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4b74119 | 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 | import cv2
import os
from ultralytics import YOLO
import numpy as np
import PIL.Image as Image
import gradio as gr
def predict_image(img):
# print(img)
# labels=['awake', 'drowsy']
model= YOLO(r"C:\Users\sdadi\Desktop\M_L\Drowsiness_detec\yolov8s_drowsy.pt")
im_array=[]
(height, width, channels)= img.shape
img= cv2.resize(img, (640, 640))
img= cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
preds= model.predict(source= img, show_labels=True, show_conf=False, imgsz=640)
try:
for pred in preds:
im_array= pred.plot()
# im_array = Image.fromarray(im_array[..., ::-1])
# pred =preds[0]
# boxes = pred.boxes.cpu().numpy()
# argmax= np.argmax(boxes.conf[0])
# labelmax= boxes.cls[argmax]
# text= labels[int(labelmax)]
except:
pass
im_array= cv2.cvtColor(im_array, cv2.COLOR_BGR2RGB)
im_array= cv2.resize(im_array, (width, height))
return im_array
image_iface= gr.Interface(fn =predict_image,
inputs= gr.Image(label='Upload Image', sources=['upload', 'clipboard']),
outputs= gr.Image(label='Inference Results'),
description= "Upload Images for Inference using the trained model")
# gr.slider(minimum= 0.1, maximum= 0.8, value= 0.5))
demo= gr.TabbedInterface(interface_list=[image_iface], tab_names=['Image Inference'], theme="soft", title= "Drowsiness detection using YOLOv8n")
if __name__ =='__main__':
demo.launch(debug="True")
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