v04 update

app.py

@@ -243,6 +243,19 @@ def yolo_det_img(img, device, model_name, infer_size, conf, iou, max_num, model_
 
     # Data Frame
     dataframe = results.pandas().xyxy[0].round(2)
+    
+    det_csv = "./Det_Report.csv"
+    det_excel = "./Det_Report.xlsx"
+
+    if "csv" in opt:
+        dataframe.to_csv(det_csv, index=False)
+    else:
+        det_csv = None
+    
+    if "excel" in opt:
+        dataframe.to_excel(det_excel, sheet_name='sheet1', index=False)
+    else:
+        det_excel = None
 
     # ----------------Load fonts----------------
     yaml_index = cls_name.index(".yaml")
@@ -333,7 +346,7 @@ def yolo_det_img(img, device, model_name, infer_size, conf, iou, max_num, model_
     for k, v in clsDet_dict.items():
         clsRatio_dict[k] = v / clsDet_dict_sum
 
-    return det_img, img_crops, objSize_dict, clsRatio_dict, dataframe, det_json, report
+    return det_img, img_crops, objSize_dict, clsRatio_dict, dataframe, det_json, report, det_csv, det_excel
 
 
 # YOLOv5 video detection function
@@ -479,7 +492,7 @@ def main(args):
     inputs_iou01 = gr.Slider(0, 1, step=slider_step, value=nms_iou, label="IoU threshold")
     inputs_maxnum01 = gr.Number(value=max_detnum, label="Maximum number of detections")
     inputs_clsName01 = gr.CheckboxGroup(choices=model_cls_name, value=model_cls_name, type="index", label="category")
-    inputs_opt01 = gr.CheckboxGroup(choices=["label", "pdf", "json", "refresh_yolov5"],
+    inputs_opt01 = gr.CheckboxGroup(choices=["refresh_yolov5", "label", "pdf", "json", "csv", "excel"],
                                     value=["label", "pdf"],
                                     type="value",
                                     label="operate")
@@ -493,7 +506,7 @@ def main(args):
     inputs_iou02 = gr.Slider(0, 1, step=slider_step, value=nms_iou, label="IoU threshold")
     inputs_maxnum02 = gr.Number(value=max_detnum, label="Maximum number of detections")
     inputs_clsName02 = gr.CheckboxGroup(choices=model_cls_name, value=model_cls_name, type="index", label="category")
-    inputs_opt02 = gr.CheckboxGroup(choices=["label", "refresh_yolov5"], value=["label"], type="value", label="operate")
+    inputs_opt02 = gr.CheckboxGroup(choices=["refresh_yolov5", "label"], value=["label"], type="value", label="operate")
 
     # Input parameters
     inputs_img_list = [
@@ -522,21 +535,23 @@ def main(args):
 
     # -------------------output component-------------------
     outputs_img = gr.Image(type="pil", label="Detection image")
-    outputs_json = gr.JSON(label="Detection information")
-    outputs_pdf = gr.File(label="Download test report")
+    outputs_crops = gr.Gallery(label="Object crop")
     outputs_df = gr.Dataframe(max_rows=5,
                               overflow_row_behaviour="paginate",
                               type="pandas",
                               label="List of detection information")
     outputs_objSize = gr.Label(label="Object size ratio statistics")
     outputs_clsSize = gr.Label(label="Category detection proportion statistics")
-    outputs_crops = gr.Gallery(label="Object crop")
+    outputs_json = gr.JSON(label="Detection information")
+    outputs_pdf = gr.File(label="pdf detection report")
+    outputs_csv = gr.File(label="csv detection report")
+    outputs_excel = gr.File(label="xlsx detection report")
 
     # -------------------output component-------------------
     outputs_video = gr.Video(format='mp4', label="Detection video")
 
     # output parameters
-    outputs_img_list = [outputs_img, outputs_crops, outputs_objSize, outputs_clsSize, outputs_df, outputs_json, outputs_pdf]
+    outputs_img_list = [outputs_img, outputs_crops, outputs_objSize, outputs_clsSize, outputs_df, outputs_json, outputs_pdf, outputs_csv, outputs_excel]
     outputs_video_list = [outputs_video]
 
     # title

requirements.txt

@@ -17,6 +17,7 @@ rich>=12.2.0
 fpdf>=1.7.2
 plotly>=5.7.0
 bokeh>=2.4.2
+openpyxl>=3.0.10
 
 # Logging -------------------------------------
 tensorboard>=2.4.1