if cv2.waitKey(1) & 0xFF == ord('q'):
break
camera.release()
cv2.destroyAllWindows()
if __name__ == "__main__":
from models.ssd import SSD300
from utils.boxes import create_prior_boxes
from utils.boxes import to_point_form
dataset_name = 'VOC2007'
# weights_path = '../trained_models/SSD300_weights.hdf5'
# weights_path = '../trained_models/weights.07-3.59.hdf5'
# weights_path = '../trained_models/weights.03-3.37.hdf5'
# weights_path = '../trained_models/weights.150-3.57.hdf5'
# weights_path = '../trained_models/weights.12-4.20.hdf5'
# weights_path = '../trained_models/weights.02-3.44.hdf5'
# weights_path = '../trained_models/weights.22-5.01.hdf5'
# weights_path = '../trained_models/weights.79-6.66.hdf5'
# weights_path = '../trained_models/weights.64-6.52.hdf5'
# weights_path = '../trained_models/weights.22-3.85.hdf5'
# weights_path = '../trained_models/weights.50-3.92.hdf5'
weights_path = '../trained_models/weights.04-3.79.hdf5'
model = SSD300(weights_path=weights_path)
prior_boxes = to_point_form(create_prior_boxes())
# prior_boxes = to_point_form(prior_boxes)
video = VideoDemo(prior_boxes, dataset_name)
video.start_video(model)
class_names = dataset_manager.class_names
print('Found:', len(ground_truth_data), 'images')
print('Class names: \n', class_names)
# prior boxes
# ------------------------------------------------------------------
# model = SSD300()
prior_boxes = create_prior_boxes()
print('Prior boxes shape:', prior_boxes.shape)
print('Prior box example:', prior_boxes[777])
image_path = '../images/fish-bike.jpg'
# input_shape = model.input_shape[1:3]
input_shape = (300, 300)
image_array = load_image(image_path, input_shape)
prior_boxes = to_point_form(prior_boxes)
box_coordinates = prior_boxes[7010:7015, :]
plot_box_data(box_coordinates, image_array)
plt.imshow(image_array)
plt.show()
# ground truth
# ------------------------------------------------------------------
image_name, box_data = random.sample(ground_truth_data.items(), 1)[0]
print('Data sample: \n', box_data)
# image_path = dataset_manager.images_path + image_name
image_path = image_name
arg_to_class = dataset_manager.arg_to_class
colors = get_colors(len(class_names))
image_array = load_image(image_path, input_shape)
plot_box_data(box_data, image_array, arg_to_class, colors=colors)
