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1e54fbd | 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 | import numpy as np
import pandas as pd
import cv2
import gradio as gr
import torch
from ultralyticsplus import YOLO, render_result
import matplotlib.pyplot as plt
# Creating a function to perform predictions
def prediction(image: gr.Image = None,
image_size: gr.Slider = 640,
conf_threshold: gr.Slider = 0.4,
iou_threshold: gr.Slider = 0.50):
model = YOLO("best.pt")
results = model.predict(
source=image,
conf=conf_threshold,
iou=iou_threshold,
imgsz=image_size
)
image = cv2.imread(image)
for result in results[0].obb:
point_1_x = float(result.xyxyxyxy[0][0][0])
point_1_y = float(result.xyxyxyxy[0][0][1])
point_2_x = float(result.xyxyxyxy[0][1][0])
point_2_y = float(result.xyxyxyxy[0][1][1])
point_3_x = float(result.xyxyxyxy[0][2][0])
point_3_y = float(result.xyxyxyxy[0][2][1])
point_4_x = float(result.xyxyxyxy[0][3][0])
point_4_y = float(result.xyxyxyxy[0][3][1])
cls = int(result.cls)
if cls == 1:
color = (0, 255, 0)
else:
color = (255, 0, 0)
conf = float(result.conf)
text = f"{cls} : {np.round(conf) * 100}%"
points = np.array([[point_1_x, point_1_y],
[point_2_x, point_2_y],
[point_3_x, point_3_y],
[point_4_x, point_4_y]], np.int32)
points = points.reshape((-1, 1, 2))
cv2.polylines(image, [points], isClosed = True, color = color, thickness = 2)
return image
inputs = [
gr.Image(type="filepath", label="Select an image"),
gr.Slider(minimum=320, maximum=1280, value=640, step=32, label="Image Size"),
gr.Slider(minimum=0.0, maximum=1.0, value=0.25, step=0.05, label="Confidence Threshold"),
gr.Slider(minimum=0.0, maximum=1.0, value=0.45, step=0.05, label="IOU Threshold")
]
outputs = gr.Image(type = "filepath", label="Output Image")
yolo_app = gr.Interface(
fn = prediction,
inputs = inputs,
outputs = outputs,
title = "VPS Model"
)
yolo_app.launch(debug = True, share = True)
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