I'm trying to make a simple binary image classification with TensorFlow, but the results are just all over the place.
The classifier is supposed to check whether my gate is open or closed. I already have some python scripts to rotate and crop the images to eliminate the surroundings, with an image size of 130w*705h.
Images are below. I know I must be doing something totally wrong, because the images are almost night and day of a difference, yet it still gives completely random results. Any tips? Is there a simpler library or maybe a cloud service I could use for this if TF is too complicated?
Any help is appreciated, thanks!
Just compute the average grey value of your images and define a threshold. If you want something more sophisticated compute average gradients or something like that. Your problem seems far too simple to use TF or CV.
After taking into consideration Martin's Answer , I decided to go with average grays after some filtering and edge detection.
I think it will work great for my case, thanks!
Some code:
import cv2
import os
import numpy as np
# https://medium.com/sicara/opencv-edge-detection-tutorial-7c3303f10788
inputPath = '/Users/axelsariel/Desktop/GateImages/Cropped/'
# subDir = 'Closed/'
subDir = 'Open/'
openImagesList = os.listdir(inputPath + subDir)
for image in openImagesList:
if not image.endswith('.JPG'):
openImagesList.remove(image)
index = 0
while True:
image = openImagesList[index]
img = cv2.imread(inputPath + subDir + image)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = cv2.medianBlur(gray,11)
grayFiltered = cv2.bilateralFilter(gray, 7, 50, 50)
edgesFiltered = cv2.Canny(grayFiltered, 80, 160)
images = np.hstack((gray, grayFiltered, edgesFiltered))
cv2.imshow(image, images)
key = cv2.waitKey()
if key == 3:
index += 1
elif key == 2:
index -= 1
elif key == ord('q'):
break
cv2.destroyAllWindows()
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