def main(): ## #ext = '.png' ## set specific classes #class_names = ['bird', 'bottle', 'chair'] ## path, txt_file = process_arguments(sys.argv) # remove old files clear_class_logs(class_names) # get interested classes indexes class_ids = get_id_classes(class_names) # get from labels image list with open(txt_file, 'rb') as f: i = 0 for img_name in f: # delete white space in prefix and suffix img_name = img_name.strip() detected_class = contain_class( os.path.join(path, img_name) + ext, class_ids, class_names) if detected_class: log_class(img_name, detected_class) print("No.%d: %s --> detect class: %s" % (i, img_name, detected_class)) else: print("No.%d: %s --> no class" % (i, img_name)) i += 1
def main(): ## # ext = '.png' ## set specific classes # class_names = ['bird', 'bottle', 'chair'] ## path, txt_file = process_arguments(sys.argv) # remove old files clear_class_logs(class_names) # get interested classes indexes class_ids = get_id_classes(class_names) # get from labels image list with open(txt_file, "rb") as f: i = 0 for img_name in f: # delete white space in prefix and suffix img_name = img_name.strip() detected_class = contain_class(os.path.join(path, img_name) + ext, class_ids, class_names) if detected_class: log_class(img_name, detected_class) print("No.%d: %s --> detect class: %s" % (i, img_name, detected_class)) else: print("No.%d: %s --> no class" % (i, img_name)) i += 1
def main(): base_dir = 'exper/voc12' gpu_id, model_name, iteration_num, phase, subset_dataset = process_arguments(sys.argv) if phase == 1: model_path = os.path.join(base_dir, 'model', model_name, 'train_iter_{}.caffemodel') elif phase == 2: model_path = os.path.join(base_dir, 'model', model_name, 'train2_iter_{}.caffemodel') if subset_dataset: net_path = os.path.join(model_name, 'deploy4.prototxt') class_names = ['bird', 'bottle', 'chair'] # CHANGE class_ids = get_id_classes(class_names) file_names = load_test_data(os.path.join(base_dir, 'list_subset/val_id.txt')) images_path = os.path.join(base_dir, 'data/images_orig') labels_path = os.path.join(base_dir, 'data/labels_sub_orig') else: net_path = os.path.join(model_name, 'deploy21.prototxt') class_ids = range(1,21) file_names = load_test_data(os.path.join(base_dir, 'list/val_id.txt')) images_path = os.path.join(base_dir, 'data/images_orig') labels_path = os.path.join(base_dir, 'data/labels_orig') lut = create_lut(class_ids) images, labels = create_full_paths(file_names, images_path, labels_path) test_net(net_path, model_path.format(iteration_num), images, labels, lut, gpu_id)
def main(): ## preprocess_mode = 'pad' im_sz = 500 class_names = ['bird', 'bottle', 'chair'] test_ratio = 0.1 image_ext = '.jpg' label_ext = '.png' ## labels_path, train_list, test_list = process_arguments(sys.argv) if train_list != None: # all classes in dataset defined using txt files class_ids = range(1,21) train_imgs, test_imgs = load_train_test_lists(train_list, test_list) else: # only specific class_labels class_ids = get_id_classes(class_names) train_imgs, test_imgs = split_train_test_imgs(class_names, test_ratio) save_test_images(test_imgs) shuffle(train_imgs) shuffle(test_imgs) num_classes = str(len(class_ids)) ## Train # Images print('Train images') path_src = 'images/' path_dst = 'train_images_' + num_classes + '_lmdb' convert2lmdb(path_src, train_imgs, image_ext, path_dst, class_ids, preprocess_mode, im_sz, 'image') # Labels print('Train labels') if labels_path: path_src = labels_path else: path_src = 'labels/' path_dst = 'train_labels_' + num_classes + '_lmdb' convert2lmdb(path_src, train_imgs, label_ext, path_dst, class_ids, preprocess_mode, im_sz, 'label') ## Test # Images print('Test images') path_src = 'images/' path_dst = 'test_images_' + num_classes + '_lmdb' convert2lmdb(path_src, test_imgs, image_ext, path_dst, class_ids, preprocess_mode, im_sz, 'image') # Labels print('Test labels') if labels_path: path_src = labels_path else: path_src = 'labels/' path_dst = 'test_labels_' + num_classes + '_lmdb' convert2lmdb(path_src, test_imgs, label_ext, path_dst, class_ids, preprocess_mode, im_sz, 'label')
def main(): ## preprocess_mode = 'pad' im_sz = 500 class_names = ['bird', 'bottle', 'chair'] test_ratio = 0.1 image_ext = '.jpg' label_ext = '.png' ## labels_path, train_list, test_list = process_arguments(sys.argv) if train_list != None: # all classes in dataset defined using txt files class_ids = range(1, 21) train_imgs, test_imgs = load_train_test_lists(train_list, test_list) else: # only specific class_labels class_ids = get_id_classes(class_names) train_imgs, test_imgs = split_train_test_imgs(class_names, test_ratio) save_test_images(test_imgs) shuffle(train_imgs) shuffle(test_imgs) num_classes = str(len(class_ids)) ## Train # Images print('Train images') path_src = 'images/' path_dst = 'train_images_' + num_classes + '_lmdb' convert2lmdb(path_src, train_imgs, image_ext, path_dst, class_ids, preprocess_mode, im_sz, 'image') # Labels print('Train labels') if labels_path: path_src = labels_path else: path_src = 'labels/' path_dst = 'train_labels_' + num_classes + '_lmdb' convert2lmdb(path_src, train_imgs, label_ext, path_dst, class_ids, preprocess_mode, im_sz, 'label') ## Test # Images print('Test images') path_src = 'images/' path_dst = 'test_images_' + num_classes + '_lmdb' convert2lmdb(path_src, test_imgs, image_ext, path_dst, class_ids, preprocess_mode, im_sz, 'image') # Labels print('Test labels') if labels_path: path_src = labels_path else: path_src = 'labels/' path_dst = 'test_labels_' + num_classes + '_lmdb' convert2lmdb(path_src, test_imgs, label_ext, path_dst, class_ids, preprocess_mode, im_sz, 'label')
def main(): iteration_num = process_arguments(sys.argv) prototxt = 'TVG_CRFRNN_COCO_VOC_TEST_3_CLASSES.prototxt' caffemodel = 'models/train_iter_{}.caffemodel' class_names = ['bird', 'bottle', 'chair'] class_ids = get_id_classes(class_names) lut = create_lut(class_ids) file_names = load_test_data() images, labels = create_full_paths(file_names, 'images', 'labels') test_net(prototxt, caffemodel.format(iteration_num), images, labels, lut)
def main(): ## ext = '.png' class_names = ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'] ## path, txt_file = process_arguments(sys.argv) clear_class_logs(class_names) class_ids = get_id_classes(class_names) with open(txt_file, 'rb') as f: for img_name in f: img_name = img_name.strip() detected_class = contain_class(os.path.join(path, img_name)+ext, class_ids, class_names) if detected_class: log_class(img_name, detected_class)
def main(): ## ext = '.png' class_names = ['bird', 'bottle', 'chair'] ## path, txt_file = process_arguments(sys.argv) clear_class_logs(class_names) class_ids = get_id_classes(class_names) with open(txt_file, 'rb') as f: for img_name in f: img_name = img_name.strip() detected_class = contain_class(os.path.join(path, img_name)+ext, class_ids, class_names) if detected_class: log_class(img_name, detected_class)
def main(): ## ext = '.png' class_names = ['bird', 'bottle', 'chair'] ## path, txt_file = process_arguments(sys.argv) clear_class_logs(class_names) class_ids = get_id_classes(class_names) with open(txt_file, 'rb') as f: for img_name in f: img_name = img_name.strip() detected_class = contain_class( os.path.join(path, img_name) + ext, class_ids, class_names) if detected_class: log_class(img_name, detected_class)
def main(): ## ext = '.png' class_names = ['bird', 'bottle', 'chair'] ## input_path, output_path, list_file, subset_data_file = process_arguments(sys.argv) clear_subset_list_logs(subset_data_file) class_ids = get_id_classes(class_names) lut = create_lut(class_ids) with open(list_file, 'rb') as f: for img_name in f: img_name = img_name.strip() img = contain_class(os.path.join(input_path, img_name)+ext, class_ids, lut) if img != None: log_image(img_name, subset_data_file) imsave(os.path.join(output_path, img_name)+ext, img)
def main(): labels_path = process_arguments(sys.argv) class_ids = get_id_classes(class_names) train_imgs, test_imgs = split_train_test_imgs(class_names, test_ratio) ## Train # Images print('Train images') path_src = 'images/' path_dst = 'train_images_3_lmdb' convert2lmdb(path_src, train_imgs, image_ext, path_dst, class_ids, preprocess_mode, im_sz, 'image') # Labels print('Train labels') if labels_path: path_src = labels_path else: path_src = 'labels/' path_dst = 'train_labels_3_lmdb' convert2lmdb(path_src, train_imgs, label_ext, path_dst, class_ids, preprocess_mode, im_sz, 'label') ## Test # Images print('Test images') path_src = 'images/' path_dst = 'test_images_3_lmdb' convert2lmdb(path_src, test_imgs, image_ext, path_dst, class_ids, preprocess_mode, im_sz, 'image') # Labels print('Test labels') if labels_path: path_src = labels_path else: path_src = 'labels/' path_dst = 'test_labels_3_lmdb' convert2lmdb(path_src, test_imgs, label_ext, path_dst, class_ids, preprocess_mode, im_sz, 'label')
def main(): ## preprocess_mode = 'pad' im_sz = 500 #file_src_images = 'train.txt' class_names = ['bird', 'bottle', 'chair'] test_ratio = 0.1 ## ext = '.png' class_ids = get_id_classes(class_names) train_imgs, test_imgs = split_train_test_imgs(class_names, ext, test_ratio) ## Train # Images print('Train images') path_src = 'images/' path_dst = 'train_images_3_lmdb' #train_imgs = get_src_imgs(file_src_images, ext) convert2lmdb(path_src, train_imgs, path_dst, class_ids, preprocess_mode, im_sz, 'image') # Labels print('Train labels') path_src = 'labels/' path_dst = 'train_labels_3_lmdb' #train_imgs = get_src_imgs(file_src_images, ext) convert2lmdb(path_src, train_imgs, path_dst, class_ids, preprocess_mode, im_sz, 'label') ## Test # Images print('Test images') path_src = 'images/' path_dst = 'test_images_3_lmdb' convert2lmdb(path_src, test_imgs, path_dst, class_ids, preprocess_mode, im_sz, 'image') # Labels print('Test labels') path_src = 'labels/' path_dst = 'test_labels_3_lmdb' convert2lmdb(path_src, test_imgs, path_dst, class_ids, preprocess_mode, im_sz, 'label')
def main(): ## ext = '.png' class_names = ['bird', 'bottle', 'chair'] ## input_path, output_path, list_file, subset_data_file = process_arguments( sys.argv) clear_subset_list_logs(subset_data_file) class_ids = get_id_classes(class_names) lut = create_lut(class_ids) with open(list_file, 'rb') as f: for img_name in f: img_name = img_name.strip() img = contain_class( os.path.join(input_path, img_name) + ext, class_ids, lut) if img != None: log_image(img_name, subset_data_file) imsave(os.path.join(output_path, img_name) + ext, img)