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import gradio as gr
import joblib
import cv2
import numpy as np
from PIL import Image
from tensorflow.keras.models import load_model
# Define paths to models and load the scaler
model_paths = {
"Regressor_decision_tree": "multioutput_regressor_decision_tree.joblib",
"Regressor_ridge": "regressor_ridge.joblib",
"Regressor_elastic_net": "elastic_net_model.joblib",
"NN_6_Layers": "NN_Layers_6.keras",
"CNN": "cnn_model_bigger.keras",
"CNN_with_reductions": "cnn_model_bigger_with_reductions.keras"
}
scaler = joblib.load("scaler.joblib")
# Function to load models based on file extension
def load_model_by_type(path):
if path.endswith('.joblib'):
return joblib.load(path)
elif path.endswith('.keras'):
return load_model(path)
else:
raise ValueError(f"Unsupported file extension for file {path}")
# Load models with appropriate method
models = {name: load_model_by_type(path) for name, path in model_paths.items()}
def detect_objects(image, model_name):
model = models[model_name]
# Assuming Gradio passes image as a numpy array and checking if conversion is needed
if image.ndim == 3 and image.shape[2] == 3: # If the image is RGB
image_gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) # Convert to grayscale using OpenCV
else:
image_gray = image # Use the image as is if already grayscale
# Check if the model requires CNN specific preprocessing
if model_name in ["CNN", "CNN_with_reductions"]:
image_processed = np.array(image_gray)
image_processed = image_processed.reshape(1, image_gray.shape[0], image_gray.shape[1], 1)
image_processed = image_processed.astype('float32')
image_processed /= 255 # Normalize pixel values
else:
# Assuming other models might expect flattened, scaled input
image_processed = image_gray.flatten().reshape(1, -1)
image_processed = scaler.transform(image_processed)
# Make prediction
predictions = model.predict(image_processed)
x, y, width, height = predictions[0]
# Draw bounding box on a copy of the original image (converted back to RGB for color drawing)
original_image_rgb = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) # Ensure image is in RGB
cv2.rectangle(original_image_rgb, (int(x), int(y)), (int(x + width), int(y + height)), (0, 255, 0), 2)
return Image.fromarray(original_image_rgb)
# Gradio interface setup
iface = gr.Interface(
fn=detect_objects,
inputs=[gr.components.Image(), gr.components.Dropdown(list(model_paths.keys()))],
outputs=gr.components.Image(),
title="Object Detection",
description="Select a model and upload an image to detect objects."
)
iface.launch(show_error=True, share=True, debug=True)
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