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import gradio as gr
#import torch
import yolov7
#
# from huggingface_hub import hf_hub_download
from huggingface_hub import hfapi
# 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 yolov7_inference(
image: gr.inputs.Image = None,
model_path: gr.inputs.Dropdown = hfapi(repo_id=f"alshimaa/model_baseline", filename=f"best_baseline.pt"),
image_size: gr.inputs.Slider = 640,
conf_threshold: gr.inputs.Slider = 0.25,
iou_threshold: gr.inputs.Slider = 0.45,
):
"""
YOLOv7 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 = yolov7.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]
inputs = [
gr.inputs.Image(type="pil", label="Input Image"),
gr.inputs.Dropdown(
choices=[
"kadirnar/yolov7-tiny-v0.1",
"kadirnar/yolov7-v0.1",
],
default="kadirnar/yolov7-tiny-v0.1",
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 = "Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors"
#examples = [['image.jpg.', 'SEE/best_baseline', 640, 0.25, 0.45], ['image.jpg', 'SEE/best_baseline', 640, 0.25, 0.45]]
demo_app = gr.Interface(
fn=yolov7_inference,
inputs=inputs,
outputs=outputs,
title=title,
cache_examples=True,
theme='huggingface',
)
demo_app.launch(debug=True, enable_queue=True)
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