updates

app.py

@@ -10,22 +10,56 @@ from datasets import load_dataset
 
 ds = load_dataset("Circularmachines/batch_indexing_machine_test", split="test")
 
+patch_size=32
+#image_size=2304
+image_size=512
+gridsize=16
+
+def donut(patch_size, img_size, lower_limit=0.40, upper_limit=1):
+  
+  gridsize=img_size//2//patch_size
+
+  #create a grid of patch coordinates relative to center of image, and calculate distance from center
+  coords=np.array([[(i+0.5,j+0.5) for i in range(-gridsize,gridsize)] for j in range(-gridsize,gridsize)])
+  norm=np.linalg.norm(coords,axis=2)
+  
+  #we are only interested in the "donut" where the parts are, anything close to the center and far from the center is disregarded
+  keep_bool=((norm>(gridsize*lower_limit))*(norm<(gridsize*upper_limit)))
+  keep=np.where(keep_bool.flatten())[0]
+
+  return coords,keep,keep_bool
+
+coords,keep,keep_bool=donut(patch_size,image_size)
+coords_valid=coords.reshape(-1,2)[keep]
+n_patches=len(keep)
+
+angle_sort=(-np.arctan2(coords_valid[:,0],coords_valid[:,1])).argsort()
+keep[angle_sort]
+
+keep_i=np.zeros(gridsize**2)
+
+keep_i[keep]=keep
 
+pred=np.load('pred.npy')
+pred_all=np.load('pred_all.npy').reshape(-1,64)
+
+"""
 gridsize=16
 n_patches=164
 
 patch_size=32
 
-pred=np.load('pred.npy')
-pred_all=np.load('pred_all.npy').reshape(-1,64)
+
 keep_bool=np.load('keep_bool.npy')
 
 keep=np.where(keep_bool.flatten())[0]
 
+
+
 keep_i=np.zeros(gridsize**2)
 
 keep_i[keep]=keep
-
+"""
 
 #st.set_page_config(
 #    page_title="Streamlit Image Coordinates: Image Update",