updates

app.py

@@ -6,54 +6,31 @@ from streamlit_image_coordinates import streamlit_image_coordinates
 
 import numpy as np
 
-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
+from datasets import load_dataset
 
-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_a=keep[angle_sort]
+ds = load_dataset("Circularmachines/batch_indexing_machine_test", split="test")
 
-#keep_i=np.zeros(gridsize**2)
 
-#keep_i[keep]=keep_a
 
-pred=np.load('pred.npy')
-pred_all=np.load('pred_all.npy').reshape(-1,64)
 
-random_i=np.load('random.npy')
 
+patch_size=32
+stride=16
+#image_size=2304
+image_size=512
+gridsize=31
 
-#st.set_page_config(
-#    page_title="Streamlit Image Coordinates: Image Update",
-#    page_icon="🎯",
-#    layout="wide",
-#)
+n_patches=961
 
-#"# :dart: Streamlit Image Coordinates: Image Update"
+pred=np.load('pred.npy')
+pred_all=np.load('pred_all.npy')#.reshape(-1,64)
 
+random_i=np.load('random.npy')
 
 
 if "point" not in st.session_state:
@@ -67,10 +44,10 @@ if "draw" not in st.session_state:
 
 def patch(ij):
   #st.write(ij)
-  immg=ij//(gridsize**2)
-  p=ij%(gridsize**2)
+  immg=ij//n_patches
+  p=ij%n_patches
 
-  imm=ds[int(immg)]['image'].resize(size=(512,512))
+  imm=ds[int(immg)]['image']#.resize(size=(512,512))
 
   y=p//gridsize
   x=p%gridsize
@@ -94,7 +71,7 @@ def find():
   batches=[]
   while ix<4:
 
-    batch=diff.argsort()[i]//(gridsize**2)//20
+    batch=diff.argsort()[i]//n_patches//20
 
     if batch not in batches:
 
@@ -158,7 +135,7 @@ with col1:
   value = streamlit_image_coordinates(current_image, key="pil")
 
   if value is not None:
-      point = value["x"]//patch_size*patch_size, value["y"]//patch_size*patch_size
+      point = value["x"]//32, value["y"]//32
 
       if point != st.session_state["point"]:
           st.session_state["point"]=point