Update app.py

app.py

@@ -1,6 +1,7 @@
 import argparse
 import csv
 import sys
+sys.path.append("./LIVE")
 from pathlib import Path
 
 import gradio as gr
@@ -142,6 +143,7 @@ run_cmd("ls")
 run_cmd("git submodule update --init --recursive")
 run_cmd("python setup.py install --user")
 run_cmd("ls")
+run_cmd("python main.py --config config/base.yaml --experiment experiment_5x1 --signature smile --target figures/smile.png --log_dir log/")
 
 
 
@@ -171,27 +173,6 @@ def yaml_csv(file_path, file_tag):
 
 def main(args):
     gr.close_all()
-
-    global model
-
-    slider_step = 0.05  # 滑动步长
-
-    nms_conf = args.nms_conf
-    nms_iou = args.nms_iou
-    label_opt = args.label_dnt_show
-    model_name = args.model_name
-    model_cfg = args.model_cfg
-    cls_name = args.cls_name
-    device = args.device
-    inference_size = args.inference_size
-
-    # 模型加载
-    model = model_loading(model_name, device)
-
-    model_names = yaml_csv(model_cfg, "model_names")
-    model_cls_name = yaml_csv(cls_name, "model_cls_name")
-
-
     # -------------------Inputs-------------------
     inputs_img = gr.inputs.Image(type="pil", label="Input Image")
     experiment_id = gr.inputs.Radio(
@@ -202,52 +183,21 @@ def main(args):
             "add [1,2,4,8,16,32, ...] total 256 paths"], type="value", default="add [1,1,1,1,1] paths", label="Path Adding Scheduler"
     )
 
-
-
-
-
-    device = gr.inputs.Dropdown(
-        choices=["cpu"], default=device, type="value", label="设备"
-    )
-    inputs_model = gr.inputs.Dropdown(
-        choices=model_names, default=model_name, type="value", label="模型"
-    )
-    inputs_size = gr.inputs.Radio(
-        choices=[320, 640], default=inference_size, label="推理尺寸"
-    )
-    input_conf = gr.inputs.Slider(
-        0, 1, step=slider_step, default=nms_conf, label="置信度阈值"
-    )
-    inputs_iou = gr.inputs.Slider(
-        0, 1, step=slider_step, default=nms_iou, label="IoU 阈值"
-    )
-    inputs_label = gr.inputs.Checkbox(default=label_opt, label="标签显示")
-    inputs_clsName = gr.inputs.CheckboxGroup(
-        choices=model_cls_name, default=model_cls_name, type="index", label="类别"
-    )
-
-    # 输入参数
+    # inputs
     inputs = [
-        inputs_img,  # 输入图片
+        inputs_img,  # input image
         experiment_id, # path adding scheduler
-        # device,  # 设备
-        # inputs_model,  # 模型
-        # inputs_size,  # 推理尺寸
-        # input_conf,  # 置信度阈值
-        # inputs_iou,  # IoU阈值
-        # inputs_label,  # 标签显示
-        # inputs_clsName,  # 类别
     ]
-    # 输出参数
+    # outputs
     outputs = gr.outputs.Image(type="pil", label="检测图片")
     outputs02 = gr.outputs.JSON(label="检测信息")
 
-    # 标题
+    # title
     title = "LIVE: Towards Layer-wise Image Vectorization"
-    # 描述
+    # description
     description = "<div align='center'>(CVPR 2022 Oral Presentation)</div>"
 
-    # 示例图片
+    # examples
     examples = [
         [
             "./examples/1.png",
@@ -271,7 +221,7 @@ def main(args):
         ],
     ]
 
-    # 接口
+    # Interface
     gr.Interface(
         fn=yolo_det,
         inputs=inputs,