# 部署 teed、depth-anything # 腐蚀算法 # 读取图片 # 输出图片 # 使用 depth-anything + teed 生成外轮廓 # 使用 teed + 腐蚀算法 生成内边缘 import zipfile from PIL import Image import cv2 import cv2_ext import numpy as np import gradio as gr import os import torch import torch.nn.functional as F from torchvision.transforms import Compose from tqdm import tqdm import TEED.main as teed from TEED.main import parse_args import logging from depthAnything.depth_anything.dpt import DepthAnything from depthAnything.depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet import shutil def multiply_blend(image1, image2): # 将图片转换为浮点数，方便计算 # Ensure image2 has the same shape as image1 image2 = np.stack((image2,) * 3, axis=-1) # Perform the blending multiplied = np.multiply(image1 / 255.0, image2 / 255.0) * 255.0 return multiplied.astype(np.uint8) # Example usage image1 = np.random.randint(0, 256, (717, 790, 3), dtype=np.uint8) image2 = np.random.randint(0, 256, (717, 790), dtype=np.uint8) result = multiply_blend(image1, image2) print(result.shape) # Should be (717, 790, 3) def screen_blend(image1, image2): # 将图片转换为浮点数，方便计算 image1 = image1.astype(float) image2 = image2.astype(float) # 执行滤色操作 screened = 1 - (1 - image1 / 255) * (1 - image2 / 255) * 255 # 将结果转换回uint8 result = np.clip(screened, 0, 255).astype('uint8') return result def erosion(img, kernel_size=3, iterations=1, dilate=False): # 灰度化 if len(img.shape) == 3: img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # # 二值化 # _, img = cv2.threshold(img, 200, 255, cv2.THRESH_BINARY) # 腐蚀 kernel = np.ones((kernel_size, kernel_size), np.uint8) if dilate: img = cv2.dilate(img, kernel, iterations=iterations) else: img = cv2.erode(img, kernel, iterations=iterations) return img def erosion_img_from_path(img_path, output_dir='./output/erosion_img', kernel_size=3, iterations=1, dilate=False): # 读取图片 if os.path.isfile(img_path): name, extension = os.path.splitext(img_path) if extension: if extension.lower() == 'txt': with open(img_path, 'r', encoding='utf-8') as f: filenames = f.read().splitlines() elif extension.lower() in ['.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff', '.webp', 'tif']: filenames = [img_path] else: filenames = os.listdir(img_path) filenames = [os.path.join(img_path, filename) for filename in filenames if not filename.startswith('.') and filename.lower().endswith( ('.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff', '.webp', 'tif'))] filenames.sort() os.makedirs(output_dir, exist_ok=True) for filename in tqdm(filenames): img = cv2.imread(filename) img = erosion(img, kernel_size, iterations, dilate) cv2.imwrite(os.path.join(output_dir, os.path.basename(filename)), img) def copy_file(src, dest): # 移动文件 source = src destination = dest try: shutil.copy(source, destination) except IOError as e: print("Unable to copy file. %s" % e) def guassian_blur_path(img_path, output_dir='./output/guassian_blur', kernel_size=3, sigmaX=0): # 读取图片 if os.path.isfile(img_path): name, extension = os.path.splitext(img_path) if extension: if extension.lower() == 'txt': with open(img_path, 'r', encoding='utf-8') as f: filenames = f.read().splitlines() elif extension.lower() in ['.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff', '.webp', 'tif']: filenames = [img_path] else: filenames = os.listdir(img_path) filenames = [os.path.join(img_path, filename) for filename in filenames if not filename.startswith('.') and filename.lower().endswith( ('.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff', '.webp', 'tif'))] filenames.sort() os.makedirs(output_dir, exist_ok=True) for filename in tqdm(filenames): img = cv2.imread(filename) img = cv2.GaussianBlur(img, (kernel_size, kernel_size), sigmaX) cv2.imwrite(os.path.join(output_dir, os.path.basename(filename)), img) def depth_anything(img_path='./input', outdir='./output/depth_anything', encoder='vitl', pred_only=True, grayscale=True): # parser = argparse.ArgumentParser() # parser.add_argument('--img-path', type=str) # parser.add_argument('--outdir', type=str, default='./vis_depth') # parser.add_argument('--encoder', type=str, default='vitl', choices=['vits', 'vitb', 'vitl']) # parser.add_argument('--pred-only', dest='pred_only', action='store_true', help='only display the prediction') # parser.add_argument('--grayscale', dest='grayscale', action='store_true', help='do not apply colorful palette') # args = parser.parse_args() margin_width = 50 caption_height = 60 font = cv2.FONT_HERSHEY_SIMPLEX font_scale = 1 font_thickness = 2 DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' model_configs = { 'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]}, 'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]}, 'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]} } depth_anything = DepthAnything(model_configs[encoder]) depth_anything.load_state_dict(torch.load('./checkpoints/depth_anything_{}14.pth'.format(encoder))) depth_anything = depth_anything.to(DEVICE).eval() total_params = sum(param.numel() for param in depth_anything.parameters()) print('Total parameters: {:.2f}M'.format(total_params / 1e6)) transform = Compose([ Resize( width=518, height=518, resize_target=False, keep_aspect_ratio=True, ensure_multiple_of=14, resize_method='lower_bound', image_interpolation_method=cv2.INTER_CUBIC, ), NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), PrepareForNet(), ]) if os.path.isfile(img_path): name, extension = os.path.splitext(img_path) if extension: if extension.lower() == 'txt': with open(img_path, 'r', encoding='utf-8') as f: filenames = f.read().splitlines() elif extension.lower() in ['.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff', '.webp', 'tif']: filenames = [img_path] else: filenames = os.listdir(img_path) filenames = [os.path.join(img_path, filename) for filename in filenames if not filename.startswith('.') and filename.lower().endswith( ('.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff', '.webp', 'tif'))] filenames.sort() os.makedirs(outdir, exist_ok=True) for filename in tqdm(filenames): raw_image = cv2.imread(filename) image = cv2.cvtColor(raw_image, cv2.COLOR_BGR2RGB) / 255.0 h, w = image.shape[:2] image = transform({'image': image})['image'] image = torch.from_numpy(image).unsqueeze(0).to(DEVICE) with torch.no_grad(): depth = depth_anything(image) depth = F.interpolate(depth[None], (h, w), mode='bilinear', align_corners=False)[0, 0] depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0 depth = depth.cpu().numpy().astype(np.uint8) if grayscale: depth = np.repeat(depth[..., np.newaxis], 3, axis=-1) else: depth = cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO) filename = os.path.basename(filename) if pred_only: cv2.imwrite(os.path.join(outdir, filename[:filename.rfind('.')] + '_depth.png'), depth) else: split_region = np.ones((raw_image.shape[0], margin_width, 3), dtype=np.uint8) * 255 combined_results = cv2.hconcat([raw_image, split_region, depth]) caption_space = np.ones((caption_height, combined_results.shape[1], 3), dtype=np.uint8) * 255 captions = ['Raw image', 'Depth Anything'] segment_width = w + margin_width for i, caption in enumerate(captions): # Calculate text size text_size = cv2.getTextSize(caption, font, font_scale, font_thickness)[0] # Calculate x-coordinate to center the text text_x = int((segment_width * i) + (w - text_size[0]) / 2) # Add text caption cv2.putText(caption_space, caption, (text_x, 40), font, font_scale, (0, 0, 0), font_thickness) final_result = cv2.vconcat([caption_space, combined_results]) cv2.imwrite(os.path.join(outdir, filename[:filename.rfind('.')] + '_img_depth.png'), final_result) def teed_imgs(img_path='./input', outdir='./output/teed_imgs', gaussianBlur=[0, 3, 0]): args, train_info = parse_args(is_testing=True, pl_opt_dir=outdir) os.makedirs('teed_tmp', exist_ok=True) if os.path.isfile(img_path): name, extension = os.path.splitext(img_path) if extension: if extension.lower() == 'txt': with open(img_path, 'r', encoding='utf-8') as f: filenames = f.read().splitlines() elif extension.lower() in ['.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff', '.webp', 'tif']: filenames = [img_path] else: filenames = os.listdir(img_path) filenames = [os.path.join(img_path, filename) for filename in filenames if not filename.startswith('.') and filename.lower().endswith( ('.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff', '.webp', 'tif'))] filenames.sort() for filename in tqdm(filenames): if gaussianBlur[0] != 0: img = cv2.imread(filename) img = cv2.GaussianBlur(img, (gaussianBlur[1], gaussianBlur[1]), gaussianBlur[2]) cv2.imwrite(os.path.join('teed_tmp', os.path.basename(filename)), img) else: copy_file(filename, 'teed_tmp') teed.main(args, train_info) shutil.rmtree('teed_tmp') def merge_2_images(img1, img2, mode, erosion_para=[[0, 0], [0, 0]], dilate=[0, 0]): # 将 img1 合并至 img2，调整大小与 img2 相同 img1 = cv2.imread(img1) img2 = cv2.imread(img2) img1 = cv2.resize(img1, (img2.shape[1], img2.shape[0])) if erosion_para[0][1] != 0: img1 = erosion(img1, erosion_para[0][0], erosion_para[0][1], dilate[0]) if erosion_para[1][1] != 0: img2 = erosion(img2, erosion_para[1][0], erosion_para[1][1], dilate[1]) if mode == 'multiply': return multiply_blend(img1, img2) elif mode == 'screen': return screen_blend(img1, img2) def merge_images_in_2_folder(folder1, folder2, outdir, suffix_need_remove=None, suffix_floder=0, mode='multiply', erosion_para=[[0, 0], [0, 0]], dilate=[0, 0]): # 将 folder1 和 folder2 中的图片合并，可选是否移除某文件夹后缀，可选腐蚀参数[kernel_size,iterations] os.makedirs(outdir, exist_ok=True) name_extension_pairs_folder1 = [os.path.splitext(filename) for filename in os.listdir(folder1) if filename.endswith( ('.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff', '.webp', 'tif'))] filenames_noext_folder1, extensions_folder1 = zip(*name_extension_pairs_folder1) name_extension_pairs_folder2 = [os.path.splitext(filename) for filename in os.listdir(folder2) if filename.endswith( ('.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff', '.webp', 'tif'))] filenames_noext_folder2, extensions_folder2 = zip(*name_extension_pairs_folder2) if suffix_need_remove: if suffix_floder == 0: filenames_raw = list(filenames_noext_folder1).copy() filenames_noext_folder1 = [ filename[:-len(suffix_need_remove)] + filename[-len(suffix_need_remove):].replace(suffix_need_remove, '') for filename in filenames_noext_folder1] if suffix_floder == 1: filenames_raw = list(filenames_noext_folder2).copy() filenames_noext_folder2 = [ filename[:-len(suffix_need_remove)] + filename[-len(suffix_need_remove):].replace(suffix_need_remove, '') for filename in filenames_noext_folder2] for index, filename in enumerate(filenames_noext_folder1): if filename in filenames_noext_folder2: print(filename) if suffix_need_remove: if suffix_floder == 0: img1 = os.path.join(folder1, filenames_raw[index] + extensions_folder1[index]) img2 = os.path.join(folder2, filename + extensions_folder2[filenames_noext_folder2.index(filename)]) if suffix_floder == 1: img1 = os.path.join(folder1, filename + extensions_folder1[index]) img2 = os.path.join(folder2, filenames_raw[filenames_noext_folder2.index(filename)] + extensions_folder2[ filenames_noext_folder2.index(filename)]) else: img1 = os.path.join(folder1, filename + extensions_folder1[index]) img2 = os.path.join(folder2, filename + extensions_folder2[filenames_noext_folder2.index(filename)]) result = merge_2_images(img1, img2, mode, erosion_para, dilate) cv2.imwrite(os.path.join(outdir, filename + extensions_folder1[index]), result) def invert_image(image): # 将图片从BGR转为灰度图 gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # 对灰度图进行反转 inverted_image = cv2.bitwise_not(gray_image) # 将反转后的灰度图转换回BGR格式 inverted_image_bgr = cv2.cvtColor(inverted_image, cv2.COLOR_GRAY2BGR) return inverted_image_bgr def process_images(input_folder='./output/merged_imgs'): output_folder = os.path.join(os.path.dirname(input_folder), 'output_invert') os.makedirs(output_folder, exist_ok=True) # 获取输入文件夹中的所有图片文件 image_files = [f for f in os.listdir(input_folder) if f.lower().endswith(('.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff', '.webp', 'tif'))] for image_file in tqdm(image_files): image_path = os.path.join(input_folder, image_file) try: # 使用PIL库读取图像 with Image.open(image_path) as img: image = np.array(img.convert('RGB'))[:, :, ::-1].copy() if image is not None: # 翻转图片 inverted_image = invert_image(image) # 保存翻转后的图片到输出文件夹 output_path = os.path.join(output_folder, image_file) cv2.imwrite(output_path, inverted_image) else: raise ValueError(f"Failed to read image: {image_file}") except Exception as e: print(f"Error processing file {image_file}: {e}") def process_line(input_files): try: # 创建临时输出文件夹 output_folder = "temp_output" os.makedirs(output_folder, exist_ok=True) # 存储处理后的图片路径 processed_images = [] # 遍历所有输入文件 for img_path in input_files: img_path = img_path.name # 获取文件路径 # 处理图片的文件夹 depth_folder = os.path.join(output_folder, "depth_anything") teed_folder = os.path.join(output_folder, "teed_imgs") dp_teed_folder = os.path.join(output_folder, "dp_teed_imgs") merged_folder = os.path.join(output_folder, "merged_imgs") # 创建每个处理步骤的文件夹 os.makedirs(depth_folder, exist_ok=True) os.makedirs(teed_folder, exist_ok=True) os.makedirs(dp_teed_folder, exist_ok=True) os.makedirs(merged_folder, exist_ok=True) # 调用处理函数 depth_anything(img_path, depth_folder) teed_imgs(img_path, teed_folder, [1, 7, 2]) teed_imgs(depth_folder, dp_teed_folder, [0, 7, 2]) merge_images_in_2_folder(teed_folder, dp_teed_folder, merged_folder, '_depth', 1, 'multiply', [[2, 0], [2, 1]], [1, 0]) process_images(merged_folder) # 创建压缩包 zip_file_path = os.path.join(output_folder, "processed_images.zip") with zipfile.ZipFile(zip_file_path, 'w') as zipf: # 将每个步骤的文件夹添加到压缩包中 for folder in [depth_folder, teed_folder, dp_teed_folder, merged_folder]: for root, _, files in os.walk(folder): for file in files: zipf.write(os.path.join(root, file), os.path.relpath(os.path.join(root, file), output_folder)) return [zip_file_path], "" # 返回压缩包路径和空错误信息 except Exception as e: return [], f"发生错误: {str(e)}" # 返回空图片和错误信息 def launch_interface(): with gr.Blocks() as demo: # 允许用户选择多张图片 input_files = gr.File(label="选择输入图片", file_count="multiple", type="filepath") submit_button = gr.Button("开始处理") # 显示处理后的文件下载链接 output_file = gr.File(label="下载处理后的文件") # 显示错误信息 error_text = gr.Textbox(label="错误信息", interactive=False, visible=False) # 点击按钮时调用 process_line 函数 submit_button.click(process_line, inputs=[input_files], outputs=[output_file, error_text]) demo.launch(share=True) if __name__ == "__main__": launch_interface()