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import gradio as gr
import cv2
import requests
import os
from ultralytics import YOLO
file_urls = [
'https://www.dropbox.com/s/b5g97xo901zb3ds/pothole_example.jpg?dl=1',
'https://www.dropbox.com/s/86uxlxxlm1iaexa/pothole_screenshot.png?dl=1',
'https://www.dropbox.com/s/7sjfwncffg8xej2/video_7.mp4?dl=1'
]
def download_file(url, save_name):
if not os.path.exists(save_name):
file = requests.get(url)
with open(save_name, 'wb') as f:
f.write(file.content)
for i, url in enumerate(file_urls):
if url.endswith('.mp4') or '.mp4?' in url:
download_file(url, 'video.mp4')
else:
download_file(url, f'image_{i}.jpg')
model = YOLO('best.pt')
path = [['image_0.jpg'], ['image_1.jpg']]
video_path = [['video.mp4']]
def show_preds_image(image_path):
image = cv2.imread(image_path)
results = model(image_path)[0]
for box in results.boxes.xyxy:
cv2.rectangle(
image,
(int(box[0]), int(box[1])),
(int(box[2]), int(box[3])),
color=(0, 0, 255),
thickness=2,
lineType=cv2.LINE_AA
)
return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
inputs_image = [
gr.Image(type="filepath", label="Input Image"),
]
outputs_image = [
gr.Image(type="numpy", label="Output Image"),
]
interface_image = gr.Interface(
fn=show_preds_image,
inputs=inputs_image,
outputs=outputs_image,
title="Pothole detector",
examples=path,
cache_examples=False,
)
def show_preds_video(video_path):
cap = cv2.VideoCapture(video_path)
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
frame_copy = frame.copy()
results = model(frame_copy)[0]
for box in results.boxes.xyxy:
cv2.rectangle(
frame_copy,
(int(box[0]), int(box[1])),
(int(box[2]), int(box[3])),
color=(0, 0, 255),
thickness=2,
lineType=cv2.LINE_AA
)
yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
cap.release()
inputs_video = [
gr.Video(label="Input Video"),
]
outputs_video = [
gr.Image(type="numpy", label="Output Frame"),
]
interface_video = gr.Interface(
fn=show_preds_video,
inputs=inputs_video,
outputs=outputs_video,
title="Pothole detector",
examples=video_path,
cache_examples=False,
)
gr.TabbedInterface(
[interface_image, interface_video],
tab_names=['Image inference', 'Video inference']
).queue().launch()
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