app.py

from datasets.arrow_dataset import InMemoryTable
import streamlit as st
from PIL import Image, ImageDraw

from streamlit_image_coordinates import streamlit_image_coordinates

import numpy as np


from datasets import load_dataset

st.set_page_config(layout="wide")



ds = load_dataset("Circularmachines/batch_indexing_machine_green_test", split="test")





patch_size=32
stride=16
#image_size=2304
image_size=512
gridsize=31

n_patches=961

#pred_dict={'Trained on color images (recommended)': np.load('pred_all_scratch.npy').reshape(-1,64),
#           'Trained on grayscale images': np.load('pred_all_grey.npy').reshape(-1,64)}
pred_dict={ 'Trained on augmented images 230809': np.load('pred_all_green_random.npy').reshape(-1,64),
            'Trained on unaugmented images 230805': np.load('pred_all_scratch.npy').reshape(-1,64)}

random_i=np.load('random.npy')


if "point" not in st.session_state:
  st.session_state["point"] = (128,64)
  st.session_state["model"] = tuple(pred_dict.keys())[0]

if "img" not in st.session_state:
  st.session_state["img"] = 0

if "draw" not in st.session_state:
  st.session_state["draw"] = True




def patch(ij):
  #st.write(ij)
  immg=ij//n_patches
 

  imm=ds[int(immg)]['image'].resize(size=(512,512))

  p=ij%n_patches
  y=p//gridsize
  x=p%gridsize
  imc=imm.crop(((x-1)*stride,(y-1)*stride,(x+3)*stride,(y+3)*stride))

  return imc

def find():
  st.session_state["sideix"] = []
  point=st.session_state["point"]
  point=(point[0]//stride,point[1]//stride)
  #point=point[0]*36+point[1]
  #st.write(point)
  #st.write(pred_all[st.session_state["img"],point[0]*36+point[1]])
  i=st.session_state["img"]
  p=point[1]*gridsize+point[0]
  diff=np.linalg.norm(pred_dict[st.session_state["model"]][np.newaxis,i*n_patches+p,:]-pred_dict[st.session_state["model"]],axis=-1)
  #re_pred=pred_all.reshape(20,20,256,64)
  #diff_re=diff.reshape((20,20,256)).argmin(axis=[])
  i=0
  ix=0
  batches=[]
  while ix<4:

    batch=diff.argsort()[i]//n_patches//20

    if batch not in batches:

      batches.append(batch)

      st.session_state["sideimg"][ix]=patch(diff.argsort()[i])
      ix+=1
    
    i+=1
  
  st.session_state["sideix"]=batches


def button_click():
    st.session_state["img"]=np.random.randint(100)
    st.session_state["draw"] = False

if "sideimg" not in st.session_state:
  st.session_state["sideimg"] = [patch(i) for i in range(4)]

if "sideix" not in st.session_state:
  find()

def get_ellipse_coords(point):# tuple[int, int]) -> tuple[int, int, int, int]):
    center = point
    #patch_size
    return (
        center[0] ,
        center[1] ,
        center[0] + patch_size,
        center[1] + patch_size,
    )




col1, col2, col3= st.columns([3,1,1])

with col1:



  current_image=ds[st.session_state["img"]]['image'].resize(size=(512,512))
  draw = ImageDraw.Draw(current_image)

  if st.session_state["draw"]:

  # Draw an ellipse at each coordinate in points
    #for point in st.session_state["points"]:
    point=st.session_state["point"]
    coords = get_ellipse_coords(point)
    draw.rectangle(coords, outline="green",width=2)

  value = streamlit_image_coordinates(current_image, key="pil")

  if value is not None:
      point = (value["x"]-8)//stride*stride, (value["y"]-8)//stride*stride

      if point != st.session_state["point"]:
          st.session_state["point"]=point
          st.session_state["draw"]=True
          st.experimental_rerun()

  #subcol1, subcol2 = st.columns(2)
  #with subcol1:
  #st.button('Previous Frame', on_click=button_click)
  scol1, scol2 = st.columns(2)
  with scol1:
    st.button('Change Image', on_click=button_click)

    st.selectbox("Model",tuple(pred_dict.keys()),key="model")


  with scol2: 
    st.button('Find similar parts', on_click=find)

  #st.write("Currently viewing frame "+str(random_i[st.session_state["img"]%20])+" in batch "+str(st.session_state["img"]//20))

  #st.write(st.session_state["img"])
  #st.write(st.session_state["point"])
  #st.write(st.session_state["draw"])

with col2:
 # st.write("current selection:")
  for i in [0,2]:
    
    if i==0:
      st.write("Target in batch "+str(st.session_state["sideix"][i]))#//(gridsize**2)//20))
    else:
      st.write("Match #"+str(i)+" in batch "+str(st.session_state["sideix"][i]))#//(gridsize**2)//20))
   
    st.image(st.session_state["sideimg"][i].resize((192,192)))


with col3:
 # st.write("current selection:")
  for i in [1,3]:

    st.write("Match #"+str(i)+" in batch "+str(st.session_state["sideix"][i]))#//(gridsize**2)//20))
   
    st.image(st.session_state["sideimg"][i].resize((192,192)))
    
    #st.write(st.session_state["sideix"][i])
    
    #st.write(st.session_state["sideix"][i])

"The batch indexing machine shakes parts while recording a video."
"The machine processed 20 batches of random parts, with each batch running for 30 seconds."

"INSCTRUCTIONS:"
"Click in the image to set target part"
"Click “Find similar parts” to find the best matches in other batches"

"The model is trained completely unsupervised using a CNN with a custom contrastive loss."

"https://github.com/circularmachines/batch_indexing_machine/"

"johan.lagerloef@gmail.com"