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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() |