
# What model to download.
MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017'
def load_graph(model_name='ssd_mobilenet_v1_coco_11_06_2017'):
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'
downloadutil.maybe_download(os.getcwd(), MODEL_FILE,
DOWNLOAD_BASE+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 graph
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='')
return detection_graph
NUM_CLASSES = 90
def load_label_map(label_map_name, num_class):
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('data', label_map_name)
# load label map
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_class, use_display_name=True)
category_index = label_map_util.create_category_index(categories)
return category_index
# def load_image_into_numpy_array(image):
# # import pdb; pdb.set_trace()
# # (im_width, im_height) = image.size
# (im_width, im_height) = image.shape
# # return np.array(image.getdata()).reshape(
# # (im_height, im_width, 3)).astype(np.uint8)
# return image.reshape((im_height, im_width, 3)).astype(np.uint8)
# def detect_object(detection_graph, image, category_index):
# 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')
# # image = Image.open(image_path)
# # the array based representation of the image will be used later in order to prepare the
# # result image with boxes and labels on it.
# # image_np = load_image_into_numpy_array(image)
# image_np = image
# # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
# image_np_expanded = np.expand_dims(image_np, axis=0)
# # Actual detection.
# (boxes, scores, classes, num) = sess.run(
# [detection_boxes, detection_scores, detection_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,
# min_score_thresh = 0.7)
# return image_np
def detect_object(detection_graph, sess, image, category_index):
with detection_graph.as_default():
with sess.as_default() 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')
# image = Image.open(image_path)
# the array based representation of the image will be used later in order to prepare the
# result image with boxes and labels on it.
# image_np = load_image_into_numpy_array(image)
image_np = image
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
# Actual detection.
(boxes, scores, classes, num) = sess.run(
[detection_boxes, detection_scores, detection_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,
min_score_thresh = 0.7)
return image_np
detection_graph = load_graph(model_name='ssd_mobilenet_v1_coco_11_06_2017')
category_index = load_label_map(label_map_name='mscoco_label_map.pbtxt', num_class=NUM_CLASSES)
image_q = Queue(maxsize=200)
processed_q = Queue(maxsize=200)
#a process that put imge into image_q
def image_worker(image_q, video_file):
print("image worker start")
video_capture = cv2.VideoCapture(video_file)
ret, frame = video_capture.read()
while ret:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
image_q.put(frame)
ret, frame = video_capture.read()
video_capture.release()
input_process = multiprocessing.Process(target=image_worker, args=(image_q, args.video))
# a process to do the detection_graph
def object_detection_worker(image_q, processed_q, detection_graph, category_index):
print("detection worker start")
gpu_options = tf.GPUOptions(allow_growth=True)
config = tf.ConfigProto(gpu_options=gpu_options)
sess = tf.Session(graph=detection_graph, config=config)
while True:
frame = image_q.get()
t = time.time()
ann_image = detect_object(detection_graph, sess, frame, category_index)
print("time for a frame:", time.time()-t)
ann_image = cv2.cvtColor(ann_image, cv2.COLOR_RGB2BGR)
processed_q.put(ann_image)
detector_process = multiprocessing.Process(target=object_detection_worker,
args=(image_q, processed_q, detection_graph, category_index))
input_process.start()
detector_process.start()
while True:
ann_image = processed_q.get()
cv2.imshow('frame', ann_image)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
input_process.terminate()
detector_process.terminate()
cv2.destroyAllWindows()