Delete app.py

app.py

@@ -1,143 +0,0 @@
-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",
-)
-sahi.utils.file.download_from_url(
-    "https://user-images.githubusercontent.com/34196005/142730936-1b397756-52e5-43be-a949-42ec0134d5d8.jpg",
-    "highway.jpg",
-)
-
-sahi.utils.file.download_from_url(
-    "https://user-images.githubusercontent.com/34196005/142742871-bf485f84-0355-43a3-be86-96b44e63c3a2.jpg",
-    "highway2.jpg",
-)
-
-sahi.utils.file.download_from_url(
-    "https://user-images.githubusercontent.com/34196005/142742872-1fefcc4d-d7e6-4c43-bbb7-6b5982f7e4ba.jpg",
-    "highway3.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'>SAHI is a lightweight vision library for performing large scale object detection/ instance segmentation.. <a href='https://github.com/obss/sahi'>SAHI Github</a> | <a href='https://medium.com/codable/sahi-a-vision-library-for-performing-sliced-inference-on-large-images-small-objects-c8b086af3b80'>SAHI Blog</a> | <a href='https://github.com/fcakyon/yolov5-pip'>YOLOv5 Github</a> </p>"
-examples = [
-    ["apple_tree.jpg", 256, 256, 0.2, 0.2, "NMS", "IOU", 0.4, True],
-    ["highway.jpg", 256, 256, 0.2, 0.2, "NMS", "IOU", 0.4, True],
-    ["highway2.jpg", 512, 512, 0.2, 0.2, "NMS", "IOU", 0.4, True],
-    ["highway3.jpg", 512, 512, 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)