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from flask import Flask, request, render_template_string
from PIL import Image
import numpy as np
import io
import base64
import torch
import torchvision.transforms as transforms
import os
import sys
import time
app = Flask(__name__)
# Add paths for different methods
sys.path.append('.') # For ZeroIG (root folder)
sys.path.append('./CFWD') # For CFWD method
HTML_TEMPLATE = """
<!DOCTYPE html>
<html>
<head>
<title>Multi-Method Low-Light Enhancement</title>
<style>
body { font-family: Arial, sans-serif; max-width: 1200px; margin: 0 auto; padding: 20px; }
.container { text-align: center; }
.method-selection { margin: 20px 0; padding: 20px; background: #f8f9fa; border-radius: 10px; }
.method-button {
margin: 5px; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer;
font-size: 14px; transition: all 0.3s;
}
.method-button.active { background: #007bff; color: white; }
.method-button.inactive { background: #e9ecef; color: #6c757d; }
.upload-area { border: 2px dashed #ccc; padding: 40px; margin: 20px 0; border-radius: 10px; }
.result { margin-top: 20px; }
.comparison { display: flex; justify-content: space-around; flex-wrap: wrap; }
.image-container { margin: 10px; }
.image-container img { max-width: 350px; height: auto; border: 1px solid #ddd; border-radius: 5px; }
.status { color: green; font-weight: bold; margin: 10px 0; }
.error { color: red; }
.processing { color: orange; font-weight: bold; }
.method-info { background: #e3f2fd; padding: 15px; margin: 10px 0; border-radius: 5px; }
.folder-status { background: #fff3cd; padding: 10px; margin: 10px 0; border-radius: 5px; font-family: monospace; }
</style>
<script>
function selectMethod(method) {
document.getElementById('selected_method').value = method;
// Update button styles
const buttons = document.querySelectorAll('.method-button');
buttons.forEach(btn => {
if (btn.dataset.method === method) {
btn.classList.add('active');
btn.classList.remove('inactive');
} else {
btn.classList.add('inactive');
btn.classList.remove('active');
}
});
// Update method info
const infos = document.querySelectorAll('.method-info');
infos.forEach(info => {
info.style.display = info.dataset.method === method ? 'block' : 'none';
});
}
window.onload = function() {
selectMethod('zeroig');
}
</script>
</head>
<body>
<div class="container">
<h1>🌟 Multi-Method Low-Light Enhancement</h1>
<p>Professional low-light image enhancement with multiple state-of-the-art methods</p>
<div class="folder-status">
<strong>📁 Method Status:</strong><br>
{{ method_status }}
</div>
<div class="method-selection">
<h3>📚 Select Enhancement Method:</h3>
<button class="method-button" data-method="zeroig" onclick="selectMethod('zeroig')">
🎯 ZeroIG (CVPR 2024)
</button>
<button class="method-button" data-method="cfwd" onclick="selectMethod('cfwd')">
🌊 CFWD (CLIP-Fourier Wavelet)
</button>
<button class="method-button" data-method="both" onclick="selectMethod('both')">
🔄 Compare Both Methods
</button>
<div class="method-info" data-method="zeroig">
<strong>🎯 ZeroIG:</strong> Zero-shot illumination-guided joint denoising and adaptive enhancement.
Fast processing, no training data required, prevents over-enhancement artifacts.
</div>
<div class="method-info" data-method="cfwd">
<strong>🌊 CFWD:</strong> CLIP-Fourier Guided Wavelet Diffusion for low-light enhancement.
Uses diffusion models with CLIP guidance for state-of-the-art quality.
</div>
<div class="method-info" data-method="both">
<strong>🔄 Comparison Mode:</strong> Process with both methods to see the differences.
Takes longer but shows you the best of both approaches side-by-side.
</div>
</div>
<form method="post" enctype="multipart/form-data">
<input type="hidden" id="selected_method" name="method" value="zeroig">
<div class="upload-area">
<input type="file" name="image" accept="image/*" required>
<br><br>
<button type="submit" style="padding: 15px 30px; font-size: 16px; background: #28a745; color: white; border: none; border-radius: 5px;">
🚀 Enhance Image
</button>
</div>
</form>
{% if status %}
<div class="status">{{ status }}</div>
{% endif %}
{% if error %}
<div class="error">{{ error }}</div>
{% endif %}
{% if results %}
<div class="result">
<h3>🎯 Enhancement Results:</h3>
<div class="comparison">
<div class="image-container">
<h4>📷 Original (Low-light)</h4>
<img src="data:image/png;base64,{{ results.original }}" alt="Original">
</div>
{% if results.zeroig %}
<div class="image-container">
<h4>🎯 ZeroIG Enhanced</h4>
<img src="data:image/png;base64,{{ results.zeroig }}" alt="ZeroIG">
<br><br>
<a href="data:image/png;base64,{{ results.zeroig }}" download="zeroig_enhanced.png"
style="background: #007bff; color: white; padding: 8px 16px; text-decoration: none; border-radius: 3px;">
📥 Download ZeroIG
</a>
</div>
{% endif %}
{% if results.cfwd %}
<div class="image-container">
<h4>🌊 CFWD Enhanced</h4>
<img src="data:image/png;base64,{{ results.cfwd }}" alt="CFWD">
<br><br>
<a href="data:image/png;base64,{{ results.cfwd }}" download="cfwd_enhanced.png"
style="background: #17a2b8; color: white; padding: 8px 16px; text-decoration: none; border-radius: 3px;">
📥 Download CFWD
</a>
</div>
{% endif %}
</div>
{% if method_used %}
<div style="margin-top: 20px; padding: 15px; background: #d4edda; border-radius: 5px;">
<strong>Method Used:</strong> {{ method_used }}<br>
<strong>Processing Time:</strong> {{ processing_time }}s
</div>
{% endif %}
</div>
{% endif %}
<div style="margin-top: 40px; padding: 20px; background: #f8f9fa; border-radius: 10px;">
<h3>📖 About the Methods</h3>
<div style="margin: 15px 0;">
<strong>🎯 ZeroIG (CVPR 2024):</strong>
<p>Zero-shot illumination-guided joint denoising and adaptive enhancement for low-light images.</p>
<p>📄 <a href="https://openaccess.thecvf.com/content/CVPR2024/papers/Shi_ZERO-IG_Zero-Shot_Illumination-Guided_Joint_Denoising_and_Adaptive_Enhancement_for_Low-Light_CVPR_2024_paper.pdf">Paper</a> |
💻 <a href="https://github.com/Doyle59217/ZeroIG">Code</a></p>
</div>
<div style="margin: 15px 0;">
<strong>🌊 CFWD (2024):</strong>
<p>Low-light Image Enhancement via CLIP-Fourier Guided Wavelet Diffusion.</p>
<p>📄 <a href="https://arxiv.org/abs/2401.03788">Paper</a> |
💻 <a href="https://github.com/hejh8/CFWD">Code</a></p>
</div>
</div>
</div>
</body>
</html>
"""
def check_method_availability():
"""Check which methods are available"""
status = []
# Check ZeroIG (root folder)
zeroig_files = ['model.py', 'loss.py', 'utils.py']
zeroig_ok = all(os.path.exists(f) for f in zeroig_files)
if zeroig_ok:
# Check for weights
weights_found = False
for weight_path in ["./weights/LOL.pt", "./weights/model.pt"]:
if os.path.exists(weight_path):
weights_found = True
break
if weights_found:
status.append("✅ ZeroIG: Ready with trained weights")
else:
status.append("⚠️ ZeroIG: Available but no trained weights found")
else:
status.append("❌ ZeroIG: Missing required files")
# Check CFWD folder
cfwd_folder_exists = os.path.exists('./CFWD')
if cfwd_folder_exists:
cfwd_files = os.listdir('./CFWD')
status.append(f"📁 CFWD folder: Found with {len(cfwd_files)} files")
# Check for common CFWD files
expected_cfwd_files = ['test.py', 'model.py', 'datasets.py']
found_files = [f for f in expected_cfwd_files if f in cfwd_files]
if found_files:
status.append(f"✅ CFWD files found: {', '.join(found_files)}")
else:
status.append("⚠️ CFWD: Folder exists but expected files not found")
else:
status.append("❌ CFWD: Folder not found - please create ./CFWD/ and upload CFWD files")
return "<br>".join(status)
class MultiMethodProcessor:
def __init__(self):
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.zeroig_model = self.load_zeroig()
self.cfwd_model = self.load_cfwd()
print(f"Multi-method processor initialized on {self.device}")
def load_zeroig(self):
"""Load ZeroIG model from root folder"""
try:
from model import Finetunemodel, Network
# Try to load trained weights
possible_weights = [
"./weights/LOL.pt",
"./weights/zeroig.pt",
"./weights/model.pt"
]
for weight_path in possible_weights:
if os.path.exists(weight_path):
try:
model = Finetunemodel(weight_path)
model.to(self.device)
model.eval()
print(f"✅ ZeroIG loaded from {weight_path}")
return model
except Exception as e:
print(f"Failed to load ZeroIG from {weight_path}: {e}")
continue
# Fallback to Network
model = Network()
model.to(self.device)
model.eval()
print("⚠️ ZeroIG using random weights")
return model
except Exception as e:
print(f"❌ ZeroIG not available: {e}")
return None
def load_cfwd(self):
"""Load CFWD model from CFWD folder"""
try:
# Check if CFWD folder exists
if not os.path.exists('./CFWD'):
print("❌ CFWD folder not found")
return None
# Add CFWD folder to path and try importing
sys.path.insert(0, './CFWD')
# Try to import CFWD modules (will depend on actual CFWD structure)
# This is a placeholder - you'll need to adapt based on actual CFWD files
try:
# Example imports - replace with actual CFWD imports
# from model import CFWDModel # Replace with actual CFWD model
# model = CFWDModel()
# model.load_weights('./CFWD/weights/cfwd_weights.pt')
print("⚠️ CFWD: Import structure not yet implemented")
return None
except ImportError as e:
print(f"❌ CFWD import failed: {e}")
return None
except Exception as e:
print(f"❌ CFWD loading error: {e}")
return None
def enhance_with_zeroig(self, image):
"""Enhance image using ZeroIG"""
if self.zeroig_model is None:
return None, "ZeroIG model not available"
try:
# Resize if needed
original_size = image.size
max_size = 800
if max(image.size) > max_size:
ratio = max_size / max(image.size)
new_size = tuple(int(dim * ratio) for dim in image.size)
image = image.resize(new_size, Image.Resampling.LANCZOS)
# Convert to tensor
transform = transforms.ToTensor()
input_tensor = transform(image).unsqueeze(0).to(self.device)
# Run ZeroIG
with torch.no_grad():
if hasattr(self.zeroig_model, 'enhance') and hasattr(self.zeroig_model, 'denoise_1'):
enhanced, denoised = self.zeroig_model(input_tensor)
result_tensor = denoised
else:
outputs = self.zeroig_model(input_tensor)
result_tensor = outputs[13]
# Convert back to PIL
result_tensor = result_tensor.squeeze(0).cpu().clamp(0, 1)
enhanced_image = transforms.ToPILImage()(result_tensor)
# Resize back if needed
if enhanced_image.size != original_size:
enhanced_image = enhanced_image.resize(original_size, Image.Resampling.LANCZOS)
return enhanced_image, "ZeroIG enhancement successful"
except Exception as e:
return None, f"ZeroIG failed: {str(e)}"
def enhance_with_cfwd(self, image):
"""Enhance image using CFWD"""
if self.cfwd_model is None:
# Placeholder enhancement until CFWD is implemented
try:
# Simple placeholder - replace with actual CFWD processing
arr = np.array(image).astype(np.float32)
enhanced = np.clip(arr * 2.2, 0, 255).astype(np.uint8)
enhanced_image = Image.fromarray(enhanced)
return enhanced_image, "CFWD placeholder (upload CFWD files to ./CFWD/ folder for real processing)"
except Exception as e:
return None, f"CFWD placeholder failed: {str(e)}"
try:
# TODO: Implement actual CFWD processing when files are uploaded
# This will depend on the actual CFWD model structure
pass
except Exception as e:
return None, f"CFWD failed: {str(e)}"
# Initialize processor
print("🚀 Loading multi-method processor...")
processor = MultiMethodProcessor()
def image_to_base64(image):
"""Convert PIL image to base64"""
img_buffer = io.BytesIO()
image.save(img_buffer, format='PNG')
img_str = base64.b64encode(img_buffer.getvalue()).decode()
return img_str
@app.route('/', methods=['GET', 'POST'])
def index():
results = None
status = None
error = None
method_used = None
processing_time = None
# Check method availability
method_status = check_method_availability()
if request.method == 'POST':
try:
file = request.files['image']
method = request.form.get('method', 'zeroig')
if file:
print(f"Processing with method: {method}")
start_time = time.time()
# Load image
image = Image.open(file.stream).convert('RGB')
original_b64 = image_to_base64(image)
results = {'original': original_b64}
if method == 'zeroig':
enhanced, msg = processor.enhance_with_zeroig(image)
if enhanced:
results['zeroig'] = image_to_base64(enhanced)
status = f"✅ ZeroIG: {msg}"
else:
error = f"❌ ZeroIG failed: {msg}"
elif method == 'cfwd':
enhanced, msg = processor.enhance_with_cfwd(image)
if enhanced:
results['cfwd'] = image_to_base64(enhanced)
status = f"✅ CFWD: {msg}"
else:
error = f"❌ CFWD failed: {msg}"
elif method == 'both':
# Process with both methods
zeroig_enhanced, zeroig_msg = processor.enhance_with_zeroig(image)
cfwd_enhanced, cfwd_msg = processor.enhance_with_cfwd(image)
status_msgs = []
if zeroig_enhanced:
results['zeroig'] = image_to_base64(zeroig_enhanced)
status_msgs.append(f"ZeroIG: {zeroig_msg}")
if cfwd_enhanced:
results['cfwd'] = image_to_base64(cfwd_enhanced)
status_msgs.append(f"CFWD: {cfwd_msg}")
status = "✅ " + " | ".join(status_msgs)
end_time = time.time()
processing_time = round(end_time - start_time, 2)
method_used = method.upper()
except Exception as e:
error = f"Error processing image: {str(e)}"
print(f"Error: {e}")
return render_template_string(HTML_TEMPLATE,
method_status=method_status,
results=results,
status=status,
error=error,
method_used=method_used,
processing_time=processing_time)
if __name__ == '__main__':
print("🚀 Starting Organized Multi-Method Enhancement App...")
app.run(host='0.0.0.0', port=7860) |