Spaces:
Sleeping
Sleeping
File size: 2,461 Bytes
473c7d4 5e01f68 473c7d4 5e01f68 473c7d4 5e01f68 473c7d4 5e01f68 473c7d4 5e01f68 473c7d4 5e01f68 473c7d4 5e01f68 473c7d4 5e01f68 473c7d4 5e01f68 473c7d4 5e01f68 473c7d4 5e01f68 473c7d4 3b3dfe3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 |
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 = [['image_0.jpg'], ['image_1.jpg']]
video_path = [['video.mp4']]
def save_annotation(image_path, results):
height, width, _ = cv2.imread(image_path).shape
annotation_txt = ""
for i, det in enumerate(results.boxes.xyxy):
# YOLO format: class x_center y_center width height
class_id = int(results.names[int(det[5])])
x_center, y_center, bbox_width, bbox_height = det[0], det[1], det[2] - det[0], det[3] - det[1]
annotation_txt += f"{class_id} {x_center / width:.6f} {y_center / height:.6f} {bbox_width / width:.6f} {bbox_height / height:.6f}\n"
return annotation_txt
def show_preds_image(image_path):
image = cv2.imread(image_path)
outputs = model.predict(source=image_path)
results = outputs[0].cpu().numpy()
annotation_txt = save_annotation(image_path, results)
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
)
# Save YOLO format annotation to a txt file
annotation_filename = f"annotation_{os.path.basename(image_path).split('.')[0]}.txt"
with open(annotation_filename, 'w') as f:
f.write(annotation_txt)
return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
inputs_image = [gr.components.Image(type="filepath", label="Input Image"),]
outputs_image = [gr.components.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,
)
interface_image.launch(debug=True)
|