not able to load the image and pass it to preprocessing for the model prediction - streamlit

Question

I am trying to upload the image from the local system within the same directory. Post uploading, when I am passing through open cv split and merge for b,g, and r colors, i get the error ValueError: not enough values to unpack (expected 3, got 0)
Error :
this is the error that is showing is there any possibility to debug in the streamlit where I can track changes at different lines of code? (As in the image path,) when executed in a google collab as individual ipynb files run properly and I get by required classification
ValueError: not enough values to unpack (expected 3, got 0)
Traceback:
File "C:\Users\ADARSH\anaconda3\lib\site-packages\streamlit\runtime\scriptrunner\script_runner.py", line 564, in _run_script
exec(code, module.__dict__)
File "C:\Users\ADARSH\streamlit\deploy_test.py", line 76, in <module>
main()
File "C:\Users\ADARSH\streamlit\deploy_test.py", line 68, in main
mask = imageToTensor('image')
File "C:\Users\ADARSH\streamlit\deploy_test.py", line 44, in imageToTensor
b,g,r = cv2.split(bgr_img)
My entire streamlit app code
from pathlib import Path
import cv2
import numpy as np
import pandas as pd
import os
import cv2
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import random
from sklearn.utils import shuffle
from tqdm import tqdm_notebook
import streamlit as st
from PIL import Image as impo
from fastai import *
from fastai.vision import *
from torchvision.models import *
class MyImageItemList(ImageList):
def open(self, fn:PathOrStr)->Image:
img = readCroppedImage(fn.replace('/./','').replace('//','/'))
# This ndarray image has to be converted to tensor before passing on as fastai Image, we can use pil2tensor
return vision.Image(px=pil2tensor(img, np.float32))
def read_image(name):
image = st.file_uploader("Upload an "+ name, type=["png", "jpg", "jpeg",'tif'])
if image is not None:
im = impo.open(image)
im.filename = image.name
return image
def imageToTensor(image):
sz = 68
bgr_img = cv2.imread(image)
b,g,r = cv2.split(bgr_img)
rgb_img = cv2.merge([r,g,b])
# crop to center to the correct size and convert from 0-255 range to 0-1 range
H,W,C = rgb_img.shape
rgb_img = rgb_img[(H-sz)//2:(sz +(H-sz)//2),(H-sz)//2:(sz +(H-sz)//2),:] / 256
return vision.Image(px=pil2tensor(rgb_img, np.float32))
def learn_infernce():
return load_learner('./')
def get_prediction(image):
if st.button('Classify'):
pred, pred_idx, probs = learn_inference.predict(image)
classes = ['negative', 'tumor']
st.write(f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}')
else:
st.write(f'Click the button to classify')
def main():
st.set_page_config(page_title='Cancer detection', page_icon=None, layout='centered', initial_sidebar_state='auto')
image = read_image('image')
mask = imageToTensor('image')
if mask is not None:
get_prediction('mask')
if __name__ == "__main__":
main()

Answer 1

In your main function you are passing 'str' instead of variables, and also I think your read_image is not well structured.
What you should do is to first save the uploaded file in a directory and fetch the file from that directory and pass it to imageToTensor() as a parameter. That's one work around which will give you a total control over the file. Otherwise you will get other error messages after the first error is fixed.
You can automate some few lines of code in a separate python file to delete the uploaded file from the directory with a given duration.
Note: Keep an eye on the imports because I skipped them to keep the code short
class MyImageItemList(ImageList):
def open(self, fn:PathOrStr)->Image:
img = readCroppedImage(fn.replace('/./','').replace('//','/'))
# This ndarray image has to be converted to tensor before passing on as fastai Image, we can use pil2tensor
return vision.Image(px=pil2tensor(img, np.float32))
# Refactured read_image()
def get_uploaded_image():
upload = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg",'tif'])
if upload is not None:
st.write(upload.name)
# Create a directory and save the image file before proceeding.
file_path = os.path.join("data/uploadedImages/", upload.name)
with open(file_path, "wb") as user_file:
user_file.write(upload.getbuffer())
return file_path # fixed indentation
def imageToTensor(image):
sz = 68
bgr_img = cv2.imread(image)
b,g,r = cv2.split(bgr_img)
rgb_img = cv2.merge([r,g,b])
# crop to center to the correct size and convert from 0-255 range to 0-1 range
H,W,C = rgb_img.shape
rgb_img = rgb_img[(H-sz)//2:(sz +(H-sz)//2),(H-sz)//2:(sz +(H-sz)//2),:] / 256
return vision.Image(px=pil2tensor(rgb_img, np.float32))
def learn_infernce():
return load_learner('./')
def get_prediction(image):
if st.button('Classify'):
pred, pred_idx, probs = learn_inference.predict(image)
classes = ['negative', 'tumor']
st.write(f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}')
else:
st.write(f'Click the button to classify')
def main():
st.set_page_config(page_title='Cancer detection', page_icon=None, layout='centered', initial_sidebar_state='auto')
# Holds the saved file path
user_image = get_uploaded_image()
if user_image is not None:
# Pass the path to imageToTensor() as a parameter.
mask = imageToTensor(user_image)
get_prediction(mask)
if __name__ == "__main__":
main()
output: