Create app.py

app.py

@@ -0,0 +1,88 @@
+import gradio as gr 
+import pandas as pd 
+
+from skimage import data
+from PIL import Image
+from torchkeras import plots 
+from torchkeras.data import get_url_img
+
+from pathlib import Path
+from ultralytics import YOLO
+import ultralytics
+from ultralytics.yolo.data import utils 
+
+model = YOLO('yolov8n.pt')
+
+#prepare example images
+Image.fromarray(data.coffee()).save('coffee.jpeg') 
+Image.fromarray(data.astronaut()).save('people.jpeg')
+Image.fromarray(data.cat()).save('cat.jpeg')
+
+#load class_names
+yaml_path = str(Path(ultralytics.__file__).parent/'datasets/coco128.yaml') 
+class_names = utils.yaml_load(yaml_path)['names']
+
+def detect(img):
+    if isinstance(img,str):
+        img = get_url_img(img) if img.startswith('http') else Image.open(img).convert('RGB')
+    result = model.predict(source=img)
+    if len(result[0].boxes.boxes)>0:
+        vis = plots.plot_detection(img,boxes=result[0].boxes.boxes,
+                     class_names=class_names, min_score=0.2)
+    else:
+        vis = img
+    return vis
+
+with gr.Blocks() as demo:
+    gr.Markdown("# yolov8目标检测演示")
+    
+    with gr.Tab("选择测试图片"):
+        files = ['people.jpeg','coffee.jpeg','cat.jpeg']
+        drop_down = gr.Dropdown(choices=files,value=files[0])
+        button = gr.Button("执行检测",variant="primary")
+        
+        
+        gr.Markdown("## 预测输出")
+        out_img = gr.Image(type='pil')
+        
+        button.click(detect,
+                     inputs=drop_down, 
+                     outputs=out_img)
+        
+    with gr.Tab("输入图片链接"):
+        default_url = 'https://t7.baidu.com/it/u=3601447414,1764260638&fm=193&f=GIF'
+        url = gr.Textbox(value=default_url)
+        button = gr.Button("执行检测",variant="primary")
+        
+        gr.Markdown("## 预测输出")
+        out_img = gr.Image(type='pil')
+        
+        button.click(detect,
+                     inputs=url, 
+                     outputs=out_img)
+        
+    with gr.Tab("上传本地图片"):
+        input_img = gr.Image(type='pil')
+        button = gr.Button("执行检测",variant="primary")
+        
+        gr.Markdown("## 预测输出")
+        out_img = gr.Image(type='pil')
+        
+        button.click(detect,
+                     inputs=input_img, 
+                     outputs=out_img)
+        
+    with gr.Tab("捕捉摄像头喔"):
+        input_img = gr.Image(source='webcam',type='pil')
+        button = gr.Button("执行检测",variant="primary")
+        
+        gr.Markdown("## 预测输出")
+        out_img = gr.Image(type='pil')
+        
+        button.click(detect,
+                     inputs=input_img, 
+                     outputs=out_img)
+        
+gr.close_all() 
+demo.queue()
+demo.launch()