commit

app.py

@@ -1,21 +1,12 @@
 import gradio as gr
 import torch
 import yolov5
-import subprocess
-import tempfile
-import time
-from pathlib import Path
-import uuid
-import cv2
-import gradio as gr
-
-
 
 # Images
-#torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg')
-#torch.hub.download_url_to_file('https://raw.githubusercontent.com/obss/sahi/main/tests/data/small-vehicles1.jpeg', 'small-vehicles1.jpeg')
-    
-def image_fn(
+torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg')
+torch.hub.download_url_to_file('https://raw.githubusercontent.com/WongKinYiu/yolov7/main/inference/images/image3.jpg', 'image3.jpg')
+
+def yolov5_inference(
     image: gr.inputs.Image = None,
     model_path: gr.inputs.Dropdown = None,
     image_size: gr.inputs.Slider = 640,
@@ -33,36 +24,102 @@ def image_fn(
     Returns:
         Rendered image
     """
-
-    model = yolov5.load(model_path, device="cpu", hf_model=True, trace=False)
+    model = yolov5.load(model_path, device="cpu")
     model.conf = conf_threshold
     model.iou = iou_threshold
     results = model([image], size=image_size)
     return results.render()[0]
-  
+        
+
+inputs = [
+    gr.inputs.Image(type="pil", label="Input Image"),
+    gr.inputs.Dropdown(["yolov5s.pt", "alshimaa/yolo5_epoch100"], label="Model"),
+    gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
+    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
+    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
+]
 
+outputs = gr.outputs.Image(type="filepath", label="Output Image")
+title = "YOLOv5"
+description = "YOLOv5 is a family of object detection models pretrained on COCO dataset. This model is a pip implementation of the original YOLOv5 model."
 
+examples = [['zidane.jpg', 'yolov5s.pt', 640, 0.25, 0.45], ['image3.jpg', 'yolov5s.pt', 640, 0.25, 0.45]]
 demo_app = gr.Interface(
-    fn=image_fn,
-    inputs=[
-    gr.inputs.Image(type="pil", label="Input Image"),
-    gr.inputs.Dropdown(
-        choices=[
-            "alshimaa/yolo5_epoch100",
-            #"kadirnar/yolov7-v0.1",
-        ],
-        default="alshimaa/yolo5_epoch100",
-        label="Model",
-    )
-    #gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size")
-    #gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
-    #gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold")
-],
-    outputs=gr.outputs.Image(type="filepath", label="Output Image"),
-    title="Object Detector: Identify People Without Mask",
-    examples=[['img1.png', 'alshimaa/yolo5_epoch100', 640, 0.25, 0.45], ['img2.png', 'alshimaa/yolo5_epoch100', 640, 0.25, 0.45], ['img3.png', 'alshimaa/yolo5_epoch100', 640, 0.25, 0.45]],
+    fn=yolov5_inference,
+    inputs=inputs,
+    outputs=outputs,
+    title=title,
+    examples=examples,
     cache_examples=True,
     live=True,
     theme='huggingface',
 )
 demo_app.launch(debug=True, enable_queue=True)
+# import gradio as gr
+# import torch
+# import yolov5
+# import subprocess
+# import tempfile
+# import time
+# from pathlib import Path
+# import uuid
+# import cv2
+# import gradio as gr
+
+
+
+# # Images
+# #torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg')
+# #torch.hub.download_url_to_file('https://raw.githubusercontent.com/obss/sahi/main/tests/data/small-vehicles1.jpeg', 'small-vehicles1.jpeg')
+    
+# def image_fn(
+#     image: gr.inputs.Image = None,
+#     model_path: gr.inputs.Dropdown = None,
+#     image_size: gr.inputs.Slider = 640,
+#     conf_threshold: gr.inputs.Slider = 0.25,
+#     iou_threshold: gr.inputs.Slider = 0.45,
+# ):
+#     """
+#     YOLOv5 inference function
+#     Args:
+#         image: Input image
+#         model_path: Path to the model
+#         image_size: Image size
+#         conf_threshold: Confidence threshold
+#         iou_threshold: IOU threshold
+#     Returns:
+#         Rendered image
+#     """
+
+#     model = yolov5.load(model_path, device="cpu", hf_model=True, trace=False)
+#     model.conf = conf_threshold
+#     model.iou = iou_threshold
+#     results = model([image], size=image_size)
+#     return results.render()[0]
+  
+
+
+# demo_app = gr.Interface(
+#     fn=image_fn,
+#     inputs=[
+#     gr.inputs.Image(type="pil", label="Input Image"),
+#     gr.inputs.Dropdown(
+#         choices=[
+#             "alshimaa/yolo5_epoch100",
+#             #"kadirnar/yolov7-v0.1",
+#         ],
+#         default="alshimaa/yolo5_epoch100",
+#         label="Model",
+#     )
+#     #gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size")
+#     #gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
+#     #gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold")
+# ],
+#     outputs=gr.outputs.Image(type="filepath", label="Output Image"),
+#     title="Object Detector: Identify People Without Mask",
+#     examples=[['img1.png', 'alshimaa/yolo5_epoch100', 640, 0.25, 0.45], ['img2.png', 'alshimaa/yolo5_epoch100', 640, 0.25, 0.45], ['img3.png', 'alshimaa/yolo5_epoch100', 640, 0.25, 0.45]],
+#     cache_examples=True,
+#     live=True,
+#     theme='huggingface',
+# )
+# demo_app.launch(debug=True, enable_queue=True)