def car():
def prepare_input(img, inp_dim):
"""
Prepare image for inputting to the neural network.
Perform tranpose and return Tensor
"""
orig_im = img
dim = orig_im.shape[1], orig_im.shape[0]
img = (custom_resize(orig_im, (inp_dim, inp_dim)))
img_ = img[:, :, ::-1].transpose((2, 0, 1)).copy()
img_ = torch.from_numpy(img_).float().div(255.0).unsqueeze(0)
return img_, orig_im, dim
def write(x, img):
c1 = tuple(x[1:3].int())
c2 = tuple(x[3:5].int())
cls = int(x[-1])
label = "{0}".format(classes[cls])
color = random.choice(colors)
cv2.rectangle(img, c1, c2, color, 1)
t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 1, 1)[0]
c2 = c1[0] + t_size[0] + 3, c1[1] + t_size[1] + 4
cv2.rectangle(img, c1, c2, color, -1)
cv2.putText(img, label, (c1[0], c1[1] + t_size[1] + 4), cv2.FONT_HERSHEY_PLAIN, 1, [225, 255, 255], 1);
#print(label)
if ( label == "car" or label == "truck"):
labels.append(label)
#print(labels)
#print("label:", label)
return img
def arg_parse():
"""
Parse arguements to the detect module
"""
parser = argparse.ArgumentParser(description='YOLO v3 Video Detection Module')
parser.add_argument("--video", dest='video', help=
"Video to run detection upon",
default="video5.avi", type=str)
parser.add_argument("--dataset", dest="dataset", help="Dataset on which the network has been trained",
default="pascal")
parser.add_argument("--confidence", dest="confidence", help="Object Confidence to filter predictions", default=0.5)
parser.add_argument("--nms_thresh", dest="nms_thresh", help="NMS Threshhold", default=0.4)
parser.add_argument("--cfg", dest='cfgfile', help=
"Config file",
default="cfg/yolov3.cfg", type=str)
parser.add_argument("--weights", dest='weightsfile', help=
"weightsfile",
default="yolov3.weights", type=str)
parser.add_argument("--reso", dest='reso', help=
"Input resolution of the network. Increase to increase accuracy. Decrease to increase speed",
default="128", type=str)
return parser.parse_args()
args = arg_parse()
confidence = float(args.confidence)
nms_thesh = float(args.nms_thresh)
start = 0
CUDA = torch.cuda.is_available()
num_classes = 80
bbox_attrs = 5 + num_classes
print("Loading network")
model = Darknet(args.cfgfile)
model.load_weights(args.weightsfile)
print("Network loaded")
classes = load_classes('data/coco.names')
colors = pkl.load(open("pallete", "rb"))
model.DNInfo["height"] = args.reso
inp_dim = int(model.DNInfo["height"])
if CUDA:
model.cuda()
model.eval()
videofile = args.video
cap = cv2.VideoCapture(videofile)
assert cap.isOpened(), 'Cannot capture source'
while cap.isOpened():
ret, frame = cap.read()
if ret:
img, orig_im, dim = prepare_input(frame, inp_dim)
im_dim = torch.FloatTensor(dim).repeat(1, 2)
if CUDA:
im_dim = im_dim.cuda()
img = img.cuda()
with torch.no_grad():
output = model(Variable(img), CUDA)
output = write_results(output, confidence, num_classes, nms=True, nms_conf=nms_thesh)
if type(output) == int:
cv2.imshow("frame", orig_im)
key = cv2.waitKey(1)
if key & 0xFF == ord('x'):
break
continue
im_dim = im_dim.repeat(output.size(0), 1)
scaling_factor = torch.min(inp_dim / im_dim, 1)[0].view(-1, 1)
output[:, [1, 3]] -= (inp_dim - scaling_factor * im_dim[:, 0].view(-1, 1)) / 2
output[:, [2, 4]] -= (inp_dim - scaling_factor * im_dim[:, 1].view(-1, 1)) / 2
output[:, 1:5] /= scaling_factor
for i in range(output.shape[0]):
output[i, [1, 3]] = torch.clamp(output[i, [1, 3]], 0.0, im_dim[i, 0])
output[i, [2, 4]] = torch.clamp(output[i, [2, 4]], 0.0, im_dim[i, 1])
labels = []
list(map(lambda x: write(x, orig_im), output))
#print("count:", len(labels))
count = len(labels)
# print(count)
cv2.imshow("frame", orig_im)
key = cv2.waitKey(1)
if key & 0xFF == ord('x'):
break
else:
break
return count
if __name__ == '__main__':
args = arg_parse()
confidence = float(args.confidence)
nms_thesh = float(args.nms_thresh)
start = 0
CUDA = torch.cuda.is_available()
num_classes = 80
bbox_attrs = 5 + num_classes
print("Loading network")
model = Darknet(args.cfgfile)
model.load_weights(args.weightsfile)
print("Network loaded")
classes = load_classes('data/coco.names')
colors = pkl.load(open("pallete", "rb"))
model.DNInfo["height"] = args.reso
inp_dim = int(model.DNInfo["height"])
if CUDA:
model.cuda()
model.eval()
videofile = 'video.avi'
# Input resolution of the network. Increase to increase accuracy. Decrease to increase speed
reso = 128
start = 0
# CUDA tensor types, that implement the same function as CPU tensors, but utilizes GPU for computation
# My laptop does not have GPU therefore I can't test this functionality.
CUDA = torch.cuda.is_available()
# 80 catagories have been trained in the model
# see coco.names
num_classes = 80
print("Loading network")
model = Darknet(cfgfile)
model.load_weights(weightsfile)
print("Network loaded")
classes = load_classes('data/coco.names')
model.DNInfo["height"] = reso
inp_dim = int(model.DNInfo["height"])
model.eval()
videofile = video
cap = cv2.VideoCapture(videofile)
assert cap.isOpened(), 'Cannot capture source: was the correct movie loaded?'
print("YOLO test program running press escape to exit")
