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import gradio as gr
import torch
from sahi.prediction import ObjectPrediction
from sahi.utils.cv import visualize_object_predictions, read_image
# from ultralyticsplus import YOLO
from ultralytics import YOLO
# Images
try:
torch.hub.download_url_to_file("https://image.jimcdn.com/app/cms/image/transf/none/path/sb7e051baffe289da/image/i98db96643a3b080e/version/1416825261/image.jpg", "mg.jpg")
except:
torch.hub.download_url_to_file('https://raw.githubusercontent.com/obss/sahi/main/tests/data/small-vehicles1.jpeg', 'mg.jpg')
# torch.hub.download_url_to_file("https://ikiwiki.iki.fi/_media/jot-email-1612-fi-iki.png", "fi.jpg")
# torch.hub.download_url_to_file("https://www.geekculture.com/joyoftech/joyimages/1612.gif", "en.jpg")
# torch.hub.download_url_to_file('https://user-images.githubusercontent.com/34196005/142742872-1fefcc4d-d7e6-4c43-bbb7-6b5982f7e4ba.jpg', 'highway1.jpg')
# torch.hub.download_url_to_file('https://raw.githubusercontent.com/obss/sahi/main/tests/data/small-vehicles1.jpeg', 'small-vehicles1.jpeg')
def yolov8_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,
):
"""
YOLOv8 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 = YOLO(""+model_path+"/train/weights/best.onnx", task="detect")
model = YOLO("https://huggingface.co/"+model_path+"/resolve/main/train/weights/best.onnx", task="detect")
model.conf = conf_threshold
model.iou = iou_threshold
# results = model.predict(image, imgsz=image_size, return_outputs=True)
results = model.predict(image)
object_prediction_list = []
print("*", len(results))
for _box in results:
for box in _box:
xyxy = [int(x) for x in box.boxes.xyxy[0]]
conf = float(box.boxes.conf[0])
cls = int(box.boxes.cls[0])
label = box.names[cls]
#label = list(map(lambda x: box.names[int(x)], cls))
#for xyxy, conf, cls, label in zip(xyxy,conf,cls,label):
object_prediction_list.append(
ObjectPrediction(
bbox=xyxy,
category_id=cls,
score=conf,
category_name=label,
)
)
print(object_prediction_list)
# for _, image_results in enumerate(results):
# if len(image_results)!=0:
# image_predictions_in_xyxy_format = image_results['det']
# for pred in image_predictions_in_xyxy_format:
# x1, y1, x2, y2 = (
# int(pred[0]),
# int(pred[1]),
# int(pred[2]),
# int(pred[3]),
# )
# bbox = [x1, y1, x2, y2]
# score = pred[4]
# category_name = model.model.names[int(pred[5])]
# category_id = pred[5]
# object_prediction = ObjectPrediction(
# bbox=bbox,
# category_id=int(category_id),
# score=score,
# category_name=category_name,
# )
# object_prediction_list.append(object_prediction)
image = read_image(image)
output_image = visualize_object_predictions(image=image, object_prediction_list=object_prediction_list)
return output_image['image']
inputs = [
gr.inputs.Image(type="filepath", label="Input Image"),
# gr.inputs.Dropdown(["kadirnar/yolov8n-v8.0", "kadirnar/yolov8m-v8.0", "kadirnar/yolov8l-v8.0", "kadirnar/yolov8x-v8.0", "kadirnar/yolov8x6-v8.0"],
# default="kadirnar/yolov8m-v8.0", label="Model"),
# gr.inputs.Dropdown(["jongkook90/yolov8_comicbook"], default="jongkook90/yolov8_comicbook", label="Model"),
gr.inputs.Dropdown(["jongkook90/yolov8_comicbook"], default="jongkook90/yolov8_comicbook", 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 = "Ultralytics YOLOv8: State-of-the-Art YOLO Models"
examples = [
['mg.jpg', 'jongkook90/yolov8_comicbook', 640, 0.25, 0.45],
#['fi.jpg', 'jongkook90/yolov8_comicbook', 640, 0.25, 0.45],
#['en.jpg', 'jongkook90/yolov8_comicbook', 640, 0.25, 0.45],
]
demo_app = gr.Interface(
fn=yolov8_inference,
inputs=inputs,
outputs=outputs,
title=title,
examples=examples,
cache_examples=True,
theme='huggingface',
)
demo_app.launch(debug=True, enable_queue=True)
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