def detect_object(self, img, conf_thres, nms_thres):
img = transforms.ToTensor()(img)
img, _ = pad_to_square(img, 0)
# Resize
img = resize(img, self.img_size)
img = img.to(self.device)
img = img.unsqueeze(0)
detection = self.model_yolov3(img)
# From [x,y,w,h] to [x_l,y_l,x_r,y_r]
detection = non_max_suppression(detection, conf_thres, nms_thres)
if detection[0] is not None:
detection = rescale_boxes(detection[0], self.img_size, (600, 600))
return detection
im_dim_list = im_dim_list.cuda()
# 5 resim için 10sn sürüyor
start_outputs_loop = time.time()
# bu for içinde tek tek resim işlemeye çalış
for i, batch in enumerate(im_batches):
# load the image
if CUDA:
batch = batch.cuda()
with torch.no_grad():
prediction = model(Variable(batch)) # bu işlem uzun sürüyor
prediction = non_max_suppression(prediction,
confidence,
num_classes,
nms_conf=nms_thesh)
try:
prediction[:,
0] += i * batch_size # transform the atribute from index in batch to index in imlist
except TypeError as e:
# yazdir(img[0])
outputs_names = pd.Series(imlist).apply(
lambda x: "{}/{}".format(args.outputs,
x.split("/")[-1]))
list(map(cv2.imwrite, outputs_names, loaded_ims))
os.remove(os.path.join(images, img[0]))
print("\nTYPE ERROR\n")
continue
if not write: # If we have't initialised output
while True:
ret, frame = cap.read()
if ret:
img = prep_image(frame, inp_dim)
im_dim = frame.shape[1], frame.shape[0]
im_dim = torch.FloatTensor(im_dim).repeat(1, 2)
if CUDA:
im_dim = im_dim.cuda()
img = img.cuda()
with torch.no_grad():
output = model(Variable(img, volatile=True))
output = non_max_suppression(output,
confidence,
num_classes,
nms_conf=nms_thesh)
if type(output) == int:
frames += 1
print("FPS of the video is {:5.4f}".format(frames /
(time.time() - start)))
cv2.imshow("frame", frame)
key = cv2.waitKey(1)
if key & 0xFF == ord('q'):
break
continue
obj_counter = output[:, -1]
obj_counter = Counter(obj_counter.numpy().astype(int).tolist())