v03 update

README.md

@@ -4,7 +4,7 @@ emoji: 🚀
 colorFrom: purple
 colorTo: yellow
 sdk: gradio
-sdk_version: 3.0.2
+sdk_version: 3.0.6
 app_file: app.py
 pinned: true
 license: gpl-3.0

app.py

@@ -83,7 +83,28 @@ def parse_args(known=False):
         help="cuda or cpu",
     )
     parser.add_argument("--inference_size", "-isz", default=640, type=int, help="model inference size")
-    parser.add_argument("--max_detnum", "-mdn", default="50", type=str, help="model max det num")
+    parser.add_argument("--max_detnum", "-mdn", default=50, type=float, help="model max det num")
+    parser.add_argument("--slider_step", "-ss", default=0.05, type=float, help="slider step")
+    parser.add_argument(
+        "--is_login",
+        "-isl",
+        action="store_true",
+        default=False,
+        help="is login",
+    )
+    parser.add_argument('--usr_pwd',
+                        "-up",
+                        nargs='+',
+                        type=str,
+                        default=["admin", "admin"],
+                        help="user & password for login")
+    parser.add_argument(
+        "--is_share",
+        "-is",
+        action="store_true",
+        default=False,
+        help="is login",
+    )
 
     args = parser.parse_known_args()[0] if known else parser.parse_args()
     return args
@@ -122,24 +143,21 @@ def model_loading(model_name, device):
 
 
 # check information
-def export_json(results, model, img_size):
-
-    return [
-        [
-            {
-                "id": i,
-                "class": int(result[i][5]),
-                # "class_name": model.model.names[int(result[i][5])],
-                "class_name": model_cls_name_cp[int(result[i][5])],
-                "normalized_box": {
-                    "x0": round(result[i][:4].tolist()[0], 6),
-                    "y0": round(result[i][:4].tolist()[1], 6),
-                    "x1": round(result[i][:4].tolist()[2], 6),
-                    "y1": round(result[i][:4].tolist()[3], 6),},
-                "confidence": round(float(result[i][4]), 2),
-                "fps": round(1000 / float(results.t[1]), 2),
-                "width": img_size[0],
-                "height": img_size[1],} for i in range(len(result))] for result in results.xyxyn]
+def export_json(results, img_size):
+
+    return [[{
+        "ID": i,
+        "CLASS": int(result[i][5]),
+        "CLASS_NAME": model_cls_name_cp[int(result[i][5])],
+        "BOUNDING_BOX": {
+            "XMIN": round(result[i][:4].tolist()[0], 6),
+            "YMIN": round(result[i][:4].tolist()[1], 6),
+            "XMAX": round(result[i][:4].tolist()[2], 6),
+            "YMAX": round(result[i][:4].tolist()[3], 6),},
+        "CONF": round(float(result[i][4]), 2),
+        "FPS": round(1000 / float(results.t[1]), 2),
+        "IMG_WIDTH": img_size[0],
+        "IMG_HEIGHT": img_size[1],} for i in range(len(result))] for result in results.xyxyn]
 
 
 # frame conversion
@@ -168,7 +186,7 @@ def pil_draw(img, countdown_msg, textFont, xyxy, font_size, opt):
 
 
 # YOLOv5 image detection function
-def yolo_det(img, device, model_name, inference_size, conf, iou, max_num, model_cls, opt):
+def yolo_det(img, device, model_name, infer_size, conf, iou, max_num, model_cls, opt):
 
     global model, model_name_tmp, device_tmp
 
@@ -195,7 +213,7 @@ def yolo_det(img, device, model_name, inference_size, conf, iou, max_num, model_
     
     img_size = img.size # frame size
 
-    results = model(img, size=inference_size) # detection
+    results = model(img, size=infer_size) # detection
     
     # Data Frame
     dataframe = results.pandas().xyxy[0].round(2)
@@ -251,12 +269,14 @@ def yolo_det(img, device, model_name, inference_size, conf, iou, max_num, model_
             area_obj = w_obj * h_obj
             area_obj_all.append(area_obj)
 
+    # ------------JSON generate------------
     det_json = export_json(results, model, img.size)[0] # Detection information
     det_json_format = json.dumps(det_json, sort_keys=False, indent=4, separators=(",", ":"), ensure_ascii=False) # JSON formatting
+    
     if "json" not in opt:
         det_json = None
 
-    # -------pdf-------
+    # -------PDF generate-------
     report = "./Det_Report.pdf"
     if "pdf" in opt:
         pdf_generate(f"{det_json_format}", report, GYD_VERSION)
@@ -293,8 +313,6 @@ def main(args):
 
     global model, model_cls_name_cp, cls_name
 
-    slider_step = 0.05 # sliding step
-
     source = args.source
     img_tool = args.img_tool
     nms_conf = args.nms_conf
@@ -305,6 +323,10 @@ def main(args):
     device = args.device
     inference_size = args.inference_size
     max_detnum = args.max_detnum
+    slider_step = args.slider_step
+    is_login = args.is_login
+    usr_pwd = args.usr_pwd
+    is_share = args.is_share
 
     is_fonts(f"{ROOT_PATH}/fonts") # Check font files
 
@@ -358,6 +380,7 @@ def main(args):
 
     # describe
     description = "<div align='center'>Customizable target detection model, easy to install, easy to use</div>"
+    # article="https://gitee.com/CV_Lab/gradio_yolov5_det"
 
     # example image
     examples = [
@@ -403,21 +426,38 @@ def main(args):
             ["label", "pdf"],],]
 
     # interface
-    gr.Interface(
+    gyd = gr.Interface(
         fn=yolo_det,
         inputs=inputs,
         outputs=outputs,
         title=title,
         description=description,
-        article="",
+        # article=article,
         # examples=examples,
         # theme="seafoam",
         # flagging_dir="run", # output directory
-    ).launch(
-        inbrowser=True, # Automatically open default browser
-        show_tips=True, # Automatically display the latest features of gradio
     )
 
+    if not is_login:
+        gyd.launch(
+            inbrowser=True,  # Automatically open default browser
+            show_tips=True,  # Automatically display the latest features of gradio
+            share=is_share,  # Project sharing, other devices can access
+            favicon_path="./icon/logo.ico",  # web icon
+            show_error=True,  # Display error message in browser console
+            quiet=True,  # Suppress most print statements
+        )
+    else:
+        gyd.launch(
+            inbrowser=True,  # Automatically open default browser
+            show_tips=True,  # Automatically display the latest features of gradio
+            auth=usr_pwd,  # login interface
+            share=is_share,  # Project sharing, other devices can access
+            favicon_path="./icon/logo.ico",  # web icon
+            show_error=True,  # Display error message in browser console
+            quiet=True,  # Suppress most print statements
+        )
+
 
 if __name__ == "__main__":
     args = parse_args()