Mask_detection / app.py
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import gradio as gr
import torch
import yolov5
import subprocess
import tempfile
import time
from pathlib import Path
import uuid
import cv2
import gradio as gr
# 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 image_fn(
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,
):
"""
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 = yolov5.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]
image_interface = gr.Interface(
fn=image_fn,
inputs=[
gr.inputs.Image(type="pil", label="Input Image"),
gr.inputs.Dropdown(
choices=[
"alshimaa/yolo5_epoch100",
#"kadirnar/yolov7-v0.1",
],
default="alshimaa/yolo5_epoch100",
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="Object Detector: Identify People Without Mask",
examples=[['img1.png', 'alshimaa/yolo5_epoch100', 640, 0.25, 0.45], ['img2.png', 'alshimaa/yolo5_epoch100', 640, 0.25, 0.45], ['img3.png', 'alshimaa/yolo5_epoch100', 640, 0.25, 0.45]],
cache_examples=True,
theme='huggingface',
)
if __name__ == "__main__":
gr.TabbedInterface(
[image_interface],
["Detect Images"],
).launch()