Reputation: 33
I am writing an image recognition program in python 3.6 for which I am using anaconda.I have my image data set stored in the location E:\food-101\images in which the 'images' folder contains multiple sub folders which contain the photographs. I want to use these images for training my machine learning model.I am able to load(read) a single image stored in E:\ I want to load multiple images from the above path how do I proceed ?I am using opencv. my code is as follows any help is appreciated
import cv2
import matplotlib
import numpy
img = cv2.imread("E:\food\images\chicken_wings\a.jpg",cv2.IMREAD_GRAYSCALE)
cv2.imshow('image',img)
cv2.waitKey(0)
cv2.destroyAllWindows()
I get the following error
Traceback (most recent call last):
File "<ipython-input-8-6310b5f3028e>", line 5, in <module>
cv2.imshow('image',img)
error: OpenCV(3.4.1) C:\bld\opencv_1520732670222\work\opencv-3.4.1\modules\highgui\src\window.cpp:356: error: (-215) size.width>0 && size.height>0 in function cv::imshow
Traceback (most recent call last):
File "<ipython-input-8-6310b5f3028e>", line 5, in <module>
cv2.imshow('image',img)
error: OpenCV(3.4.1) C:\bld\opencv_1520732670222\work\opencv-3.4.1\modules\highgui\src\window.cpp:356: error: (-215) size.width>0 && size.height>0 in function cv::imshow
Upvotes: 2
Views: 19833
Reputation: 2307
An easy way would be to install Glob. You can do this from the anaconda prompt with pip install glob
.
Glob allows you to query files like you would in a terminal. Once you have Glob you could save all of the file names to a list and then loop through this list reading your images (NumPy arrays) into a new list.
import cv2
import numpy
import glob
folders = glob.glob('path\\to\\folder\\containing\\folders\\*')
imagenames_list = []
for folder in folders:
for f in glob.glob(folder+'/*.jpg'):
imagenames_list.append(f)
read_images = []
for image in imagenames_list:
read_images.append(cv2.imread(image, cv2.IMREAD_GRAYSCALE))
You could then access an image by indexing it i.e.
plt.imshow(read_images[0])
Upvotes: 6