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| import gradio as gr | |
| from deepforest import main | |
| import cv2 | |
| def show_trees(img_path): | |
| try: | |
| model = main.deepforest() | |
| model.use_release() | |
| except Exception as model_error: | |
| raise gr.Error(f"Error initializing the deepforest model: {model_error}") | |
| img=cv2.imread(img_path) | |
| if img is None: | |
| raise gr.Error(f"Image path is not valid.") | |
| if img.shape[0]<1000 and img.shape[1]<1000: | |
| img = model.predict_image(path=img_path, return_plot=True) | |
| elif img.shape[0]>1000 or img.shape[1]>1000: | |
| img=model.predict_image(path=img_path,return_plot=True,thickness=2) | |
| else: | |
| img = model.predict_image(path=img_path, return_plot=True,thickness=3) | |
| if img is None: | |
| raise gr.Error("No predictions were made. Check your test image. Ensure it conists") | |
| img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) | |
| return img | |
| def show_birds(img_path): | |
| try: | |
| model = main.deepforest() | |
| model.use_bird_release() | |
| except Exception as model_error: | |
| raise gr.Error(f"Error initializing the deepforest model: {model_error}") | |
| img=cv2.imread(img_path) | |
| if img is None: | |
| raise gr.Error(f"Image path is not valid.") | |
| if img.shape[0]<1000 and img.shape[1]<1000: | |
| img = model.predict_image(path=img_path, return_plot=True) | |
| elif img.shape[0]>1000 or img.shape[1]>1000: | |
| img=model.predict_image(path=img_path,return_plot=True,thickness=2) | |
| else: | |
| img = model.predict_image(path=img_path, return_plot=True,thickness=3) | |
| if img is None: | |
| raise gr.Error("No predictions were made. Check your test image. Ensure it conists") | |
| img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) | |
| return img | |
| with gr.Blocks() as demo: | |
| gr.Markdown('# Deepforest') | |
| gr.Markdown('## Tree Detection Model') | |
| gr.Markdown('### Predict trees') | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_image = gr.Image(label="Input Image", type="filepath") | |
| with gr.Column(): | |
| output_image = gr.Image(label="Predicted image") | |
| submit_button_trees = gr.Button("Predict trees") | |
| submit_button_trees.click(show_trees, inputs=input_image, outputs=output_image) | |
| gr.Markdown('### Predict birds') | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_image=gr.Image(label="Input image",type="filepath") | |
| with gr.Column(): | |
| output_image=gr.Image(label="Predicted Image") | |
| submit_button_birds = gr.Button("Predict birds") | |
| submit_button_birds.click(show_birds,inputs=input_image,outputs=output_image) | |
| if __name__ == "__main__": | |
| demo.launch() |