feat:recover image resolution

app.py

@@ -31,16 +31,16 @@ def predict(numpy_img):
     # #convert output to numpy
     heatmaps_pred_np=heatmaps_pred.squeeze(0).permute(1,2,0).detach().cpu().numpy()
     # #heatmaps to joints location
-    coord_joints=heatmaps_to_coords(heatmaps_pred_np,resolu_out=[256,256],prob_threshold=0.1)
+    coord_joints=heatmaps_to_coords(heatmaps_pred_np,resolu_out=[numpy_img.shape[0],numpy_img.shape[1]],prob_threshold=0.1)
     inference_time=time.time()-start_time
     inference_time_text="model inference time:{:.4f}s".format(inference_time)
     # #draw coords on image_np
-    img_rgb=draw_joints(img_np,coord_joints)
+    img_rgb=draw_joints(numpy_img,coord_joints)
     return img_rgb,inference_time_text
     
     
 
-demo=gr.Interface(fn=predict, inputs=gr.Image(),outputs=[gr.Image(type='numpy',width=256,height=256),"text"],examples=example_list)
+demo=gr.Interface(fn=predict, inputs=gr.Image(),outputs=[gr.Image(type='numpy'),"text"],examples=example_list)
 
 if __name__=="__main__":
     demo.launch(show_api=False)

tool_utils.py

@@ -344,7 +344,7 @@ def heatmaps2rgb(heatmaps):
 #     return img
 def draw_joints(img, pts):
     # Convert the image to the range [0, 255] for visualization
-    img_visualization = (img * 255).astype(np.uint8)
+    img_visualization = (img).astype(np.uint8)
 
     # Draw lines for the body parts
     for i in range(10, 13 - 1):