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#!/usr/bin/env python3
"""
Model Validation Script for Strawberry Ripeness Classification
Tests the trained model on sample images to verify functionality
"""
import os
import sys
import torch
import numpy as np
import cv2
from pathlib import Path
import json
from datetime import datetime
# Add current directory to path for imports
sys.path.append('.')
from train_ripeness_classifier import create_model, get_transforms
def load_model(model_path):
"""Load the trained classification model"""
print(f"Loading model from: {model_path}")
if not os.path.exists(model_path):
raise FileNotFoundError(f"Model file not found: {model_path}")
# Create model architecture
model = create_model(num_classes=3, backbone='resnet18', pretrained=False)
# Load trained weights
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.load_state_dict(torch.load(model_path, map_location=device))
model = model.to(device)
model.eval()
print(f"Model loaded successfully on {device}")
return model, device
def get_test_images():
"""Get sample test images from the dataset"""
test_dirs = [
'model/ripeness_manual_dataset/unripe',
'model/ripeness_manual_dataset/ripe',
'model/ripeness_manual_dataset/overripe'
]
test_images = []
for test_dir in test_dirs:
if os.path.exists(test_dir):
images = list(Path(test_dir).glob('*.jpg'))[:3] # Get first 3 images from each class
for img_path in images:
test_images.append({
'path': str(img_path),
'true_label': os.path.basename(test_dir),
'class_name': os.path.basename(test_dir)
})
return test_images
def predict_image(model, device, image_path, transform):
"""Predict ripeness for a single image"""
try:
# Load and preprocess image
image = cv2.imread(image_path)
if image is None:
return None, "Failed to load image"
# Convert BGR to RGB
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
from PIL import Image
image_pil = Image.fromarray(image)
# Apply transforms
input_tensor = transform(image_pil).unsqueeze(0).to(device)
# Get prediction
with torch.no_grad():
outputs = model(input_tensor)
probabilities = torch.softmax(outputs, dim=1)
predicted_class_idx = torch.argmax(probabilities, dim=1).item()
confidence = probabilities[0][predicted_class_idx].item()
# Get class names
class_names = ['overripe', 'ripe', 'unripe']
predicted_class = class_names[predicted_class_idx]
# Get all probabilities
probs_dict = {
class_names[i]: float(probabilities[0][i].item())
for i in range(len(class_names))
}
return {
'predicted_class': predicted_class,
'confidence': confidence,
'probabilities': probs_dict
}, None
except Exception as e:
return None, str(e)
def validate_model():
"""Main validation function"""
print("=== Strawberry Ripeness Classification Model Validation ===")
print(f"Validation time: {datetime.now().isoformat()}")
print()
# Load model
model_path = 'model/ripeness_classifier_best.pth'
try:
model, device = load_model(model_path)
except Exception as e:
print(f"❌ Failed to load model: {e}")
return False
# Get transforms
_, transform = get_transforms(img_size=224)
# Get test images
test_images = get_test_images()
if not test_images:
print("❌ No test images found")
return False
print(f"Found {len(test_images)} test images")
print()
# Test predictions
results = []
correct_predictions = 0
total_predictions = 0
print("Testing predictions...")
print("-" * 80)
for i, test_img in enumerate(test_images):
image_path = test_img['path']
true_label = test_img['true_label']
# Make prediction
prediction, error = predict_image(model, device, image_path, transform)
if error:
print(f"❌ Image {i+1}: Error - {error}")
continue
predicted_class = prediction['predicted_class']
confidence = prediction['confidence']
# Check if prediction is correct
is_correct = predicted_class == true_label
if is_correct:
correct_predictions += 1
total_predictions += 1
# Print result
status = "✅" if is_correct else "❌"
print(f"{status} Image {i+1}: {os.path.basename(image_path)}")
print(f" True: {true_label} | Predicted: {predicted_class} ({confidence:.3f})")
print(f" Probabilities: overripe={prediction['probabilities']['overripe']:.3f}, "
f"ripe={prediction['probabilities']['ripe']:.3f}, "
f"unripe={prediction['probabilities']['unripe']:.3f}")
print()
# Store result
results.append({
'image_path': image_path,
'true_label': true_label,
'predicted_class': predicted_class,
'confidence': confidence,
'probabilities': prediction['probabilities'],
'correct': is_correct
})
# Calculate accuracy
accuracy = (correct_predictions / total_predictions * 100) if total_predictions > 0 else 0
print("=" * 80)
print("VALIDATION RESULTS")
print("=" * 80)
print(f"Total images tested: {total_predictions}")
print(f"Correct predictions: {correct_predictions}")
print(f"Accuracy: {accuracy:.1f}%")
print()
# Class-wise analysis
class_stats = {}
for result in results:
true_class = result['true_label']
if true_class not in class_stats:
class_stats[true_class] = {'correct': 0, 'total': 0}
class_stats[true_class]['total'] += 1
if result['correct']:
class_stats[true_class]['correct'] += 1
print("Class-wise Performance:")
for class_name, stats in class_stats.items():
class_accuracy = (stats['correct'] / stats['total'] * 100) if stats['total'] > 0 else 0
print(f" {class_name}: {stats['correct']}/{stats['total']} ({class_accuracy:.1f}%)")
print()
# Save detailed results
validation_results = {
'validation_time': datetime.now().isoformat(),
'model_path': model_path,
'device': str(device),
'total_images': total_predictions,
'correct_predictions': correct_predictions,
'accuracy_percent': accuracy,
'class_stats': class_stats,
'detailed_results': results
}
results_path = 'model_validation_results.json'
with open(results_path, 'w') as f:
json.dump(validation_results, f, indent=2)
print(f"Detailed results saved to: {results_path}")
# Validation verdict
if accuracy >= 90:
print("🎉 VALIDATION PASSED: Model performs excellently!")
return True
elif accuracy >= 80:
print("⚠️ VALIDATION WARNING: Model performs moderately well")
return True
else:
print("❌ VALIDATION FAILED: Model performance is poor")
return False
if __name__ == '__main__':
success = validate_model()
sys.exit(0 if success else 1)
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