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import gradio as gr
import cv2
def predict_fn(img_path):
#Read and tranform input image
# preds=model.predict(img_path).json()
og_img=cv2.imread(img_path)
# img=binary_cv2(og_img,preds)
# outputs = kp_predictor(img) # format is documented at https://detectron2.readthedocs.io/tutorials/models.html#model-output-format
# print("outputs==".format(outputs["instances"].to("cpu")))
# p=np.asarray(outputs["instances"].to("cpu").pred_keypoints, dtype='float32')[0].reshape(-1)
# pairss=convert_to_pairs(p)
# segm=segm_imf(preds,og_img)
# output=visualize(segm,pairss)
return og_img
inputs_image = [
gr.components.Image(type="filepath", label="Upload an XRay Image of the Pelvis"),
]
outputs_image = [
gr.components.Image(type="numpy", label="Output Image")
]
gr.Interface(
predict_fn,
inputs=inputs_image,
outputs=outputs_image,
title="Coordinates of the Landmarks",
_api_mode=True
).launch()
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