commit

app.py

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+# import gradio as gr
+# import torch
+# import yolov5
+
+# # 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/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,
+#     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")
+#     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", "yolov5l.pt", "yolov5x.pt"], 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=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)

requirements.txt

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+gradio
+torch
+yolov5
+HfApi

yolo5_epoch100

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+Subproject commit c2212ddb924a66157b32a3af4f71b35c3d4c23fb