def test():
vocab_size = VOCAB_SIZE + 2
model = Seq2seqModel(sp.attn_model, vocab_size, sp.hidden_size, vocab_size,
sp.num_layer, sp.dropout)
model.load_model()
max_len = MAX_LEN + 1
start = token2id.get('^', 37)
id2token = {str(idx): token for token, idx in token2id.items()}
for filename in os.listdir('./images'):
x, y, lens, label = load_image('./images/' + filename)
x = Variable(x.unsqueeze(0))
beam_path = model.best_path(x, max_len, start, topk=3)
greedy_path = model.best_path(x, max_len, start, topk=1)
beam_path = ''.join([
id2token.get(str(kk), '_') for kk in beam_path[0]
]).split('$')[0].lower()
greedy_path = ''.join([
id2token.get(str(kk), '_') for kk in greedy_path[0]
]).split('$')[0].lower()
beam_true = 1 if beam_path == label.lower() else 0
greedy_true = 1 if greedy_path == label.lower() else 0
print("label\t{}\tbeam\t{}\t{}\tgreedy\t{}\t{}".format(
label, beam_path, beam_true, greedy_path, greedy_true))
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 install_netcity_menu():
x_telegram_user_action = crud_search_action_window(
odoo, [["name", "=", "Telegram Users"]])[0]
image = load_image("assets/logo_fastapi.png")
netcity_menu = Menu(
name="NetCity",
web_icon_data=image,
action=f"ir.actions.act_window,{x_telegram_user_action}")
crud_create_menu(odoo, netcity_menu)
def test():
ctc = CTCModel(output_size=VOCAB_SIZE,
num_layers=hp.num_layer,
num_units=hp.num_units,
dropout=hp.dropout)
ctc.load_model()
id2token = {str(idx): token for token, idx in token2id.items()}
for filename in os.listdir('./images'):
x, y, lens, label = load_image('./images/' + filename)
x = Variable(x.unsqueeze(0))
outputs = CTCtest(x, ctc, use_cuda=False)
pre_label = ''.join([id2token.get(str(idx), '_') for idx in outputs])
acc = 1 if pre_label == label.lower() else 0
print("pre:\t{}\t\ttruth:\t{}\t\t是否正确\t{}".format(
pre_label, label, acc))
def test():
model = CTCModel(output_size=VOCAB_SIZE,
num_layers=hp.num_layer,
hidden_size=hp.hidden_size,
dropout=hp.dropout)
model.load_model()
id2token = {str(idx): token for token, idx in token2id.items()}
for filename in os.listdir(TEST_DATA):
x, y, lens, label = load_image(TEST_DATA + filename)
x = Variable(x.unsqueeze(0))
outputs = model(x)
predict = model.decode_ctc_outputs(outputs, blank=0)
pre_label = ''.join(
[id2token.get(str(idx), '_') for idx in predict[0]])
acc = 1 if pre_label.lower() == label.lower() else 0
print("predict:\t{}\t\tlabel:\t{}\t\t{}".format(pre_label, label, acc))
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()
def set_image(self, image_file):
if image_file:
self.image_file = image_file
self._image, self.rect = load_image(self.image_file)