def _init_category_index(label_map_path):
"""Creates category index from class indexes to name of the classes.
Args:
label_map_path: path to the mapping.
Returns:
A map for mapping int keys to string categories.
"""
label_map = label_map_util.load_labelmap(label_map_path)
num_classes = np.max(x.id for x in label_map.item)
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)
return category_index
# nms
nms_begin = time.time()
if len(sys.argv) > 1 and sys.argv[1] == 'only_person':
boxes, classes, scores = dsnms(res, only_person=True)
else:
boxes, classes, scores = dsnms(res)
nms_end = time.time()
print('total nms: {:.4f}s'.format(nms_end - nms_begin))
# save&visualization
save_begin = time.time()
PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')
NUM_CLASSES = 80
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)
if not os.path.exists('video/output'):
os.makedirs('video/output')
else:
for root, dirs, files in os.walk('video/output'):
for f in files:
os.unlink(os.path.join(root, f))
for i, image_path in enumerate(pkllist):
start = time.time()
image_process = get_labeled_image(category_index, image_path,
np.array(boxes[i]),
np.array(classes[i]),
np.array(scores[i]))
scipy.misc.imsave('video/output/frame{}.jpg'.format(i), image_process)
print('(%d/%d)writing image time: %5.3fs \r' %
def initLabels(path):
global category_index
label_map = label_map_util.load_labelmap(path)
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 __init__(self):
#TODO load classifier
# pass
# light state
self.cur_ls = TrafficLight.UNKNOWN
# This is needed since file is stored in
sys.path.append("..")
# Name of the directory containing the object detection module we're using
MODEL_NAME = 'light_classification/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,MODEL_NAME,'labelmap.pbtxt')
# Number of classes the object detector can identify
NUM_CLASSES = 3
# Load the label map.
# Label maps map indices to category names, so that when our convolution
# network predicts `1`, we know that this corresponds to `Red`.
# 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='')
self.sess = tf.Session(graph=detection_graph)
# Define input and output tensors (i.e. data) for the object detection classifier
# Input tensor is the image
self.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
self.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.
self.detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
self.detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
# Number of objects detected
self.num_detections = detection_graph.get_tensor_by_name('num_detections:0')
def detection(video_input=0, show=False, write=False, threshold=0.5):
def angle(coord):
def unit_vector(vector):
return vector / np.linalg.norm(vector)
v1_u = unit_vector((1, 0))
v2_u = unit_vector(coord)
theta = np.rad2deg(np.arccos(np.clip(np.dot(v1_u, v2_u), -1.0, 1.0)))
if (coord[1] > 0):
theta = 360 - theta
return theta
def direction(theta):
if theta <= 9 or theta >= 351:
direction = 'E'
elif theta >= 81 and theta <= 99:
direction = 'N'
elif theta >= 171 and theta <= 189:
direction = 'W'
elif theta >= 261 and theta <= 279:
direction = 'S'
elif theta > 9 and theta < 81:
direction = 'NE'
elif theta > 99 and theta < 171:
direction = 'NW'
elif theta > 189 and theta < 261:
direction = 'SW'
else:
direction = 'SE'
return direction
if (video_input == '0'):
fvs = FileVideoStream(0).start()
else:
fvs = FileVideoStream(video_input).start()
cap = fvs.get_stream()
global cap_width
cap_width = int(cap.get(3))
cap_height = int(cap.get(4))
if write:
out = cv2.VideoWriter('output/output_{:%y%m%d_%H%M%S}.avi'.format(
datetime.datetime.now()), cv2.VideoWriter_fourcc(*'XVID'), cap.get(5),
(cap_width // 3, cap_height // 3))
MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'
MODEL_FILE = MODEL_NAME + '.tar.gz'
DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'
PATH_TO_CKPT = os.path.join('data', 'model', 'frozen_inference_graph.pb')
PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')
NUM_CLASSES = 2
if not os.path.isfile(PATH_TO_CKPT):
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 not file.isdir() and not 'saved_model.pb' in file_name:
file.name = file_name
tar_file.extract(file, os.path.join('data', 'model'))
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:
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
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')
try:
init = False
count = 0
theta = 0
center_curr = np.array([0, 0])
center_delta_curr = np.array([0, 0])
center_ref = np.array([0.5, 0.5])
center_prev = np.array([0.5, 0.5])
ref = (0, 0, 0, 0)
while True:
image_np = fvs.read()
if not fvs.more():
fvs.stop()
print("Released video source.")
break
if (counter % 20 == 0):
image_np_expanded = np.expand_dims(image_np, axis=0)
(boxes, scores, classes, num) = sess.run([detection_boxes,
detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
for i in range(min(20, np.squeeze(boxes).shape[0])):
class_value = np.squeeze(classes).astype(np.int32)[i]
score_value = np.squeeze(scores)[i]
if class_value in category_index.keys():
ymin, xmin, ymax, xmax = tuple(np.squeeze(boxes)[i].tolist())
class_name = category_index[class_value]['name']
if score_value > threshold:
center = np.array([(xmin + xmax) / 2, (ymin + ymax) / 2])
center_curr = center
center_delta_curr = center_curr - center_prev
distance = np.linalg.norm(center_ref - center_curr)
if distance > 0.25 and count < 5 and init:
count += 1
scores[0][i] = 0
else:
count = 0
if distance > 0.1 or not init:
init = True
center_prev = center_ref
center_ref = center_curr
ref = (xmin, xmax, ymin, ymax)
theta = angle(center_ref - center_prev)
print(('{0} @ {1:.5}% | angle: {2:.6} | direction: {3:2} | center: ({4[0]:.2f}, {4[1]:.2f}) | distance from ref: {5:.6}').format(class_name, str(score_value * 100), str(theta), direction(theta), center, str(distance)))
coord_prev = (int(center_prev[0] * cap_width // 3), int(center_prev[1] * cap_height // 3))
coord_ref = (int(center_ref[0] * cap_width // 3), int(center_ref[1] * cap_height // 3))
min_vertex = (int(ref[0] * cap_width // 3), int(ref[2] * cap_height // 3))
max_vertex = (int(ref[1] * cap_width // 3), int(ref[3] * cap_height // 3))
cv2.rectangle(image_np, min_vertex, max_vertex, (255, 0, 0), 2)
cv2.arrowedLine(image_np, coord_prev, coord_ref, (255, 0, 0), 3, tipLength=0.25)
display_str = 'angle: {0:.2f} ({1})'.format(theta, direction(theta))
cv2.putText(image_np, display_str, (int(ref[0] * cap_width // 3), int((ref[2] - 0.0275) * cap_height // 3)), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (255, 0, 0), 2, cv2.LINE_AA)
if write:
out.write(image_np)
if show:
cv2.imshow('Object Detection', image_np)
if cv2.waitKey(50) & 0xFF == ord('q'):
cv2.destroyAllWindows()
break
except KeyboardInterrupt:
fvs.stop()
if write:
out.release()
print("Released video source.")
with model.as_default():
# initialize the graph definition
# graphDef = tf.GraphDef()
graphDef = tf.compat.v1.GraphDef()
# load the graph from disk
# with tf.gfile.GFile(args["model"], "rb") as f:
with tf.io.gfile.GFile(args["model"], "rb") as f:
serializedGraph = f.read()
graphDef.ParseFromString(serializedGraph)
tf.import_graph_def(graphDef, name="")
# load the class labels from disk
labelMap = label_map_util.load_labelmap(args["labels"])
categories = label_map_util.convert_label_map_to_categories(
labelMap, max_num_classes=args["num_classes"],
use_display_name=True)
categoryIdx = label_map_util.create_category_index(categories)
# create a session to perform inference
with model.as_default():
with tf.Session(graph=model) as sess:
# grab a reference to the input image tensor and the boxes
# tensor
imageTensor = model.get_tensor_by_name("image_tensor:0")
boxesTensor = model.get_tensor_by_name("detection_boxes:0")
# for each bounding box we would like to know the score
# (i.e., probability) and class label
scoresTensor = model.get_tensor_by_name("detection_scores:0")
classesTensor = model.get_tensor_by_name("detection_classes:0")
def main(self, image_filepath):
TEST_IMAGES = self.get_test_images(image_filepath)
box_coord = []
for IMAGE_NAME in tqdm(TEST_IMAGES):
print('********************************')
print('Processing image:', IMAGE_NAME)
print('********************************')
# Path to image
PATH_TO_IMAGE = os.path.join(self.TEST_IMAGE_PATH, IMAGE_NAME)
img = cv2.imread(PATH_TO_IMAGE)
width = img.shape[1]
height = img.shape[0]
# Load the label map.
label_map = label_map_util.load_labelmap(self.PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(
label_map,
max_num_classes=self.NUM_CLASSES,
use_display_name=True)
category_index = label_map_util.create_category_index(categories)
# Load Tensorflow model into memory.
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(self.PATH_TO_FROZEN_GRAPH, 'rb') as f:
serialized_graph = f.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
sess = tf.Session(graph=detection_graph)
# 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
detection_boxes = detection_graph.get_tensor_by_name(
'detection_boxes:0')
# Each score represents level of confidence for each of the objects.
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')
# Load image using OpenCV and
# expand image dimensions to have shape: [1, None, None, 3]
image = cv2.imread(PATH_TO_IMAGE)
image_expanded = np.expand_dims(image, 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: image_expanded})
if self.visualize:
# Draw the results of the detection
vis_util.visualize_boxes_and_labels_on_image_array(
image,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=6,
min_score_thresh=0.2)
# All the results have been drawn on image
cv2.imwrite(
os.path.join(self.BASE_PATH,
'results/{}'.format(IMAGE_NAME)), image)
#plt.imshow(image,cmap='gnuplot')
#plt.show()
# Get scores and bboxes
for i, score in enumerate(np.squeeze(scores)):
# inx of bbox
bbox = 'bbox_{}'.format(i)
if score >= 0.2:
#get bbox coordinates
ymin, xmin, ymax, xmax = np.squeeze(boxes)[i]
ymin = int(ymin * height)
xmin = int(xmin * width)
ymax = int(ymax * height)
xmax = int(xmax * width)
coordinates = (ymin, xmin, ymax, xmax)
box_coord.append(coordinates)
else:
continue
print('*****************************************************')
print('Image Name:', IMAGE_NAME)
print('Box Coordinates:', box_coord)
print('Number of Detected Boxes', np.array(box_coord).shape[0])
print('*****************************************************')
print('Total Detected Objects:', np.array(box_coord).shape[0])
print('Shape of Box Array:', np.array(box_coord).shape)
return np.array(box_coord)
def people_knives(temp):
MODEL_NAME = './outputs/faster_rcnn/faster_rcnn_inception_v2_coco_2018_01_28'
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'
PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')
NUM_CLASSES = 90
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.compat.v1.GraphDef()
with tf.compat.v1.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)
TEST_IMAGE_PATHS = [ os.path.join(temp)]
# Size, in inches, of the output images.
IMAGE_SIZE = (12, 8)
with detection_graph.as_default():
with tf.compat.v1.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')
i='0'
j=0
for image_path in TEST_IMAGE_PATHS:
#image = Image.open(temp)
response = requests.get(temp)
image= Image.open(BytesIO(response.content))
# 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.
(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.
p = 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=1)
if(len(p)==0):
return ("")
else:
j=0
person=0
flag=0
flag1=0
flag2=0
n=len(p)
ans=""
temp1=""
while(j<n):
temp1 = ""
while(p[j]!=':'):
temp1 = temp1 + p[j]
j=j+1
if(temp1=="person"):
if(p[j]==':'):
j=j+2
temp=0
while(p[j]!='.' and p[j]!='%'):
temp = temp*10 + ord(p[j]) - 48
j=j+1
if(temp>95):
person = person + 1
elif(temp1=="knife"):
if(p[j]==':'):
j=j+2
temp=0
while(p[j]!='.' and p[j]!='%'):
temp = temp*10 + ord(p[j]) - 48
j=j+1
if(temp>=59 and flag==0):
ans+=temp1+" ,"
flag=1
elif(temp1=="baseball bat"):
if(p[j]==':'):
j=j+2
temp=0
while(p[j]!='.' and p[j]!='%' ):
temp = temp*10 + ord(p[j]) - 48
j=j+1
if(temp>=59 and flag1==0):
ans+=temp1+" ,"
flag1=1
elif(temp1=="scissors"):
if(p[j]==':'):
j=j+2
temp=0
while(p[j]!='.' and p[j]!='%'):
temp = temp*10 + ord(p[j]) - 48
j=j+1
if(temp>=59 and flag2==0):
ans+=temp1+" ,"
flag2=1
while(p[j]!='%'):
j=j+1
j=j+1
if(p[j]=='$'):
break
if(person>0):
if(person==1 and flag == 0 and flag1 == 0 and flag2 == 0):
ans = ans + "1 person"
elif(person==1):
ans = ans + "and 1 person"
elif(person>1 and flag == 0 and flag1 == 0 and flag2 == 0):
ans = ans + str(person) + " people"
else:
ans = ans + "and " + str(person) + " people"
return ans
def loadLabels(self):
label_map = label_map_util.load_labelmap(self.PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes=self.NUM_CLASSES, use_display_name=True)
self.category_index = label_map_util.create_category_index(categories)
def models_yuce(con):
detection_graph = tf.Graph() # 加载目标定位模型
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, '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) # 加载目标定位类别编号
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)
# model = load_model(PATH_TO_Classification)
c1, c2 = con
c1.close() # 主进程用conn1发送数据,子进程要用对应的conn2接受,所以讲conn1关闭,不关闭程序会阻塞
with detection_graph.as_default():
with tf.Session() as sess:
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)
image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')
while True:
try: # 异常处理,出现异常退出
tf_list = c2.recv()
value = 0 # 0左1右
read_result = []
plate_dict.clear()
for image_np_expanded in tf_list[0]: # 一组违法一张一张跑
# print(time.strftime("%Y-%m-%d %H:%M:%S ", time.localtime()))
output_dict = sess.run(tensor_dict, feed_dict={image_tensor: image_np_expanded})
# print(time.strftime("%Y-%m-%d %H:%M:%S ", time.localtime()))
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]
image2 = Image.fromarray(tf_list[1][value])
car_list, plate_list = [], []
for i in range(output_dict['num_detections']): # 收集每个图片第一张车跟车牌 如果有的话
if output_dict['detection_scores'][i] < 0.5 or (len(car_list) == 2 and plate_list):
break
if output_dict['detection_classes'][i] == 2 and not plate_list:
plate_list.append(output_dict['detection_boxes'][i])
if output_dict['detection_classes'][i] == 1 and len(car_list) < 2:
car_list.append(output_dict['detection_boxes'][i])
if len(car_list) == 2 and int(image2.size[1] * car_list[1][2]) > int(image2.size[1] * car_list[0][0]):
if not int(tf_list[5]):
with open('result.csv', 'a')as f:
f.write(tf_list[2] + ' 0 1 {}2\n'.format(value)) # 并行 2
plate_dict.clear()
save_img(tf_list[3], tf_list[2], tf_list[4], 1)
break
if plate_list: # 如果有车牌 肯定是有车
img = image2.crop((int(image2.size[0] * plate_list[0][1]),
int(image2.size[1] * plate_list[0][0]),
int(image2.size[0] * plate_list[0][3]),
int(image2.size[1] * plate_list[0][2])))
plate_dict[value] = img
elif car_list: # 如果只有车牌 判断车牌底的位置跟图片底部位置关系
if image2.size[1] - int(image2.size[1] * car_list[0][2]) < 90:
if not int(tf_list[5]):
with open('result.csv', 'a')as f:
f.write(tf_list[2] + ' 0 1 {}1\n'.format(value)) # 车身不完整 1
plate_dict.clear()
save_img(tf_list[3], tf_list[2], tf_list[4], 1)
break
elif value == 1 and not plate_dict: # 两辆车都没找到车牌 但是车位置明显完整
if not int(tf_list[5]):
with open('result.csv', 'a')as f:
f.write(tf_list[2] + ' 1 0\n') # 先默认车牌都对
save_img(tf_list[3], tf_list[2], tf_list[4], 0)
else:
with open('result.csv', 'r+')as f:
for a in f:
read_result.append(a.split(' '))
f.seek(0)
f.truncate()
for i in read_result:
if i[0] == tf_list[2]:
i[1] = '1'
i[2] = '0'
i[3] = '\n'
save_img(TEST_IMAGE_PATHS[1], tf_list[2], tf_list[4], 0)
f.write(' '.join(i))
else: # 能到这的图片都没找到 那就是没车喽
if not int(tf_list[5]):
with open('result.csv', 'a')as f:
f.write(tf_list[2] + ' 0 1 {}0\n'.format(value)) # 没找到车 0
plate_dict.clear()
save_img(tf_list[3], tf_list[2], tf_list[4], 1)
break
value += 1
if plate_dict:
if not int(tf_list[5]):
with open('result.csv', 'a')as f:
f.write(tf_list[2] + ' 1 0\n') # 先默认车牌都对
save_img(tf_list[3], tf_list[2], tf_list[4], 0)
else:
with open('result.csv', 'r+')as f:
for a in f:
read_result.append(a.split(' '))
f.seek(0)
f.truncate()
for i in read_result:
if i[0] == tf_list[2]:
i[1] = '1'
i[2] = '0'
i[3] = '\n'
save_img(TEST_IMAGE_PATHS[1], tf_list[2], tf_list[4], 0)
f.write(' '.join(i))
# print(tf_list[2].split('_')[3])
# for plate_key, plate_value in plate_dict.items():
# img_tensor = image.img_to_array(plate_value.resize((152, 52)))
# # print(plate_value.size)
# # print(img_tensor.shape)
# plate_value.resize((152, 52)).save('{}.jpg'.format(plate_key))
# img_tensor = np.expand_dims(img_tensor, axis=0)
# i = model.predict(img_tensor)
# for j in range(7):
# print(chars[int(np.where(i[j] == np.max(i[j]))[1])], end='')
# print()
except EOFError: # 说明素有数据已经全部接受,进程会抛出异常
break
def models_yuce(con):
num_classes = 90
path_to_labels = 'D:/1/exe/mask_rcnn/mask_rcnn_test-master/training/mscoco_label_map.pbtxt'
detection_graph = tf.Graph() # 加载目标定位模型
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(
'D:/1/exe/mask_rcnn/mask_rcnn_inception_v2_coco_2018_01_28/frozen_inference_graph.pb',
'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) # 加载目标定位类别编号
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)
c1, c2 = con
c1.close() # 主进程用conn1发送数据,子进程要用对应的conn2接受,所以讲conn1关闭,不关闭程序会阻塞
with detection_graph.as_default():
with tf.Session() as sess:
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)
detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])
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])
image_tensor = tf.get_default_graph().get_tensor_by_name(
'image_tensor:0')
while True:
try: # 异常处理,出现异常退出
tf_list = c2.recv()
value = 0 # 0左1右
for image_np_expanded in tf_list[0]: # 一组违法一张一张跑
detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
detection_masks, detection_boxes,
image_np_expanded.shape[1],
image_np_expanded.shape[2])
detection_masks_reframed = tf.cast(
tf.greater(detection_masks_reframed, 0.5),
tf.uint8)
tensor_dict['detection_masks'] = tf.expand_dims(
detection_masks_reframed, 0)
print(
time.strftime("%Y-%m-%d %H:%M:%S ",
time.localtime()), '开始')
output_dict = sess.run(
tensor_dict,
feed_dict={image_tensor: image_np_expanded})
print(
time.strftime("%Y-%m-%d %H:%M:%S ",
time.localtime()), '结束')
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]
output_dict['detection_masks'] = output_dict[
'detection_masks'][0]
vis_util.visualize_boxes_and_labels_on_image_array(
tf_list[1][value],
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)
image2 = Image.fromarray(tf_list[1][value])
image2.save('result/{}_{}{}'.format(
os.path.splitext(tf_list[2])[0], value,
os.path.splitext(tf_list[2])[1]))
value += 1
except EOFError: # 说明素有数据已经全部接受,进程会抛出异常
break
