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dieasedetection.py
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dieasedetection.py
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import cv2
import numpy as np
import time
# Load the pre-trained model
from keras.models import load_model
model = load_model('Apple.h5')
# Define the labels for the classes
labels = ['Apple Scab', 'Apple Black Rot', 'Apple Cedar Dust', 'Healthy']
# Initialize disease counts
disease_counts = {label: 0 for label in labels}
def display_counts():
# Print the disease counts
print("Disease Counts:")
for label, count in disease_counts.items():
print(f"{label}: {count}")
def main():
# Create a VideoCapture object
cap = cv2.VideoCapture(0)
# Initialize timer
start_time = time.time()
display_interval = 1.0 # Display counts every 1 second
while True:
# Capture frame-by-frame
ret, frame = cap.read()
# Convert the frame to the format expected by the model
resized_frame = cv2.resize(frame, (224, 224)) # Resize to match the input size of the model
resized_frame = np.expand_dims(resized_frame, axis=0) # Add batch dimension
normalized_frame = resized_frame / 255.0 # Normalize pixel values to [0, 1]
# Use the model to make predictions
predictions = model.predict(normalized_frame)
# Get the predicted class label
predicted_label_index = np.argmax(predictions)
predicted_label = labels[predicted_label_index]
# Display the predicted label on the frame
cv2.putText(frame, predicted_label, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
# Update disease counts
if predicted_label != 'Healthy':
disease_counts[predicted_label] += 1
# Display the resulting frame
cv2.imshow('Leaf Disease Detection', frame)
# Check if it's time to display counts
if time.time() - start_time >= display_interval:
display_counts()
start_time = time.time()
# Check for exit key
if cv2.waitKey(1) & 0xFF == ord('q'):
print("System Stopped")
break
# Release the capture
cap.release()
cv2.destroyAllWindows()
if __name__ == "__main__":
main()