Spaces:
Sleeping
Sleeping
File size: 2,909 Bytes
dd57c98 9ec6d86 dd57c98 9ec6d86 dd57c98 9ec6d86 dd57c98 9ec6d86 dd57c98 9ec6d86 dd57c98 0783c95 dd57c98 0783c95 9ec6d86 dd57c98 9ec6d86 dd57c98 9ec6d86 |
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 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 |
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.
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 det in 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
cap.release()
inputs_video = gr.components.Video(label="Input Video")
outputs_video = gr.components.Image(label="Output Image", type="numpy")
interface_video = gr.Interface(
fn=show_preds_video,
inputs=inputs_video,
outputs=outputs_video,
title="Pothole Detector",
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()
|