import os import random import numpy as np import pandas as pd from PIL import Image from torchvision import datasets, transforms, io import torch def get_random_texture(dataset_occluder): index = random.randint(0, len(dataset_occluder) - 1) texture_path, texture_class_index = dataset_occluder.imgs[index] texture_class = dataset_occluder.classes[texture_class_index] # Load the texture with the alpha channel texture = io.read_image(texture_path, mode=io.image.ImageReadMode.RGB_ALPHA) return texture, texture_class def resize_occluder(occluder_pil, target_area, image_width, image_height): alpha = np.array(occluder_pil.getchannel('A')) non_transparent_area = np.count_nonzero(alpha > 0) area_scale_factor = target_area / non_transparent_area width_scale_factor = np.sqrt(area_scale_factor * (occluder_pil.width / occluder_pil.height)) height_scale_factor = np.sqrt(area_scale_factor * (occluder_pil.height / occluder_pil.width)) new_width = occluder_pil.width * width_scale_factor new_height = occluder_pil.height * height_scale_factor resized_occluder = occluder_pil.resize((int(new_width), int(new_height)), Image.LANCZOS) return resized_occluder def randomly_rotate_occluder(occluder_pil): angle = random.uniform(-180, 180) return occluder_pil.rotate(angle, resample=Image.BICUBIC, expand=True) def occlude_image(image, occluder_tensor, percentage_occlusion, occluded_dir, img_name): occluder_pil = transforms.ToPILImage(mode='RGBA')(occluder_tensor) occluder_pil = randomly_rotate_occluder(occluder_pil) image_pil = transforms.ToPILImage()(image) target_area = image_pil.width * image_pil.height * percentage_occlusion occluder_pil = resize_occluder(occluder_pil, target_area, image_pil.width, image_pil.height) if occluder_pil.width > image_pil.width or occluder_pil.height > image_pil.height: pos = (image_pil.width // 2 - occluder_pil.width // 2, image_pil.height // 2 - occluder_pil.height // 2) else: max_x = max(0, image_pil.width - occluder_pil.width) max_y = max(0, image_pil.height - occluder_pil.height) pos = (random.randint(0, max_x), random.randint(0, max_y)) image_pil.paste(occluder_pil, pos, occluder_pil) image_with_occluder_tensor = transforms.ToTensor()(image_pil) occluder_alpha = occluder_pil.getchannel('A') binary_mask = Image.new('1', image_pil.size) binary_mask.paste(occluder_alpha, pos, occluder_alpha) mask_array = np.array(binary_mask) mask_path = os.path.join(occluded_dir, f"{img_name}_mask.npy") np.save(mask_path, mask_array) return image_with_occluder_tensor, mask_path, pos def rebuild_display_mask(image_path, mask_path): image_pil = Image.open(image_path) binary_mask = Image.new('1', image_pil.size) mask_array = np.load(mask_path) mask_indices = np.transpose(np.nonzero(mask_array)) for i, j in mask_indices: binary_mask.putpixel((j, i), 1) binary_mask.show() def build_dataset(data_path, transform): dataset = datasets.ImageFolder(data_path, transform=transform) nb_classes = len(dataset.classes) return dataset, nb_classes def build_transform(): t = [] t.append(transforms.ToTensor()) return transforms.Compose(t) def main(): data_dir = 'imagenet1' texture_dir = 'occluders_segmented' occluded_data_dir = 'imagenet_occluded' transform = build_transform() dataset, nb_classes = build_dataset(data_dir, transform) dataset_occluder, _ = build_dataset(texture_dir, transform) occlusion_info = pd.DataFrame(columns=["image_name", "class_name", "occluder_class", "percentage_occlusion", "mask", "pos"]) for idx in range(len(dataset)): image, label = dataset[idx] category = dataset.classes[label] in_dir = os.path.join(data_dir, category) occluded_dir = os.path.join(occluded_data_dir, category) os.makedirs(occluded_dir, exist_ok=True) img_name = dataset.imgs[idx][0].split('/')[-1].split('.')[0] occluder_tensor, occluder_class = get_random_texture(dataset_occluder) occluded_image, mask_path, pos = occlude_image(image, occluder_tensor, 0.5, occluded_dir, img_name) mask_array = np.load(mask_path) actual_percentage_occlusion = np.count_nonzero(mask_array) / (image.shape[1] * image.shape[2]) occluded_image_path = os.path.join(occluded_dir, f"{img_name}_occluded.png") transforms.ToPILImage()(occluded_image).save(occluded_image_path) new_row = pd.DataFrame({ "image_name": [f"{img_name}_occluded.png"], "class_name": [category], "occluder_class": [occluder_class], "percentage_occlusion": [actual_percentage_occlusion], "mask": [mask_path], "pos": [pos] }) occlusion_info = pd.concat([occlusion_info, new_row], ignore_index=True) occlusion_info.to_csv(os.path.join(occluded_data_dir, "occlusion_info.csv"), index=False) if __name__ == "__main__": main()