Update app.py

app.py

@@ -1,7 +1,7 @@
-# Gradio YOLOv8 Det v1.2.1
+# Gradio YOLOv8 Det v1.3.0
 # 创建人：曾逸夫
-# 创建时间：2023-12-7
-# pip install gradio>=4.8.0
+# 创建时间：2023-12-15
+# pip install gradio>=4.9.1
 # python gradio_yolov8_det_v1.py
 
 
@@ -581,130 +581,77 @@ def main(args):
         with gr.Row():
             gr.Markdown(GYD_SUB_TITLE)
         with gr.Row():
-            with gr.Column(scale=1):
                 with gr.Tabs():
                     with gr.TabItem("目标检测与图像分割"):
                         with gr.Row():
-                            inputs_img = gr.Image(
-                                image_mode="RGB", type="filepath", label="原始图片"
-                            )
-                        with gr.Row():
-                            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.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")
+                            with gr.Column(scale=1):
+                                with gr.Row():
+                                    inputs_img = gr.Image(image_mode="RGB", type="filepath", label="原始图片")
+                                with gr.Row():
+                                    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.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")
+                            with gr.Column(scale=1):
+                                # with gr.Row():
+                                #     outputs_img = gr.Image(type="pil", label="检测图片")
+                                with gr.Row():
+                                    outputs_img_slider = ImageSlider(position=0.5, label="检测图片")
+                                with gr.Row():
+                                    outputs_imgfiles = gr.Files(label="图片下载")
+                                with gr.Row():
+                                    outputs_objSize = gr.Label(label="目标尺寸占比统计")
+                                with gr.Row():
+                                    outputs_clsSize = gr.Label(label="类别检测占比统计")
                         with gr.Row():
                             gr.Examples(
                                 examples=EXAMPLES_DET,
                                 fn=yolo_det_img,
                                 inputs=[
-                                    inputs_img,
-                                    inputs_model,
-                                    device_opt,
-                                    inputs_size,
-                                    input_conf,
-                                    inputs_iou,
-                                    max_det,
-                                    obj_size,
-                                ],
+                                    inputs_img, inputs_model, device_opt, inputs_size, input_conf, inputs_iou, max_det,
+                                    obj_size],
                                 # outputs=[outputs_img, outputs_objSize, outputs_clsSize],
-                                cache_examples=False,
-                            )
+                                cache_examples=False)
 
                     with gr.TabItem("图像分类"):
                         with gr.Row():
-                            inputs_img_cls = gr.Image(
-                                image_mode="RGB", type="filepath", label="原始图片"
-                            )
-                        with gr.Row():
-                            inputs_model_cls = gr.Dropdown(
-                                choices=[
-                                    "yolov8n-cls",
-                                    "yolov8s-cls",
-                                    "yolov8l-cls",
-                                    "yolov8m-cls",
-                                    "yolov8x-cls",
-                                ],
-                                value="yolov8s-cls",
-                                type="value",
-                                label="模型",
-                            )
-                        with gr.Row():
-                            gr.ClearButton(inputs_img, value="清除")
-                            det_btn_img_cls = gr.Button(value="检测", variant="primary")
+                            with gr.Column(scale=1):
+                                with gr.Row():
+                                    inputs_img_cls = gr.Image(image_mode="RGB", type="filepath", label="原始图片")
+                                with gr.Row():
+                                    inputs_model_cls = gr.Dropdown(choices=[
+                                        "yolov8n-cls", "yolov8s-cls", "yolov8l-cls", "yolov8m-cls", "yolov8x-cls"],
+                                                                value="yolov8s-cls",
+                                                                type="value",
+                                                                label="模型")
+                                with gr.Row():
+                                    gr.ClearButton(inputs_img, value="清除")
+                                    det_btn_img_cls = gr.Button(value='检测', variant="primary")
+                            with gr.Column(scale=1):
+                                with gr.Row():
+                                    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.Row():
                             gr.Examples(
                                 examples=EXAMPLES_CLAS,
                                 fn=yolo_cls_img,
                                 inputs=[inputs_img_cls, inputs_model_cls],
                                 # outputs=[outputs_img_cls, outputs_ratio_cls],
-                                cache_examples=False,
-                            )
-
-            # -------- 输出 --------
-            with gr.Column(scale=1):
-                with gr.Tabs():
-                    with gr.TabItem("目标检测与图像分割"):
-                        # with gr.Row():
-                        #     outputs_img = gr.Image(type="pil", label="检测图片")
-                        with gr.Row():
-                            outputs_img_slider = ImageSlider(position=0.5, label="检测图片")
-                        with gr.Row():
-                            outputs_imgfiles = gr.Files(label="图片下载")
-                        with gr.Row():
-                            outputs_objSize = gr.Label(label="目标尺寸占比统计")
-                        with gr.Row():
-                            outputs_clsSize = gr.Label(label="类别检测占比统计")
+                                cache_examples=False)
+                            
 
-                    with gr.TabItem("图像分类"):
-                        with gr.Row():
-                            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
@@ -718,29 +665,21 @@ def main(args):
             ```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
             ```
@@ -787,4 +726,4 @@ if __name__ == "__main__":
         favicon_path="./icon/logo.ico",  # 网页图标
         show_error=True,  # 在浏览器控制台中显示错误信息
         quiet=True,  # 禁止大多数打印语句
-    )
+    )