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import gradio as gr
import torch
import json
#import yolov7
import yolov7detect.helpers as yolov7d
# 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')
model_path = "kadirnar/yolov7-v0.1" #"kadirnar/yolov7-tiny-v0.1"
image_size = 640
conf_threshold = 0.25
iou_threshold = 0.45
def yolov7_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,
):
"""
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 = yolov7d.load_model(model_path, device="cpu", hf_model=True, trace=False)
model.conf = conf_threshold
model.iou = iou_threshold
results = model([image], size=image_size)
tensor = {
"tensorflow": [
]
}
if results.pred is not None:
for i, element in enumerate(results.pred[0]):
object = {}
#print (element[0])
itemclass = round(element[5].item())
object["classe"] = itemclass
object["nome"] = results.names[itemclass]
object["score"] = element[4].item()
object["x"] = element[0].item()
object["y"] = element[1].item()
object["w"] = element[2].item()
object["h"] = element[3].item()
tensor["tensorflow"].append(object)
text = json.dumps(tensor)
#print (text)
return text #results.render()[0]
inputs = [
gr.inputs.Image(type="pil", label="Input Image"),
]
#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 = [['small-vehicles1.jpeg'], ['zidane.jpg']]
demo_app = gr.Interface(
fn=yolov7_inference,
inputs=inputs,
outputs=["text"],
title=title,
examples=examples,
#cache_examples=True,
#theme='huggingface',
)
demo_app.launch(debug=True, server_name="192.168.0.153", server_port=8081, enable_queue=True)
#demo_app.launch(debug=True, server_port=8083, enable_queue=True)
#demo_app.launch(debug=True, enable_queue=True)
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