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app.py

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+# 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()

requirements.txt

@@ -0,0 +1,138 @@
+absl-py==2.1.0
+aiofiles==23.2.1
+aiohttp==3.9.5
+aiosignal==1.3.1
+altair==5.3.0
+annotated-types==0.7.0
+anyio==4.4.0
+astunparse==1.6.3
+async-timeout==4.0.3
+attrs==23.2.0
+certifi==2024.7.4
+cffi==1.16.0
+charset-normalizer==3.3.2
+click==8.1.7
+colorama==0.4.6
+contourpy==1.2.1
+cv2ext==0.0.13
+cycler==0.12.1
+dnspython==2.6.1
+email_validator==2.2.0
+exceptiongroup==1.2.2
+fastapi==0.111.1
+fastapi-cli==0.0.4
+ffmpy==0.3.2
+filelock==3.15.4
+flatbuffers==24.3.25
+fonttools==4.53.1
+frozenlist==1.4.1
+fsspec==2024.6.1
+gast==0.6.0
+google-pasta==0.2.0
+gradio==3.23.0
+gradio_client==1.1.0
+grpcio==1.65.4
+h11==0.14.0
+h5py==3.11.0
+httpcore==1.0.5
+httptools==0.6.1
+httpx==0.27.0
+huggingface-hub==0.24.0
+idna==3.7
+imageio==2.34.2
+importlib_resources==6.4.0
+intel-openmp==2021.4.0
+Jinja2==3.1.4
+joblib==1.4.2
+jsonschema==4.23.0
+jsonschema-specifications==2023.12.1
+keras==3.4.1
+kiwisolver==1.4.5
+kornia==0.7.3
+kornia_rs==0.1.5
+lazy_loader==0.4
+libclang==18.1.1
+linkify-it-py==2.0.3
+Markdown==3.6
+markdown-it-py==2.2.0
+MarkupSafe==2.1.5
+matplotlib==3.9.1
+mdit-py-plugins==0.3.3
+mdurl==0.1.2
+mkl==2021.4.0
+ml-dtypes==0.4.0
+mpmath==1.3.0
+multidict==6.0.5
+namex==0.0.8
+networkx==3.3
+numpy==1.26.4
+opencv-contrib-python==4.10.0.84
+opt-einsum==3.3.0
+optree==0.12.1
+orjson==3.10.6
+outcome==1.3.0.post0
+packaging==24.1
+pandas==2.2.2
+pillow==10.4.0
+protobuf==4.25.4
+pycparser==2.22
+pydantic==2.8.2
+pydantic_core==2.20.1
+pydub==0.25.1
+Pygments==2.18.0
+pyparsing==3.1.2
+PySocks==1.7.1
+python-dateutil==2.9.0.post0
+python-dotenv==1.0.1
+python-multipart==0.0.9
+pytz==2024.1
+PyYAML==6.0.1
+referencing==0.35.1
+requests==2.32.3
+rich==13.7.1
+rpds-py==0.19.0
+ruff==0.5.4
+scikit-image==0.24.0
+scikit-learn==1.5.1
+scipy==1.14.0
+selenium==4.23.1
+semantic-version==2.10.0
+shellingham==1.5.4
+six==1.16.0
+sniffio==1.3.1
+sortedcontainers==2.4.0
+starlette==0.37.2
+sympy==1.13.1
+tbb==2021.13.0
+tensorboard==2.17.0
+tensorboard-data-server==0.7.2
+tensorflow==2.17.0
+tensorflow-intel==2.17.0
+tensorflow-io-gcs-filesystem==0.31.0
+termcolor==2.4.0
+thop==0.1.1.post2209072238
+threadpoolctl==3.5.0
+tifffile==2024.7.21
+tomlkit==0.12.0
+toolz==0.12.1
+torch==2.3.1
+torchaudio==2.3.1
+torchvision==0.18.1
+tqdm==4.66.4
+trio==0.26.0
+trio-websocket==0.11.1
+typer==0.12.3
+typing_extensions==4.12.2
+tzdata==2024.1
+uc-micro-py==1.0.3
+urllib3==2.2.2
+uvicorn==0.30.3
+watchfiles==0.22.0
+webdriver-manager==4.0.2
+websocket-client==1.8.0
+websockets==11.0.3
+Werkzeug==3.0.3
+wordcloud==1.9.3
+wrapt==1.16.0
+wsproto==1.2.0
+yarl==1.9.4