def test_detect(setup_tf_eager, setup_darknet_weights):
darknet_weights = setup_darknet_weights
image_path = os.path.join(PROJECT_ROOT, "tests", "samples", "sample.jpeg")
# 1. create yolo model & load weights
yolov3 = Yolonet()
yolov3.load_darknet_params(darknet_weights)
# 2. preprocess the image
image = cv2.imread(image_path)
image = image[:,:,::-1]
d = YoloDetector(yolov3, COCO_ANCHORS, net_size=416)
boxes, labels, probs = d.detect(image)
assert len(boxes) == 31
def create_detector(self, model):
d = YoloDetector(model, self._model_config["anchors"], net_size=self._model_config["net_size"])
return d
def create_detector(self, model):
detector = YoloDetector(model,
self._model_config['anchors'],
net_size=self._model_config['net_size'])
return detector
def detect(self, image, threshold=.5):
detector = YoloDetector(self.yolonet(),
self.anchors,
net_size=self.net_size)
return detector.detect(image, threshold) # boxes, labels, probs
print(train_generator.steps_per_epoch)
# 2. create model
model = Yolonet(n_classes=len(config["model"]["labels"]))
model.load_darknet_params(config["pretrained"]["darknet_format"], skip_detect_layer=True)
# 4. traini
train_fn(model,
train_generator,
valid_generator,
learning_rate=config["train"]["learning_rate"],
save_dname=config["train"]["save_folder"],
num_epoches=config["train"]["num_epoch"])
# 5. prepare sample images
img_fnames = glob.glob(os.path.join(config["train"]["train_image_folder"], "*.*"))
imgs = [cv2.imread(fname)[:,:,::-1] for fname in img_fnames]
# 6. create new model & load trained weights
model = Yolonet(n_classes=len(config["model"]["labels"]))
model.load_weights(os.path.join(config["train"]["save_folder"], "weights.h5"))
detector = YoloDetector(model)
# 7. predict & plot
boxes = detector.detect(imgs[0], config["model"]["anchors"])
image = draw_boxes(imgs[0], boxes, labels=config["model"]["labels"])
plt.imshow(image)
plt.show()
if __name__ == '__main__':
args = argparser.parse_args()
image_path = args.image
import json
with open(args.config) as data_file:
config = json.load(data_file)
# Download if not exits weight file
download_if_not_exists(config["pretrained"]["darknet_format"],
"https://pjreddie.com/media/files/yolov3.weights")
# 1. create yolo model & load weights
yolov3 = Yolonet()
yolov3.load_darknet_params(config["pretrained"]["darknet_format"])
# 2. preprocess the image
image = cv2.imread(image_path)
image = image[:, :, ::-1]
d = YoloDetector(yolov3)
boxes = d.detect(image, COCO_ANCHORS, net_size=416)
# 4. draw detected boxes
image = draw_boxes(image, boxes, config["model"]["labels"], obj_thresh=0.5)
# 5. plot
plt.imshow(image)
plt.show()