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"""
Utility functions for the Plant Disease Detection UI
"""
import torch
import numpy as np
from PIL import Image
import torchvision.transforms as transforms
import config
def preprocess_image(image, image_size=config.IMAGE_SIZE):
"""
Preprocess image for model input
Args:
image: PIL Image or numpy array
image_size: Target size (height, width)
Returns:
Preprocessed tensor ready for model
"""
# Convert to PIL Image if numpy array
if isinstance(image, np.ndarray):
image = Image.fromarray(image.astype('uint8'))
# Convert RGBA to RGB if necessary
if image.mode == 'RGBA':
image = image.convert('RGB')
# Define preprocessing transforms
transform = transforms.Compose([
transforms.Resize(image_size),
transforms.ToTensor(),
transforms.Normalize(mean=config.NORMALIZE_MEAN, std=config.NORMALIZE_STD)
])
# Apply transforms
tensor = transform(image)
# Add batch dimension
tensor = tensor.unsqueeze(0)
return tensor
def postprocess_predictions(logits, class_names=config.CLASS_NAMES, top_k=config.TOP_K_PREDICTIONS):
"""
Convert model logits to human-readable predictions
Args:
logits: Raw model output
class_names: List of class names
top_k: Number of top predictions to return
Returns:
Dictionary of predictions with confidences
"""
# Convert logits to probabilities using softmax
probs = torch.nn.functional.softmax(logits, dim=1)
# Convert to numpy
probs = probs.cpu().detach().numpy()[0]
# Create predictions dictionary
predictions = {name: float(prob) for name, prob in zip(class_names, probs)}
# Get top-k predictions
top_predictions = sorted(predictions.items(), key=lambda x: x[1], reverse=True)[:top_k]
return dict(top_predictions), predictions
def format_prediction_for_display(predictions):
"""
Format predictions for Gradio display
Args:
predictions: Dictionary of class names and probabilities
Returns:
Dictionary formatted for Gradio Label component
"""
# Filter out very low confidence predictions
filtered = {k: v for k, v in predictions.items() if v >= config.CONFIDENCE_THRESHOLD}
return filtered
def format_class_name(class_name):
"""
Format class name for better readability
Args:
class_name: Original class name (e.g., "Tomato___Late_blight")
Returns:
Formatted class name (e.g., "Tomato - Late blight")
"""
# Replace underscores with spaces and split on ___
parts = class_name.split("___")
if len(parts) == 2:
plant, disease = parts
plant = plant.replace("_", " ")
disease = disease.replace("_", " ")
return f"{plant} - {disease}"
else:
return class_name.replace("_", " ")
def get_disease_info(class_name):
"""
Get information about a disease (for future enhancement)
Args:
class_name: Disease class name
Returns:
Dictionary with disease information
"""
# This is a placeholder - you could expand this with actual disease information
parts = class_name.split("___")
info = {
"plant": parts[0].replace("_", " ") if len(parts) > 0 else "Unknown",
"disease": parts[1].replace("_", " ") if len(parts) > 1 else "Unknown",
"is_healthy": "healthy" in class_name.lower(),
"formatted_name": format_class_name(class_name)
}
return info
def batch_preprocess_images(images):
"""
Preprocess multiple images for batch prediction
Args:
images: List of PIL Images or numpy arrays
Returns:
Batched tensor ready for model
"""
tensors = [preprocess_image(img) for img in images]
batch = torch.cat(tensors, dim=0)
return batch
def create_confidence_label(predictions, top_k=5):
"""
Create a formatted string showing top predictions
Args:
predictions: Dictionary of predictions
top_k: Number of top predictions to show
Returns:
Formatted string
"""
top_preds = sorted(predictions.items(), key=lambda x: x[1], reverse=True)[:top_k]
lines = []
for i, (class_name, prob) in enumerate(top_preds, 1):
formatted_name = format_class_name(class_name)
lines.append(f"{i}. {formatted_name}: {prob*100:.2f}%")
return "\n".join(lines)
if __name__ == "__main__":
# Test utilities
print("Testing utility functions...")
# Test class name formatting
test_names = [
"Tomato___Late_blight",
"Apple___healthy",
"Corn_(maize)___Common_rust_"
]
print("\nClass name formatting:")
for name in test_names:
print(f" {name} -> {format_class_name(name)}")
# Test disease info
print("\nDisease info:")
for name in test_names:
info = get_disease_info(name)
print(f" {name}:")
print(f" Plant: {info['plant']}")
print(f" Disease: {info['disease']}")
print(f" Healthy: {info['is_healthy']}")
# Test image preprocessing
print("\nImage preprocessing:")
dummy_image = Image.new('RGB', (512, 512), color='red')
tensor = preprocess_image(dummy_image)
print(f" Input size: {dummy_image.size}")
print(f" Output tensor shape: {tensor.shape}")
# Test mock predictions
print("\nMock predictions:")
from models.mock_model import create_mock_predictions
preds = create_mock_predictions(config.CLASS_NAMES)
top_preds, all_preds = postprocess_predictions(
torch.tensor([list(preds.values())]),
config.CLASS_NAMES
)
print(create_confidence_label(top_preds))
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