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eea83e8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 | import os
import time
from pathlib import Path
from PIL import Image
from flask import Flask, request, jsonify
# Import model loaders and predictors
from .RIDCP.inference import load_ridcp_model, ridcp_predict
from .SCUNet.inference import load_scu_model, scu_predict
from .Retinexformer.inference import load_retinexformer_model, retinexformer_predict
from .img2img_turbo.inference import load_turbo_model, turbo_predict
from .ESRGAN.inference import load_esrgan_model, esrgan_predict
from .IDT.inference import load_idt_model, idt_predict
from .iqa_reward import IQAReward
# Configure environment variables
os.environ["BASICSR_JIT"] = "True"
os.environ["CUDA_VISIBLE_DEVICES"] = "7"
# Initialize Flask application
app = Flask(__name__)
# Global variables
models = {}
iqa = IQAReward()
class ModelTester:
"""
Model testing service for image restoration models.
This class manages model loading, image processing, and quality assessment.
"""
def __init__(self, output_base_dir="datasets/tmp_result"):
"""
Initialize the model tester.
Args:
output_base_dir (str): Base directory for storing results.
"""
self.output_base_dir = output_base_dir
self.models = {}
self.iqa = IQAReward()
self.model_loaders = {
'scunet': (load_scu_model, scu_predict),
'retinexformer_lolv2': (lambda: load_retinexformer_model('LOLV2'), retinexformer_predict),
'retinexformer_fivek': (lambda: load_retinexformer_model('FiveK'), retinexformer_predict),
'turbo_night': (lambda: load_turbo_model('night'), turbo_predict),
'turbo_rain': (lambda: load_turbo_model('rain'), turbo_predict),
'turbo_snow': (lambda: load_turbo_model('snow'), turbo_predict),
'real_esrgan': (load_esrgan_model, esrgan_predict),
'ridcp': (load_ridcp_model, ridcp_predict),
'idt': (load_idt_model, idt_predict)
}
def load_models(self, model_names):
"""
Load specified models into memory.
Args:
model_names (list): List of model names to load.
"""
print(f"Loading models: {', '.join(model_names)}")
self.models = {}
for model_name in model_names:
if model_name in self.model_loaders:
loader_fn = self.model_loaders[model_name][0]
self.models[model_name] = loader_fn()
print(f"Loaded {model_name}")
else:
print(f"Unknown model: {model_name}")
print(f"Finished loading {len(self.models)} models")
def resize_image(self, img_path, output_dir, target_size=(256, 256)):
"""
Resize input image to a standard size.
Args:
img_path (str): Path to the input image.
output_dir (str): Directory to save the resized image.
target_size (tuple): Target resolution (width, height).
Returns:
str: Path to the resized image.
"""
# Create output directory if it doesn't exist
os.makedirs(output_dir, exist_ok=True)
with Image.open(img_path) as img:
# Ensure consistent color mode
img = img.convert('RGB')
# Use high-quality resampling
img = img.resize(target_size, Image.LANCZOS)
# Generate output filename
img_name = os.path.splitext(os.path.basename(img_path))[0]
save_path = os.path.join(output_dir, f"{img_name}.png")
# Save the resized image
img.save(save_path, format='PNG')
return save_path
def process_image_with_models(self, model_list, img_path, output_dir):
"""
Process an image with a sequence of models.
Args:
model_list (list): List of model names to apply in sequence.
img_path (str): Path to the input image.
output_dir (str): Directory to save the processed images.
Returns:
str: Path to the final processed image.
"""
# Resize input image
img_path = self.resize_image(img_path, output_dir)
# Apply each model in sequence
for model_name in model_list:
if model_name not in self.models:
print(f"Model {model_name} not loaded, skipping")
continue
# Get the predict function for this model
_, predict_fn = self.model_loaders[model_name]
# Process the image with the current model
img_path = predict_fn(self.models[model_name], img_path, output_dir)
print(f"Applied {model_name}, saved result to {img_path}")
return img_path
def create_output_dir(self):
"""
Create a unique output directory based on current timestamp.
Returns:
str: Path to the created output directory.
"""
timestamp = int(time.time())
output_dir = os.path.join(self.output_base_dir, f"{timestamp}")
os.makedirs(output_dir, exist_ok=True)
return output_dir
def process_request(self, img_path, model_list):
"""
Process an image with the specified models and evaluate the result.
Args:
img_path (str): Path to the input image.
model_list (list): List of model names to apply.
Returns:
dict: Dictionary with output path and quality score.
Raises:
FileNotFoundError: If the input image doesn't exist.
"""
# Verify the image path
if not os.path.exists(img_path):
raise FileNotFoundError(f"Image file not found: {img_path}")
# Create a unique output directory
output_dir = self.create_output_dir()
# Process the image
final_output = self.process_image_with_models(model_list, img_path, output_dir)
# Evaluate the result
score = self.iqa.get_iqa_score(final_output)
return {
"output_path": final_output,
"score": score
}
# Initialize the model tester
model_tester = None
@app.route('/process_image', methods=['POST'])
def process_image():
"""
API endpoint for processing an image with specified models.
Expects a JSON payload with:
- img_path: Path to the input image
- models: List of model names to apply
Returns:
- JSON with output_path and score
"""
global model_tester
# Parse request data
data = request.get_json()
img_path = data.get('img_path')
models_to_use = data.get('models', [])
# Validate input
if not img_path:
return jsonify({"error": "Missing image path"}), 400
if not models_to_use:
return jsonify({"error": "No models specified"}), 400
try:
# Process the image
result = model_tester.process_request(img_path, models_to_use)
return jsonify(result)
except FileNotFoundError as e:
return jsonify({"error": str(e)}), 404
except Exception as e:
return jsonify({"error": f"Processing failed: {str(e)}"}), 500
def start_server(host='0.0.0.0', port=5010, model_names=None):
"""
Start the API server with specified models.
Args:
host (str): Host address to bind the server.
port (int): Port to listen on.
model_names (list): List of model names to load. If None, loads a default set.
"""
global model_tester
# Initialize the model tester
model_tester = ModelTester()
# Define default models if none specified
if model_names is None:
model_names = [
'scunet', 'real_esrgan', 'ridcp', 'idt',
'turbo_rain', 'turbo_night',
'retinexformer_lolv2', 'retinexformer_fivek'
]
# Load the models
model_tester.load_models(model_names)
# Start the Flask application
print(f"Starting API server at http://{host}:{port}")
app.run(host=host, port=port)
if __name__ == '__main__':
# Start the server with default settings
start_server() |