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import gradio as gr
from ultralyticsplus import YOLO, render_result
import cv2
import time
import torch
# --------------------------
# System Checks & Optimization
# --------------------------
print("\n" + "="*40)
print("System Configuration Check:")
print(f"PyTorch Version: {torch.__version__}")
print(f"CUDA Available: {torch.cuda.is_available()}")
print(f"CUDA Device Count: {torch.cuda.device_count()}")
if torch.cuda.is_available():
print(f"Using GPU: {torch.cuda.get_device_name(0)}")
else:
print("Using CPU - For better performance, consider using a GPU environment")
print("="*40 + "\n")
# --------------------------
# Model Configuration
# --------------------------
# Load model with performance optimizations
model = YOLO('foduucom/plant-leaf-detection-and-classification')
# Configure model parameters
model_params = {
'conf': 0.25,
'iou': 0.45,
'imgsz': 640,
'device': 'cuda' if torch.cuda.is_available() else 'cpu',
'half': True if torch.cuda.is_available() else False # FP16 acceleration
}
model.overrides.update(model_params)
# Warmup model with dummy input
print("Performing model warmup...")
dummy_input = torch.randn(1, 3, 640, 640).to(model_params['device'])
if model_params['half']:
dummy_input = dummy_input.half()
model.predict(dummy_input, verbose=False)
print("Model warmup complete!\n")
# --------------------------
# Image Processing Pipeline
# --------------------------
def preprocess_image(image):
"""Optimized image preprocessing"""
# Convert RGB to BGR
img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
# Resize maintaining aspect ratio
max_size = 1280
h, w = img.shape[:2]
scale = min(max_size/h, max_size/w)
img = cv2.resize(img, (int(w*scale), int(h*scale)),
interpolation=cv2.INTER_LINEAR)
return img
# --------------------------
# Detection Function
# --------------------------
def detect_leaves(image):
try:
start_time = time.time()
# Step 1: Preprocessing
preprocess_start = time.time()
img = preprocess_image(image)
print(f"Preprocessing time: {time.time() - preprocess_start:.2f}s")
# Step 2: Prediction
predict_start = time.time()
results = model.predict(
source=img,
verbose=False,
stream=False, # Disable streaming mode
augment=False # Disable TTA for speed
)
print(f"Prediction time: {time.time() - predict_start:.2f}s")
# Step 3: Postprocessing
postprocess_start = time.time()
num_leaves = len(results[0].boxes)
rendered_img = render_result(model=model, image=img, result=results[0])
rendered_img = cv2.cvtColor(rendered_img, cv2.COLOR_BGR2RGB)
print(f"Postprocessing time: {time.time() - postprocess_start:.2f}s")
total_time = time.time() - start_time
print(f"\nTotal processing time: {total_time:.2f}s")
print(f"Detected leaves: {num_leaves}")
print("-"*50)
return rendered_img, num_leaves
except Exception as e:
print(f"Error processing image: {str(e)}")
return None, 0
# --------------------------
# Gradio Interface
# --------------------------
with gr.Blocks(title="Leaf Detection", theme=gr.themes.Soft()) as demo:
gr.Markdown("# 🍃 Real-Time Plant Leaf Detection")
gr.Markdown("Upload a plant image to analyze leaf count and health")
with gr.Row():
input_image = gr.Image(label="Input Image", type="numpy")
output_image = gr.Image(label="Detection Results", width=600)
with gr.Row():
leaf_count = gr.Number(label="Detected Leaves", precision=0)
process_btn = gr.Button("Analyze Image", variant="primary")
progress = gr.Textbox(label="Processing Status", visible=True)
process_btn.click(
fn=detect_leaves,
inputs=[input_image],
outputs=[output_image, leaf_count]
)
if __name__ == "__main__":
demo.launch(
server_port=7860,
show_error=True,
share=False
) |