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app.py

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+import gradio as gr
+import sahi.utils
+from sahi import AutoDetectionModel
+import sahi.predict
+import sahi.slicing
+from PIL import Image
+import numpy
+
+IMAGE_SIZE = 640
+
+# Images
+sahi.utils.file.download_from_url(
+    "https://user-images.githubusercontent.com/34196005/142730935-2ace3999-a47b-49bb-83e0-2bdd509f1c90.jpg",
+    "apple_tree.jpg",
+)
+
+
+
+# Model
+model = AutoDetectionModel.from_pretrained(
+    model_type="yolov5", model_path="yolov5s6.pt", device="cpu", confidence_threshold=0.5, image_size=IMAGE_SIZE
+)
+
+
+def sahi_yolo_inference(
+    image,
+    slice_height=512,
+    slice_width=512,
+    overlap_height_ratio=0.2,
+    overlap_width_ratio=0.2,
+    postprocess_type="NMS",
+    postprocess_match_metric="IOU",
+    postprocess_match_threshold=0.5,
+    postprocess_class_agnostic=False,
+):
+
+    image_width, image_height = image.size
+    sliced_bboxes = sahi.slicing.get_slice_bboxes(
+        image_height,
+        image_width,
+        slice_height,
+        slice_width,
+        False,
+        overlap_height_ratio,
+        overlap_width_ratio,
+    )
+    if len(sliced_bboxes) > 60:
+        raise ValueError(
+            f"{len(sliced_bboxes)} slices are too much for huggingface spaces, try smaller slice size."
+        )
+
+    # standard inference
+    prediction_result_1 = sahi.predict.get_prediction(
+        image=image, detection_model=model
+    )
+    print(image)
+    visual_result_1 = sahi.utils.cv.visualize_object_predictions(
+        image=numpy.array(image),
+        object_prediction_list=prediction_result_1.object_prediction_list,
+    )
+    output_1 = Image.fromarray(visual_result_1["image"])
+
+    # sliced inference
+    prediction_result_2 = sahi.predict.get_sliced_prediction(
+        image=image,
+        detection_model=model,
+        slice_height=int(slice_height),
+        slice_width=int(slice_width),
+        overlap_height_ratio=overlap_height_ratio,
+        overlap_width_ratio=overlap_width_ratio,
+        postprocess_type=postprocess_type,
+        postprocess_match_metric=postprocess_match_metric,
+        postprocess_match_threshold=postprocess_match_threshold,
+        postprocess_class_agnostic=postprocess_class_agnostic,
+    )
+    visual_result_2 = sahi.utils.cv.visualize_object_predictions(
+        image=numpy.array(image),
+        object_prediction_list=prediction_result_2.object_prediction_list,
+    )
+
+    output_2 = Image.fromarray(visual_result_2["image"])
+
+    return output_1, output_2
+
+
+inputs = [
+    gr.Image(type="pil", label="Original Image"),
+    gr.Number(default=512, label="slice_height"),
+    gr.Number(default=512, label="slice_width"),
+    gr.Number(default=0.2, label="overlap_height_ratio"),
+    gr.Number(default=0.2, label="overlap_width_ratio"),
+    gr.Dropdown(
+        ["NMS", "GREEDYNMM"],
+        type="value",
+        value="NMS",
+        label="postprocess_type",
+    ),
+    gr.Dropdown(
+        ["IOU", "IOS"], type="value", default="IOU", label="postprocess_type"
+    ),
+    gr.Number(default=0.5, label="postprocess_match_threshold"),
+    gr.Checkbox(default=True, label="postprocess_class_agnostic"),
+]
+
+outputs = [
+    gr.Image(type="pil", label="YOLOv5s"),
+    gr.Image(type="pil", label="YOLOv5s + SAHI"),
+]
+
+title = "Small Object Detection with SAHI + YOLOv5"
+description = "SAHI + YOLOv5 demo for small object detection. Upload an image or click an example image to use."
+article = "<p style='text-align: center'><a href='http://claireye.com.tw'>Claireye</a> | 2023</p>"
+examples = [
+    ["apple_tree.jpg", 256, 256, 0.2, 0.2, "NMS", "IOU", 0.4, True]
+
+]
+
+gr.Interface(
+    sahi_yolo_inference,
+    inputs,
+    outputs,
+    title=title,
+    description=description,
+    article=article,
+    examples=examples,
+    theme="huggingface",
+    cache_examples=True,
+).launch(debug=True, enable_queue=True)

requirements.txt

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+torch==1.10.2+cpu
+torchvision==0.11.3+cpu
+-f https://download.pytorch.org/whl/torch_stable.html
+yolov5==7.0.8
+sahi==0.11.11