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import gradio as gr
import cv2
import requests
import os
from ultralytics import YOLO
model = YOLO('best_model.pt')
example_list = [["examples/" + example] for example in os.listdir("examples")]
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)
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,
)
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)
inputs_video = [
gr.components.Video(type="filepath", label="Input Video"),
]
outputs_video = [
gr.components.Image(type="numpy", 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,
)
gr.TabbedInterface(
[interface_image, interface_video],
tab_names=['Image inference', 'Video inference']
).queue().launch() |