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mohamed12ahmed commited on Sep 23, 2025

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Update app.py

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app.py +164 -69

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@@ -1,84 +1,179 @@
 import os
 import shutil
 import torch
 import cv2
-import gradio as gr
-from PIL import Image
-#os.chdir('Restormer')
-# Download sample images
-os.system("wget https://github.com/swz30/Restormer/releases/download/v1.0/sample_images.zip")
-shutil.unpack_archive('sample_images.zip')
-os.remove('sample_images.zip')
-examples = [['sample_images/Real_Denoising/degraded/117355.png', 'Denoising'],
-            ['sample_images/Single_Image_Defocus_Deblurring/degraded/engagement.jpg', 'Defocus Deblurring'],
-            ['sample_images/Motion_Deblurring/degraded/GoPro-GOPR0854_11_00-000090-input.jpg','Motion Deblurring'],
-            ['sample_images/Deraining/degraded/Rain100H-77-input.jpg','Deraining']]
-inference_on = ['Full Resolution Image', 'Downsampled Image']
-title = "Restormer"
-description = """
-Gradio demo for <b>Restormer: Efficient Transformer for High-Resolution Image Restoration</b>, CVPR 2022--ORAL. <a href='https://arxiv.org/abs/2111.09881'>[Paper]</a><a href='https://github.com/swz30/Restormer'>[Github Code]</a>\n
-<b> Note:</b> Since this demo uses CPU, by default it will run on the downsampled version of the input image (for speedup). But if you want to perform inference on the original input, then choose the option "Full Resolution Image" from the dropdown menu.
 """
-##With Restormer, you can perform: (1) Image Denoising, (2) Defocus Deblurring, (3)  Motion Deblurring, and (4) Image Deraining.
-##To use it, simply upload your own image, or click one of the examples provided below.
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2111.09881'>Restormer: Efficient Transformer for High-Resolution Image Restoration </a> | <a href='https://github.com/swz30/Restormer'>Github Repo</a></p>"
-def inference(img, task, run_on):
-    if not os.path.exists('temp'):
-      os.system('mkdir temp')
-    if run_on == 'Full Resolution Image':
-      img = img
-    else: # 'Downsampled Image'
-    ####  Resize the longer edge of the input image
-      max_res = 512
-      width, height = img.size
-      if max(width,height) > max_res:
-        scale = max_res /max(width,height)
-        width = int(scale*width)
-        height = int(scale*height)
-        img = img.resize((width,height), Image.ANTIALIAS)
-    img.save("temp/image.jpg", "JPEG")
-    if task == 'Motion Deblurring':
-      task = 'Motion_Deblurring'
-      os.system("python demo_gradio.py --task 'Motion_Deblurring' --input_path 'temp/image.jpg' --result_dir './temp/'")
-    if task == 'Defocus Deblurring':
-      task = 'Single_Image_Defocus_Deblurring'
-      os.system("python demo_gradio.py --task 'Single_Image_Defocus_Deblurring' --input_path 'temp/image.jpg' --result_dir './temp/'")
-    if task == 'Denoising':
-      task = 'Real_Denoising'
-      os.system("python demo_gradio.py --task 'Real_Denoising' --input_path 'temp/image.jpg' --result_dir './temp/'")
-    if task == 'Deraining':
-      os.system("python demo_gradio.py --task 'Deraining' --input_path 'temp/image.jpg' --result_dir './temp/'")
-    return f'temp/{task}/image.jpg'
-gr.Interface(
-    inference,
-    [
-        gr.inputs.Image(type="pil", label="Input"),
-        gr.inputs.Radio(["Denoising", "Defocus Deblurring", "Motion Deblurring", "Deraining"], default="Denoising", label='task'),
-        gr.inputs.Dropdown(choices=inference_on, type="value", default='Downsampled Image', label='Inference on')
-    ],
-    gr.outputs.Image(type="file", label="Output"),
-    title=title,
-    description=description,
-    article=article,
-    theme ="huggingface",
-    examples=examples,
-    allow_flagging=False,
-    ).launch(debug=False,enable_queue=True)

 import os
 import shutil
 import torch
+import torch.nn.functional as F
 import cv2
+from skimage import img_as_ubyte
+from flask import Flask, request, jsonify, send_file, render_template_string
+from werkzeug.utils import secure_filename
+import webbrowser
+import time
+# Flask App setup
+app = Flask(__name__)
+UPLOAD_FOLDER = 'uploads'
+RESULTS_FOLDER = 'results'
+app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
+app.config['RESULTS_FOLDER'] = RESULTS_FOLDER
+os.makedirs(UPLOAD_FOLDER, exist_ok=True)
+os.makedirs(RESULTS_FOLDER, exist_ok=True)
+# Model and Device setup
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = None
+def get_model():
+    global model
+    if model is None:
+        try:
+            # تم تعديل اسم النموذج هنا
+            model = torch.jit.load("motion_deblurring.pt", map_location=device)
+            model.to(device)
+            model.eval()
+            print("✅ Model loaded successfully")
+        except Exception as e:
+            print(f"❌ Error loading model: {e}")
+            model = None
+    return model
+# Image Processing function
+def process_image_with_model(input_path):
+    model = get_model()
+    if model is None:
+        raise RuntimeError("Model not loaded.")
+    # تم تعديل اسم المهمة هنا
+    task = "Motion_Deblurring"
+    out_dir = os.path.join(app.config["RESULTS_FOLDER"], task)
+    os.makedirs(out_dir, exist_ok=True)
+    img = cv2.cvtColor(cv2.imread(input_path), cv2.COLOR_BGR2RGB)
+    input_ = torch.from_numpy(img).float().div(255.).permute(2, 0, 1).unsqueeze(0).to(device)
+    h, w = input_.shape[2], input_.shape[3]
+    H = ((h + 8) // 8) * 8
+    W = ((w + 8) // 8) * 8
+    padh = H - h if h % 8 != 0 else 0
+    padw = W - w if w % 8 != 0 else 0
+    input_ = F.pad(input_, (0, padw, 0, padh), "reflect")
+    with torch.inference_mode():
+        restored = torch.clamp(model(input_), 0, 1)
+    restored = img_as_ubyte(
+        restored[:, :, :h, :w].permute(0, 2, 3, 1).cpu().numpy()[0]
+    )
+    out_path = os.path.join(out_dir, os.path.split(input_path)[-1])
+    cv2.imwrite(out_path, cv2.cvtColor(restored, cv2.COLOR_RGB2BGR))
+    return out_path
+# HTML Interface
+html_content = """
+<!DOCTYPE html>
+<html>
+<head>
+    <title>Restormer Motion Deblurring Demo</title>
+    <style>
+        body { text-align:center; font-family: sans-serif; }
+        .container { max-width: 600px; margin: auto; padding: 20px; border: 1px solid #ccc; border-radius: 8px; }
+        .image-display { display:flex; justify-content:center; gap:20px; margin-top:20px; }
+        img { max-width:300px; border:1px solid #ddd; }
+        h3 { margin-bottom: 10px; }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <h1>Restormer: Motion Deblurring Demo</h1>
+        <form id="uploadForm" enctype="multipart/form-data">
+            <input type="file" id="fileInput" name="file" accept="image/*" required><br><br>
+            <button type="submit">Process Image</button>
+        </form>
+        <p id="loading" style="display:none;">Processing... Please wait.</p>
+        <div class="image-display">
+            <div>
+                <h3>Original</h3>
+                <img id="original" style="display:none;">
+            </div>
+            <div>
+                <h3>Restored</h3>
+                <img id="restored" style="display:none;">
+            </div>
+        </div>
+    </div>
+    <script>
+        const form = document.getElementById("uploadForm");
+        const fileInput = document.getElementById("fileInput");
+        const loading = document.getElementById("loading");
+        const original = document.getElementById("original");
+        const restored = document.getElementById("restored");
+        fileInput.addEventListener("change", (e) => {
+            if (e.target.files.length > 0) {
+                original.src = URL.createObjectURL(e.target.files[0]);
+                original.style.display = "block";
+                restored.style.display = "none";
+            }
+        });
+        form.addEventListener("submit", async (e) => {
+            e.preventDefault();
+            if (fileInput.files.length === 0) return;
+            const formData = new FormData();
+            formData.append("file", fileInput.files[0]);
+            loading.style.display = "block";
+            try {
+                const response = await fetch("/process_image", {
+                    method: "POST",
+                    body: formData
+                });
+                if (response.ok) {
+                    const blob = await response.blob();
+                    const url = URL.createObjectURL(blob);
+                    restored.src = url;
+                    restored.style.display = "block";
+                } else {
+                    const error = await response.json();
+                    alert("Error: " + error.error);
+                }
+            } catch (err) {
+                alert("Request failed: " + err);
+            } finally {
+                loading.style.display = "none";
+            }
+        });
+    </script>
+</body>
+</html>
 """
+# Flask Routes
+@app.route("/")
+def index():
+    return render_template_string(html_content)
+@app.route("/process_image", methods=["POST"])
+def process_image():
+    if "file" not in request.files:
+        return jsonify({"error": "No file part"}), 400
+    file = request.files["file"]
+    if file.filename == "":
+        return jsonify({"error": "No filename"}), 400
+    filename = secure_filename(file.filename)
+    input_path = os.path.join(app.config["UPLOAD_FOLDER"], filename)
+    file.save(input_path)
+    try:
+        output_path = process_image_with_model(input_path)
+        return send_file(output_path, mimetype="image/jpeg")
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+# Main
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=7860, debug=True)