from data_augmentation import SSDAugmentation from utils.datasets import DataManager from utils.preprocessing import load_image from utils.datasets import get_arg_to_class from utils.visualizer import draw_image_boxes import cv2 # import numpy as np size = 300 dataset_name = 'VOC2007' class_names = ['background', 'aeroplane'] class_to_arg = get_arg_to_class(class_names) # transformer = SSDAugmentation(size, mean=(104, 117, 123)) transformer = SSDAugmentation(size, mean=(0, 0, 0)) data_manager = DataManager(dataset_name, class_names) data = data_manager.load_data() for image_key in data.keys(): image_path = data_manager.image_prefix + image_key print(cv2.imread(image_path)) image_array = load_image(image_path, (size, size))[0] original_image_array = load_image(image_path, None)[0] ground_truth_data = data[image_key] boxes = ground_truth_data[:, :4] labels = ground_truth_data[:, 4:] transformed_data = transformer(image_array, boxes, labels) image_array, boxes, labels = transformed_data # image_array = image_array[:, :, ::-1] image_array = image_array.astype('uint8') draw_image_boxes(boxes, image_array, class_to_arg, normalized=True)
# model = flip_l2_weights(model) prior_boxes = create_prior_boxes() input_shape = model.input_shape[1:3] class_threshold = .1 iou_threshold = .45 average_precisions = [] # range(1, 21) for ground_truth_class_arg in range(1, 21): labels = [] scores = [] class_names = get_class_names(dataset_name) # ground_truth_class_arg = class_arg selected_classes = [class_names[0]] + [class_names[ground_truth_class_arg]] num_ground_truth_boxes = 0 class_decoder = get_arg_to_class(class_names) num_classes = len(class_names) data_manager = DataManager(dataset_name, selected_classes, data_prefix, image_prefix) ground_truth_data = data_manager.load_data() difficult_data_flags = data_manager.parser.difficult_objects image_names = sorted(list(ground_truth_data.keys())) # print('Number of images found:', len(image_names)) for image_name in image_names: ground_truth_sample = ground_truth_data[image_name] image_prefix = data_manager.image_prefix image_path = image_prefix + image_name image_array, original_image_size = load_image(image_path, input_shape) image_array = preprocess_images(image_array) predicted_data = predict(model, image_array, prior_boxes,