Update app.py

app.py

@@ -1,20 +1,158 @@
+# 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
 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
-import shutil
-import os
-
+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
-from TEED.main import parse_args, main 
+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
 
-# 深度处理函数
-def depth_anything_image(image, encoder='vitl', pred_only=True, grayscale=True):
     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]},
@@ -22,77 +160,263 @@ def depth_anything_image(image, encoder='vitl', pred_only=True, grayscale=True):
     }
 
     depth_anything = DepthAnything(model_configs[encoder])
-    depth_anything.load_state_dict(torch.load(f'./checkpoints/depth_anything_{encoder}14.pth'))
+    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),
+        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(),
     ])
 
-    raw_image = np.array(image.convert('RGB'))[:, :, ::-1].copy()  # RGB to BGR
-    h, w = raw_image.shape[:2]
-    image_tensor = transform({'image': raw_image / 255.0})['image']
-    image_tensor = torch.from_numpy(image_tensor).unsqueeze(0).to(DEVICE)
+    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()
 
-    with torch.no_grad():
-        depth = depth_anything(image_tensor)
+    os.makedirs(outdir, exist_ok=True)
 
-    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)
+    for filename in tqdm(filenames):
+        raw_image = cv2.imread(filename)
+        image = cv2.cvtColor(raw_image, cv2.COLOR_BGR2RGB) / 255.0
 
-    return np.repeat(depth[..., np.newaxis], 3, axis=-1) if grayscale else cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO)
+        h, w = image.shape[:2]
 
-# TEED 图像处理函数
-def teed_process_image(image):
-    os.makedirs('./output/teed_imgs', exist_ok=True)
-    os.makedirs('./teed_tmp', exist_ok=True)
+        image = transform({'image': image})['image']
+        image = torch.from_numpy(image).unsqueeze(0).to(DEVICE)
 
-    temp_image_path = './teed_tmp/temp_image.png'
-    cv2.imwrite(temp_image_path, np.array(image))
+        with torch.no_grad():
+            depth = depth_anything(image)
 
-    args, train_info = parse_args(is_testing=True, pl_opt_dir='./output/teed_imgs')
-    args.input_val_dir = './teed_tmp'
-    args.output_dir = './output/teed_imgs'
+        depth = F.interpolate(depth[None], (h, w), mode='bilinear', align_corners=False)[0, 0]
+        depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
 
-    checkpoint_path =  './TEED/checkpoints/BIPED/5/5_model.pth'
-    if not os.path.exists(checkpoint_path):
-        raise FileNotFoundError(f"Checkpoint file not found: {checkpoint_path}")
-    
-    args.checkpoint_data = checkpoint_path  # 确保使用正确的路径
+        depth = depth.cpu().numpy().astype(np.uint8)
 
-    main(args, train_info)
+        if grayscale:
+            depth = np.repeat(depth[..., np.newaxis], 3, axis=-1)
+        else:
+            depth = cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO)
 
-    shutil.rmtree('./teed_tmp')
-    return cv2.imread(os.path.join('./output/teed_imgs', 'processed_image.png'))
+        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
 
-# 处理单个图像
-def process_single_image(image):
-    depth_result = depth_anything_image(image, 'vitl')
-    teed_result = teed_process_image(image)
-    merged_result = multiply_blend(depth_result, teed_result)
-    return merged_result
+            for i, caption in enumerate(captions):
+                # Calculate text size
+                text_size = cv2.getTextSize(caption, font, font_scale, font_thickness)[0]
 
-# Gradio 界面处理函数
-def gradio_process_line(img):
-    processed_image = process_single_image(img)
-    return Image.fromarray(processed_image)
+                # Calculate x-coordinate to center the text
+                text_x = int((segment_width * i) + (w - text_size[0]) / 2)
 
-# Gradio 界面
-import gradio as gr
+                # 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, output_folder):
+    try:
+        # 创建输出文件夹（如果不存在）
+        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
+
+            # 处理图片的函数调用
+            depth_anything(img_path, os.path.join(output_folder, "depth_anything"))
+            teed_imgs(img_path, os.path.join(output_folder, "teed_imgs"), [1, 7, 2])
+            teed_imgs(os.path.join(output_folder, "depth_anything"), os.path.join(output_folder, "dp_teed_imgs"), [0, 7, 2])
+            merge_images_in_2_folder(os.path.join(output_folder, "teed_imgs"),
+                                      os.path.join(output_folder, "dp_teed_imgs"),
+                                      os.path.join(output_folder, "merged_imgs"),
+                                      '_depth', 1, 'multiply', [[2, 0], [2, 1]], [1, 0])
+            process_images(os.path.join(output_folder, "merged_imgs"))
+
+        # 获取 merged_imgs 中的图片路径
+        processed_images = [os.path.join(output_folder, "merged_imgs", f)
+                            for f in os.listdir(os.path.join(output_folder, "merged_imgs"))
+                            if f.lower().endswith(('.jpg', '.jpeg', '.png'))]
+
+        return processed_images, ""  # 返回处理后的图片路径和空错误信息
+    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")
+
+        # 手动输入输出文件夹路径
+        output_folder = gr.Textbox(label="输出文件夹路径", placeholder="请输入输出文件夹路径", lines=1)
+
+        submit_button = gr.Button("开始处理")
+
+        # 显示处理后的图片
+        output_gallery = gr.Gallery(label="处理后的图片", show_label=True, height='auto')
+
+        # 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, output_folder], outputs=[output_gallery, error_text])
+
+    demo.launch(share=True)
 
-iface = gr.Interface(
-    fn=gradio_process_line,
-    inputs=gr.Image(type="pil"),
-    outputs=gr.Image(type="pil"),
-    title="Image Processing with Depth Anything and TEED",
-    description="Upload an image to process it with depth estimation and edge detection."
-)
 
-# 启动 Gradio 应用
-iface.launch()
+if __name__ == "__main__":
+    launch_interface()