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import gradio as gr
import cv2
import requests
import os
import numpy as np
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)
open(save_name, 'wb').write(file.content)
for i, url in enumerate(file_urls):
if 'mp4' in file_urls[i]:
download_file(
file_urls[i],
f"video.mp4"
)
else:
download_file(
file_urls[i],
f"image_{i}.jpg"
)
model = YOLO('best.pt')
path = [['1.jpeg'], ['2.jpeg']]
video_path = [['contoh.mp4']]
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_video(video_path):
cap = cv2.VideoCapture(video_path)
while(cap.isOpened()):
ret, frame = cap.read()
if ret:
frame_copy = frame.copy()
outputs = model.predict(source=frame)
results = outputs[0].cpu().numpy()
for i, det in enumerate(results.boxes.xyxy):
cv2.rectangle(
frame_copy,
(int(det[0]), int(det[1])),
(int(det[2]), int(det[3])),
color=(0, 0, 255),
thickness=2,
lineType=cv2.LINE_AA
)
yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
else:
break
def show_preds_webcam(frame):
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
outputs = model.predict(source=frame)
results = outputs[0].cpu().numpy()
for i, det in enumerate(results.boxes.xyxy):
cv2.rectangle(
frame,
(int(det[0]), int(det[1])),
(int(det[2]), int(det[3])),
color=(0, 0, 255),
thickness=2,
lineType=cv2.LINE_AA
)
return frame
inputs_image = gr.Image(label="Input Image")
outputs_image = gr.Image(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,
)
inputs_video = gr.Video(label="Input Video")
outputs_video = gr.Image(label="Output Image")
interface_video = gr.Interface(
fn=show_preds_video,
inputs=inputs_video,
outputs=outputs_video,
title="Pothole detector",
examples=video_path,
cache_examples=False,
)
inputs_webcam = gr.Image(sources="webcam", streaming=True)
outputs_webcam = gr.Image(label="Output Image")
interface_webcam = gr.Interface(
fn=show_preds_webcam,
inputs=inputs_webcam,
outputs=outputs_webcam,
title="Webcam Object Detection"
)
gr.TabbedInterface(
[interface_image, interface_video, interface_webcam],
tab_names=['Image Inference', 'Video Inference', 'Webcam Inference']
).queue().launch()
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