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 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()
if __name__ == '__main__':
cam = cv2.VideoCapture(0)
pred = Predictor('config.json', 'model.h5')
person_pred = Person_Predictor(0.5, 0.5)
pusher = DBPusher()
while True:
# get frame
ret, frame = cam.read()
# predict humans and generate points
boxes, image = pred.predict(frame)
im, res = person_pred.predict(frame)
num = draw_boxes_frame(frame, image.shape, res,
person_pred.class_names,
person_pred.model.input_size, 'person')
image_h, image_w, _ = image.shape
points = bboxes_to_points(boxes, image_w, image_h)
print(len(res[0][num]))
points_2 = [(int((box[0] + box[2]) / 2), int((box[1] + box[3]) / 2))
for box in res[0][num]]
points.extend(points_2)
if len(points) == 0: continue
msts = Graph(coords=points).mst().prune_long_edges(1)
clusters = []
lines = []
for mst in msts:
r2 = poly_r_squared_of_points(mst.coords)
print(r2)