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import gradio as gr
import cv2
import requests
import os
from ultralytics import YOLO
model = YOLO("best_model.pt")
example_imgs = [
os.path.join("example", "img", example) for example in os.listdir("example/img")
]
example_vids = [
os.path.join("example", "vid", example) for example in os.listdir("example/vid")
]
def show_preds_image(image_path):
image = cv2.imread(image_path)
outputs = model.predict(source=image_path)
results = outputs[0].cpu().numpy()
for i, det in enumerate(results.boxes.xyxy):
cv2.rectangle(
image,
(int(det[0]), int(det[1])),
(int(det[2]), int(det[3])),
color=(0, 0, 255),
thickness=2,
lineType=cv2.LINE_AA,
)
return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
def show_preds_image(image_path):
image = cv2.imread(image_path)
outputs = model.predict(source=image_path)
results = outputs[0].cpu().numpy()
for det in results.boxes.xyxy:
cv2.rectangle(
image,
(int(det[0]), int(det[1])),
(int(det[2]), int(det[3])),
color=(0, 0, 255),
thickness=2,
lineType=cv2.LINE_AA,
)
return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Define the Gradio interface for image input
interface_image = gr.Interface(
fn=show_preds_image,
inputs=gr.components.Image(type="filepath", label="Input Image"),
outputs=gr.components.Image(type="numpy", label="Output Image"),
title="Pothole Detector - Image",
examples=example_imgs,
cache_examples=False,
)
# For video processing, it's best to process and then show the output video.
# This is a simplified placeholder for video processing, indicating where to include the video processing logic.
def show_preds_video(video_path):
# Placeholder for video processing function
# Process the video here and save the output, then return the path to the processed video
processed_video_path = "processed_video.mp4" # Example output path
return processed_video_path
# Define the Gradio interface for video input
interface_video = gr.Interface(
fn=show_preds_video,
inputs=gr.components.Video(label="Input Video"),
outputs=gr.components.Video(type="numpy", label="Processed Video"),
title="Pothole Detector - Video",
examples=example_vids,
cache_examples=False,
)
# Combine the interfaces into a tabbed interface
gr.TabbedInterface(
[interface_image, interface_video], tab_names=["Image Inference", "Video Inference"]
).launch()
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