Update app.py

app.py

@@ -1,7 +1,9 @@
-# Gradio YOLOv8 Det v1.2.0
+# Gradio YOLOv8 Det v1.2.1
 # 创建人：曾逸夫
-# 创建时间：2023-11-10
-# pip install gradio>=4.3.0
+# 创建时间：2023-12-7
+# pip install gradio>=4.8.0
+# python gradio_yolov8_det_v1.py
+
 
 import argparse
 import csv
@@ -32,7 +34,7 @@ from PIL import Image, ImageDraw, ImageFont
 from util.fonts_opt import is_fonts
 
 # Gradio YOLOv8 Det版本
-GYD_VERSION = "Gradio YOLOv8 Det v1.2.0"
+GYD_VERSION = "Gradio YOLOv8 Det v1.2.1"
 
 # 文件后缀
 suffix_list = [".csv", ".yaml"]
@@ -503,14 +505,15 @@ def main(args):
                             device_opt = gr.Radio(choices=["cpu", "0", "1", "2", "3"], value="cpu", label="设备")
                         with gr.Row():
                             inputs_model = gr.Dropdown(choices=model_names, value=model_name, type="value", label="模型")
-                        with gr.Row():
-                            inputs_size = gr.Slider(320, 1600, step=1, value=inference_size, label="推理尺寸")
-                            max_det = gr.Slider(1, 1000, step=1, value=max_detnum, label="最大检测数")
-                        with gr.Row():
-                            input_conf = gr.Slider(0, 1, step=slider_step, value=nms_conf, label="置信度阈值")
-                            inputs_iou = gr.Slider(0, 1, step=slider_step, value=nms_iou, label="IoU 阈值")
-                        with gr.Row():
-                            obj_size = gr.Radio(choices=["所有尺寸", "小目标", "中目标", "大目标"], value="所有尺寸", label="目标尺寸")
+                        with gr.Accordion("高级设置", open=True):
+                            with gr.Row():
+                                inputs_size = gr.Slider(320, 1600, step=1, value=inference_size, label="推理尺寸")
+                                max_det = gr.Slider(1, 1000, step=1, value=max_detnum, label="最大检测数")
+                            with gr.Row():
+                                input_conf = gr.Slider(0, 1, step=slider_step, value=nms_conf, label="置信度阈值")
+                                inputs_iou = gr.Slider(0, 1, step=slider_step, value=nms_iou, label="IoU 阈值")
+                            with gr.Row():
+                                obj_size = gr.Radio(choices=["所有尺寸", "小目标", "中目标", "大目标"], value="所有尺寸", label="目标尺寸")
                         with gr.Row():
                             gr.ClearButton(inputs_img, value="清除")
                             det_btn_img = gr.Button(value='检测', variant="primary")
@@ -559,6 +562,47 @@ def main(args):
                             outputs_img_cls = gr.Image(type="pil", label="检测图片")
                         with gr.Row():
                             outputs_ratio_cls = gr.Label(label="图像分类结果")
+        with gr.Accordion("Gradio YOLOv8 Det 安装与使用教程"):
+            gr.Markdown(
+                """## Gradio YOLOv8 Det 安装与使用教程
+            ```shell
+            conda create -n yolo python==3.8
+            conda activate yolo # 进入环境
+            git clone https://gitee.com/CV_Lab/gradio-yolov8-det.git
+            cd gradio-yolov8-det
+            pip install -r ./requirements.txt -U
+            ```
+            ```shell
+            # 共享模式
+            python gradio_yolov8_det_v1.py -is # 在浏览器中以共享模式打开，https://**.gradio.app/
+
+            # 自定义模型配置
+            python gradio_yolov8_det_v1.py -mc ./model_config/model_name_all.yaml
+
+            # 自定义下拉框默认模型名称
+            python gradio_yolov8_det_v1.py -mn yolov8m
+
+            # 自定义类别名称
+            python gradio_yolov8_det_v1.py -cls ./cls_name/cls_name_zh.yaml  （目标检测与图像分割）
+            python gradio_yolov8_det_v1.py -cin ./cls_name/cls_imgnet_name_zh.yaml  （图像分类）
+
+            # 自定义NMS置信度阈值
+            python gradio_yolov8_det_v1.py -conf 0.8
+
+            # 自定义NMS IoU阈值
+            python gradio_yolov8_det_v1.py -iou 0.5
+
+            # 设置推理尺寸，默认为640
+            python gradio_yolov8_det_v1.py -isz 320
+
+            # 设置最大检测数，默认为50
+            python gradio_yolov8_det_v1.py -mdn 100
+
+            # 设置滑块步长，默认为0.05
+            python gradio_yolov8_det_v1.py -ss 0.01
+            ```
+            """
+            )
 
         det_btn_img.click(fn=yolo_det_img,
                           inputs=[