from zedstreamer import ZedCamera
if __name__ == '__main__':
data = parse_data("data/tennisball.data")
CUDA = torch.cuda.is_available() and data["use_cuda"]
device = torch.device("cuda" if CUDA else "cpu")
confidence = float(data["confidence"])
nmsThresh = float(data["nms_thresh"])
# FIXME: Change this to match number of classes in names file
# AND change the network's yolo layers to match
numClasses = 80
classes = loadClasses("names/coco.names")
model = Darknet("cfg/yolov3.cfg")
model.loadStateDict("weights/yolov3-320.pt")
# model.loadWeight("weights/yolov3-320.weights")
inpDim = int(data["reso"])
# If there's a GPU availible, put the model on GPU
model.to(device)
# Set the model in evaluation mode. Notifies network to not train
model.eval()
# Detection phase
zed = ZedCamera()
# stream = cv2.VideoCapture(0)
frames = 0
start = time.time()
if __name__ == '__main__':
data = parse_data("data/tennisball_VAL.data")
CUDA = torch.cuda.is_available() and data["use_cuda"]
device = torch.device("cuda" if CUDA else "cpu")
confidence = float(data["confidence"])
nmsThresh = float(data["nms_thresh"])
# FIXME: Change this to match number of classes in names file
# AND change the network's yolo layers to match
numClasses = 1
classes = loadClasses(data["names"])
model = Darknet(data["cfg"])
# model.loadWeight(data["weights"])
model.loadStateDict("checkpoints/epoch_95.pt")
inpDim = int(data["reso"])
# If there's a GPU availible, put the model on GPU
model.to(device)
# Set the model in evaluation mode. Notifies network to not train
model.eval()
# Detection phase
zed = ZedCamera()
zed.resetSettings()
# zed.setCamSettings(brightness=4,
# contrast=0,
# hue=0,
