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import gradio as gr
import yolov7
from yolov7.models.common import autoShape
from yolov7.models.experimental import attempt_load
from yolov7.utils.google_utils import attempt_download_from_hub, attempt_download
from yolov7.utils.torch_utils import TracedModel
YOLO_MODEL_FILE_NAME="kadirnar/yolov7-v0.1"
def load_local_model(model_file, autoshape=True, device='cpu', trace=False, size=640, half=False, hf_model=False):
"""
Creates a specified YOLOv7 model
Arguments:
model_path (str): path of the model
device (str): select device that model will be loaded (cpu, cuda)
trace (bool): if True, model will be traced
size (int): size of the input image
half (bool): if True, model will be in half precision
hf_model (bool): if True, model will be loaded from huggingface hub
Returns:
pytorch model
(Adapted from yolov7.hubconf.create)
"""
model = attempt_load(model_file, map_location=device)
if trace:
model = TracedModel(model, device, size)
if autoshape:
model = autoShape(model)
if half:
model.half()
return model
# YOLO_MODEL_FILE_NAME="kadirnar/yolov7-tiny-v0.1"
def yolov7_inference(
image: gr.inputs.Image = None,
image_size: gr.inputs.Slider = 640,
conf_threshold: gr.inputs.Slider = 0.25,
iou_threshold: gr.inputs.Slider = 0.45,
):
model = yolov7.load_model(YOLO_MODEL_FILE_NAME, device="cpu", hf_model=False, 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.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: evaluation yolov7.pt"
examples = [['car.jpeg', 640, 0.5, 0.75],
['horse.jpeg', 640, 0.5, 0.75]]
demo_app = gr.Interface(
fn=yolov7_inference,
inputs=inputs,
outputs=outputs,
title=title,
examples=examples,
cache_examples=True,
)
demo_app.launch(debug=True, enable_queue=True) |