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import numpy as np
from PIL import Image
import gradio as gr
from ultralytics import YOLO
from ultralytics.yolo.utils.ops import scale_image
import cv2
# Load the YOLO model
m_raw_model = YOLO("M-Raw.pt")
n_raw_model = YOLO("N-Raw.pt")
s_raw_model = YOLO("S-Raw.pt")
def snap(image, model, conf, iou):
# Convert the image to a numpy array
image = np.array(image)
# Run the selected model
results = None
if model == "M-Raw":
results = m_raw_model(image, conf=conf, iou=iou)
elif model == "N-Raw":
results = n_raw_model(image, conf=conf, iou=iou)
elif model == "S-Raw":
results = s_raw_model(image, conf=conf, iou=iou)
# Convert the results list into an output image
for result in results:
classes = result.boxes.cls.cpu().numpy()
probs = result.boxes.conf.cpu().numpy()
boxes = result.boxes.xyxy[0].cpu().numpy()
for i in range(len(boxes)):
x1, y1, x2, y2 = boxes[i]
x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
cv2.putText(image, f"{classes[i]} {probs[i]:.2f}", (x1, y1), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
# Convert the resulting image to a PIL image
resulting_image = Image.fromarray(image)
# Get the labels
labels = results.pandas().xyxy[0]["name"].values
# Sort the labels by their x-value first and then by their y-value
# print(labels)
return [resulting_image]
demo = gr.Interface(
snap,
[gr.Image(source="webcam", tool=None, streaming=True), gr.inputs.Radio(["M-Raw", "S-Raw", "N-Raw"], value="M-Raw"), gr.Slider(0, 1, value=0.6, label="Classifier Confidence Threshold"), gr.Slider(0, 1, value=0.7, label="IoU Threshold")],
["image"],
title="Baybayin Instance Detection"
)
if __name__ == "__main__":
demo.launch() |