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test_camera.py
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# coding:utf-8
import ConfigParser
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import cv2
import tensorflow as tf
import zipfile
from collections import defaultdict
from io import StringIO
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from PIL import Image
from object_detection.utils import ops as utils_ops
from utils import label_map_util
from utils import visualization_utils as vis_util
import pylab
from scipy import misc
from time import time
os.environ['CUDA_VISIBLE_DEVICES'] = ''#以下是cpu方式
#os.environ['CUDA_VISIBLE_DEVICES']='0'#这个是gpu方式
PATH_TO_CKPT = '/home/lixuan/Desktop/traffic_lights/ssd_mobilenet_v1_coco_2017_11_17/frozen_inference_graph.pb'
PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')
NUM_CLASSES = 90
cf = ConfigParser.ConfigParser()
cf.read('/home/lixuan/camera.conf')
choose = int(cf.get('db','db_trafficlights'))
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)
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)
PATH_TO_TEST_IMAGES_DIR = 'test_images'
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 3) ]
# Size, in inches, of the output images.
IMAGE_SIZE = (12, 8)
def run_inference_for_single_image(image, graph):
with graph.as_default():
with tf.Session() as sess:
# Get handles to input and output tensors
ops = tf.get_default_graph().get_operations()
all_tensor_names = {output.name for op in ops for output in op.outputs}
tensor_dict = {}
for key in [
'num_detections', 'detection_boxes', 'detection_scores',
'detection_classes', 'detection_masks'
]:
tensor_name = key + ':0'
if tensor_name in all_tensor_names:
tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(
tensor_name)
if 'detection_masks' in tensor_dict:
# The following processing is only for single image
detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])
# Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.
real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)
detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])
detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])
detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
detection_masks, detection_boxes, image.shape[0], image.shape[1])
detection_masks_reframed = tf.cast(
tf.greater(detection_masks_reframed, 0.5), tf.uint8)
# Follow the convention by adding back the batch dimension
tensor_dict['detection_masks'] = tf.expand_dims(
detection_masks_reframed, 0)
image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')
# Run inference
output_dict = sess.run(tensor_dict,
feed_dict={image_tensor: np.expand_dims(image, 0)})
# all outputs are float32 numpy arrays, so convert types as appropriate
output_dict['num_detections'] = int(output_dict['num_detections'][0])
output_dict['detection_classes'] = output_dict[
'detection_classes'][0].astype(np.uint8)
output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
output_dict['detection_scores'] = output_dict['detection_scores'][0]
if 'detection_masks' in output_dict:
output_dict['detection_masks'] = output_dict['detection_masks'][0]
return output_dict
#for image_path in TEST_IMAGE_PATHS:
cap = cv2.VideoCapture(choose)
while True:
start = time()
print "---------------------------------------------"
ret,frame =cap.read()
#frame =cv2.pyrDown(cv2.pyrDown(frame))
image = Image.fromarray(cv2.cvtColor(frame,cv2.COLOR_BGR2RGB))
#image = Image.open('/home/lixuan/Desktop/traffic_lights/test_images/green.jpg')
# 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)
# 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.
output_dict = run_inference_for_single_image(image_np, detection_graph)
# Visualization of the results of a detection.
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
output_dict['detection_boxes'],
output_dict['detection_classes'],
output_dict['detection_scores'],
category_index,
instance_masks=output_dict.get('detection_masks'),
use_normalized_coordinates=True,
line_thickness=8)
stop = time()
print(str(stop-start) + "秒")
print"-----------------------------------------------------"
#plt.figure(figsize=IMAGE_SIZE)
'''
a_index = 0
b = output_dict['detection_scores']
print categories[55]
print output_dict['detection_classes']
for str1 in output_dict['detection_classes']:
print str1
a = categories[str1-1]
a_index += 1
if b[a_index-1] > 0.5:
print a['name']
print b[a_index-1]
'''
#misc.imsave('image_np.png', image_np)
#cv2.imshow('image_np',image_np)