def main(tiny):
if tiny:
model = YOLOv3_tiny(n_classes=80,
iou_threshold=0.5,
confidence_threshold=0.5)
else:
model = YOLOv3(n_classes=80,
iou_threshold=0.5,
confidence_threshold=0.5)
inputs = tf.placeholder(tf.float32, [1, 416, 416, 3])
model(inputs)
model_vars = tf.global_variables(scope=model.scope)
if tiny:
assign_ops = load_weights_tiny(model_vars,
'./weights/yolov3-tiny.weights')
else:
assign_ops = load_weights(model_vars, './weights/yolov3.weights')
saver = tf.train.Saver(tf.global_variables(scope=model.scope))
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
save_path = './weights/model-tiny.ckpt' if tiny else './weights/model.ckpt'
sess.run(assign_ops)
saver.save(sess, save_path)
print("Model Saved at \"" + save_path + "\"")
def main(mode, tiny, iou_threshold, confidence_threshold, path):
class_names, n_classes = load_class_names()
if tiny:
model = YOLOv3_tiny(n_classes=n_classes,
iou_threshold=iou_threshold,
confidence_threshold=confidence_threshold)
else:
model = YOLOv3(n_classes=n_classes,
iou_threshold=iou_threshold,
confidence_threshold=confidence_threshold)
inputs = tf.placeholder(tf.float32, [1, *model.input_size, 3])
detections = model(inputs)
saver = tf.train.Saver(tf.global_variables(scope=model.scope))
with tf.Session() as sess:
saver.restore(sess, './weights/model-tiny.ckpt' if tiny else './weights/model.ckpt')
if mode == 'image':
image = load_image(path, input_size=model.input_size)
result = sess.run(detections, feed_dict={inputs: image})
draw_boxes(path, boxes_dict=result[0], class_names=class_names, input_size=model.input_size)
return
elif mode == 'video':
cv2.namedWindow("Detections")
video = cv2.VideoCapture(path)
fourcc = int(video.get(cv2.CAP_PROP_FOURCC))
fps = video.get(cv2.CAP_PROP_FPS)
frame_size = (int(video.get(cv2.CAP_PROP_FRAME_WIDTH)), int(video.get(cv2.CAP_PROP_FRAME_HEIGHT)))
out = cv2.VideoWriter('./detections/video_output.mp4', fourcc, fps, frame_size)
print("Video being saved at \"" + './detections/video_output.mp4' + "\"")
print("Press 'q' to quit")
while True:
retval, frame = video.read()
if not retval:
break
resized_frame = cv2.resize(frame, dsize=tuple((x) for x in model.input_size[::-1]), interpolation=cv2.INTER_NEAREST)
result = sess.run(detections, feed_dict={inputs: [resized_frame]})
draw_boxes_frame(frame, frame_size, result, class_names, model.input_size)
cv2.imshow("Detections", frame)
key = cv2.waitKey(1) & 0xFF
if key == ord('q'):
break
out.write(frame)
cv2.destroyAllWindows()
video.release()
return
elif mode == 'webcam':
while True:
frame, addr = receive()
frame_size = (frame.shape[1], frame.shape[0])
resized_frame = cv2.resize(frame, dsize=tuple((x) for x in model.input_size[::-1]), interpolation=cv2.INTER_NEAREST)
result = sess.run(detections, feed_dict={inputs: [resized_frame]})
draw_boxes_frame(frame, frame_size, result, class_names, model.input_size)
jpgstring = cv2.imencode(".jpg", frame)
packet = jpgstring[1].tostring()
udpServSock.sendto(packet, addr)
return
def __init__(self, iou_threshold, confidence_threshold):
tf.reset_default_graph()
class_names, n_classes = load_class_names()
self.model = YOLOv3(n_classes=n_classes,
iou_threshold=iou_threshold,
confidence_threshold=confidence_threshold)
self.inputs = tf.placeholder(
tf.float32, [1, *self.model.input_size, 3])
self.detections = self.model(self.inputs)
saver = tf.train.Saver(tf.global_variables(scope=self.model.scope))
self.sess = tf.Session()
saver.restore(self.sess, './weights/model.ckpt')
def build_model(self):
input_layer = tf.keras.layers.Input([self.W, self.H, self.D])
feature_maps = YOLOv3(input_layer)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = decode(fm, i)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
utils.load_weights(model, "yoloweights/yolov3.weights")
#print(model.summary())
return model
def __init__(self, iou_threshold, confidence_threshold):
self.class_names, self.n_classes = load_class_names()
self.model = YOLOv3(n_classes=self.n_classes,
iou_threshold=iou_threshold,
confidence_threshold=confidence_threshold)
self.inputs = tf.placeholder(tf.float32,
[1, *self.model.input_size, 3])
self.detections = self.model(self.inputs)
self.saver = tf.train.Saver(
tf.global_variables(scope=self.model.scope))
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
self.saver.restore(self.sess, './weights/model.ckpt')
raw_label = {}
with open(cfg.TEST.ANNOT_PATH,'r',encoding='utf-8') as f:
for line in f:
line = line.strip().split(' ')
p = line[0]
tag = []
for _ in line[1:]:
tag.append([float(__) for __ in _.split(',')])
tag[-1] = tag[-1][:-1]+[1]+tag[-1][-1:]
raw_label[p] = tag
# 获取输入图像的shape
n2s = read_json('./data/n2s.json')
model = YOLOv3({
'num_class':9
})
# 打印variable
model.get_loss(init_call[0],
((init_call[1], init_call[4]),
(init_call[2], init_call[5]),
(init_call[3], init_call[6])))
# for index, x in enumerate(model.variables):
# print('%s %s %s'%(str(index), str(x.name), str(x.shape)))
optimizer1,_ = create_optimizer(1e-4, cfg.TRAIN.EPOCHS*size, size, 1e-7)
optimizer2,_ = create_optimizer(1e-3, cfg.TRAIN.EPOCHS*size, size, 1e-6)
optimizer = [optimizer1, optimizer2]
def main(mode, tiny, iou_threshold, confidence_threshold, path):
rectangleColor = (0, 255, 0)
frameCounter = 0
currentCarID = 0
fps = 0
carTracker = {}
carNumbers = {}
carLocation1 = {}
carLocation2 = {}
speed = [None] * 1000
class_names, n_classes = load_class_names()
if tiny:
model = YOLOv3_tiny(n_classes=n_classes,
iou_threshold=iou_threshold,
confidence_threshold=confidence_threshold)
else:
model = YOLOv3(n_classes=n_classes,
iou_threshold=iou_threshold,
confidence_threshold=confidence_threshold)
inputs = tf.placeholder(tf.float32, [1, *model.input_size, 3])
detections = model(inputs)
saver = tf.train.Saver(tf.global_variables(scope=model.scope))
with tf.Session() as sess:
saver.restore(
sess, './weights/model-tiny.ckpt' if tiny else './weights/model.ckpt')
if mode == 'image':
image = load_image(path, input_size=model.input_size)
result = sess.run(detections, feed_dict={inputs: image})
draw_boxes(
path, boxes_dict=result[0], class_names=class_names, input_size=model.input_size)
return
cv2.namedWindow("Detections")
print("Video being saved at \"" + './detections/video_output.mp4' + "\"")
print("Press 'q' to quit")
pathIn = './data/PETS_2000_Frames/'
files = [f for f in os.listdir(pathIn)]
files.sort(key= lambda x: int(x.split('.')[0].split('_')[1]))
frame=cv2.imread(pathIn+files[0])
frame_size = frame.shape[:2][::-1]
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter(
'./detections/video_output.mp4', fourcc, 20, frame_size)
for i in range(len(files)):
frame = cv2.imread(pathIn+files[i])
resized_frame = cv2.resize(frame, dsize=tuple(
(x) for x in model.input_size[::-1]), interpolation=cv2.INTER_NEAREST)
result = sess.run(detections, feed_dict={inputs: [resized_frame]})
draw_boxes_frame(frame, frame_size, result,
class_names, model.input_size)
start_time = time.time()
# rc, image = video.read()
if type(frame) == type(None):
break
frame = cv2.resize(frame, frame_size)
resultImage = frame
frameCounter = frameCounter + 1
carIDtoDelete = []
for carID in carTracker.keys():
trackingQuality = carTracker[carID].update(frame)
if trackingQuality < 7:
carIDtoDelete.append(carID)
for carID in carIDtoDelete:
print('Removing carID ' + str(carID) + \
' from list of trackers.')
print('Removing carID ' + str(carID) + ' previous location.')
print('Removing carID ' + str(carID) + ' current location.')
carTracker.pop(carID, None)
carLocation1.pop(carID, None)
carLocation2.pop(carID, None)
if not (frameCounter % 10):
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cars = carCascade.detectMultiScale(
gray, 1.1, 13, 18, (24, 24))
for (_x, _y, _w, _h) in cars:
x = int(_x)
y = int(_y)
w = int(_w)
h = int(_h)
x_bar = x + 0.5 * w
y_bar = y + 0.5 * h
matchCarID = None
for carID in carTracker.keys():
trackedPosition = carTracker[carID].get_position()
t_x = int(trackedPosition.left())
t_y = int(trackedPosition.top())
t_w = int(trackedPosition.width())
t_h = int(trackedPosition.height())
t_x_bar = t_x + 0.5 * t_w
t_y_bar = t_y + 0.5 * t_h
if ((t_x <= x_bar <= (t_x + t_w)) and (t_y <= y_bar <= (t_y + t_h)) and (x <= t_x_bar <= (x + w)) and (y <= t_y_bar <= (y + h))):
matchCarID = carID
if matchCarID is None:
print('Creating new tracker ' + str(currentCarID))
tracker = dlib.correlation_tracker()
tracker.start_track(
frame, dlib.rectangle(x, y, x + w, y + h))
carTracker[currentCarID] = tracker
carLocation1[currentCarID] = [x, y, w, h]
currentCarID = currentCarID + 1
for carID in carTracker.keys():
trackedPosition = carTracker[carID].get_position()
t_x = int(trackedPosition.left())
t_y = int(trackedPosition.top())
t_w = int(trackedPosition.width())
t_h = int(trackedPosition.height())
carLocation2[carID] = [t_x, t_y, t_w, t_h]
end_time = time.time()
if not (end_time == start_time):
# print("Reached at 168")
fps = 1.0/(end_time - start_time)
for i in carLocation1.keys():
if frameCounter % 1 == 0:
[x1, y1, w1, h1] = carLocation1[i]
[x2, y2, w2, h2] = carLocation2[i]
# print 'previous location: ' + str(carLocation1[i]) + ', current location: ' + str(carLocation2[i])
carLocation1[i] = [x2, y2, w2, h2]
# print("Reached at 177")
# print 'new previous location: ' + str(carLocation1[i])
if [x1, y1, w1, h1] != [x2, y2, w2, h2]:
# print("Reached at 181")
if (speed[i] == None or speed[i] == 0):
speed[i] = estimateSpeed(
[x1, y1, w1, h1], [x2, y2, w2, h2])
# if y1 > 275 and y1 < 285:
if speed[i] != None:
print(str(int(speed[i])) + " km/hr")
cv2.putText(resultImage, str(int(speed[i])) + " km/hr", (int(x1 + w1/2), int(y1-5)), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (255, 255, 255), 2)
cv2.imshow("Detections", resultImage)
key = cv2.waitKey(1) & 0xFF
if key == ord('q'):
break
out.write(resultImage)
cv2.destroyAllWindows()