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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",
)
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)
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