I have a folder having bunch of images, out of which few images are almost dark like this: Dark Images, and few images are good images like this: Good images
Basically i am able to identify the darker images by using the below code, for the darker images the np.mean(image) comes below 0.1, and for good images it comes above 0.1 range:
from skimage import io, img_as_float
import numpy as np
image = io.imread('C:/Data/Testing/Image_0_5.jpg')
image = img_as_float(image)
print(np.mean(image))
But what i want is to pass the specific folder having all the images so that my code can parse through all the images, and list down the images having dark images. Need help on this.
Thanks for the direction guys, appreciated.
Here's my code:
import matplotlib.image as mpimg
import os
def load_images(folder):
images = []
for filename in os.listdir(folder):
img = mpimg.imread(os.path.join(folder, filename))
img = img_as_float(img)
#print(np.mean(img))
if img is not None:
images.append(img)
if(np.mean(img) < 0.1):
print filename
load_images('C:/Data/Testing')
I have achieved what i was looking for :)
Related
I'm trying to convert multiple JPG files into PNG files. I'm able to do it for a single file but the loop doesn't seem to work for multiple files. Could you please help with that? I'm sharing my code below:
from PIL import Image
img = Image.open('./image.jpg')
img.save('new_image.png','png')
print('All done!')
You can try this -
from PIL import Image
import glob
counter = 0
for image in glob.glob("./*.jpg"):
counter = counter + 1
img = Image.open(image)
img.save(str(counter)+'new_image.png','png')
#So the following code worked. Sorry for the formatting, I'm just beginning to learn!
from PIL import Image
import glob
import os
directory = r'C:\Users\Umar Iqbal\Desktop\newfolder' #this is where we will save our converted images
for image in glob.glob('./*.jpg'):
img = Image.open(image)
clean_name = os.path.splitext(image)[0] #if we don't use this, we get jpg in the file's name
img.save(f'{directory}{clean_name}.png', 'png') #this allows us to save the new images in the directory
I have read mountains of posts on pytesseract, but I cannot get it to read text off a dead simple image; It returns an empty string.
Here is the image:
I have tried scaling it, grayscaling it, and adjusting the contrast, thresholding, blurring, everything it says in other posts, but my problem is that I don't know what the OCR wants to work better. Does it want blurry text? High contrast?
Code to try:
import pytesseract
from PIL import Image
print pytesseract.image_to_string(Image.open(IMAGE FILE))
As you can see in my code, the image is stored locally on my computer, hence Image.open()
Trying something along the lines of
import pytesseract
from PIL import Image
import requests
import io
response = requests.get('https://i.stack.imgur.com/J2ojU.png')
img = Image.open(io.BytesIO(response.content))
text = pytesseract.image_to_string(img, lang='eng', config='--psm 7')
print(text)
with --psm values equal or larger than 6 did yield "Gm" for me.
If the image is stored locally (and in your working directory), just drop the response variable and change the definition of text with the lines
image_name = "J2ojU.png" # or whatever appropriate
text = pytesseract.image_to_string(Image.open(image_name), lang='eng', config='--psm 7')
There are several reasons:
Edges are not sharp and continuous (By sharp I mean smooth, not with teeth)
Image is too small, you need to resize
Font is missing (not mandatory, but trained font incredibly improve possibility of recognition)
Based on points 1) and 2) I was able to recognize text.
1) I resized image 3x and 2) I blurred the image to make edges smooth
import pytesseract
import cv2
import numpy as np
import urllib
import requests
pytesseract.pytesseract.tesseract_cmd = 'C:/Program Files (x86)/Tesseract-OCR/tesseract'
from PIL import Image
def url_to_image(url):
resp = urllib.request.urlopen(url)
image = np.asarray(bytearray(resp.read()), dtype="uint8")
image = cv2.imdecode(image, cv2.IMREAD_COLOR)
return image
url = 'https://i.stack.imgur.com/J2ojU.png'
img = url_to_image(url)
retval, img = cv2.threshold(img,200,255, cv2.THRESH_BINARY)
img = cv2.resize(img,(0,0),fx=3,fy=3)
img = cv2.GaussianBlur(img,(11,11),0)
img = cv2.medianBlur(img,9)
cv2.imshow('asd',img)
cv2.waitKey(0)
cv2.destroyAllWindows()
txt = pytesseract.image_to_string(img)
print('recognition:', txt)
>> recognition: Gm
Note:
This script is good for testing any image on web
Note 2:
All processing is based on your posted image
Note 3:
Text recognition is not easy. Every recognition requires special processing. If you try this steps with different image, it may not work at all. Important is to try a lot of recognition on images so you understand what tesseract wants
I have a data set of images in an image processing project. I want to input an image and scan through the data set to recognize the given image. What module/ library/ approach( eg: ML) should I use to identify my image in my python- opencv code?
To find exactly the same image, you don't need any kind of ML. The image is just an array of pixels, so you can check if the array of the input image equals that of an image in your dataset.
import glob
import cv2
import numpy as np
# Read in source image (the one you want to match to others in the dataset)
source = cv2.imread('test.jpg')
# Make a list of all the images in the dataset (I assume they are images in a directory)
filelist = glob.glob(r'C:\Users\...\Images\*.JPG')
# Loop through the images, read them in and check if an image is equal to your source
for file in filelist:
img = cv2.imread(file)
if np.array_equal(source, img):
print("%s is the same image as source" %(file))
break
I'm using numpy and matplotlib to read all the images in the folder for image processing techniques. Although, I have done the part of reading image dataset from folders and process it with numpy array. But the problem, I'm facing is of showing all the images with matplotlib.imshow function. Everytime I want to show all the images with imshow function, unfortunately it just give me first image nothing else.
My code is below:
import os
import numpy as np
import matplotlib.pyplot as mpplot
import matplotlib.image as mpimg
images = []
path = "../path/to/folder"
for root, _, files in os.walk(path):
current_directory_path = os.path.abspath(root)
for f in files:
name, ext = os.path.splitext(f)
if ext == ".jpg":
current_image_path = os.path.join(current_directory_path,f)
current_image = mpimg.imread(current_image_path)
images.append(current_image)
for img in images:
print len(img.shape)
i = 0
for i in range(len(img.shape)):
mpplot.imshow(img)
mpplot.show()
I will be thankful if somebody can help me in this.
P.S. I'm pretty new with python, numpy and also at stackoverflow. So, please don't mind if the question is unclear or not direct.
Thanks,
About showing only one plot in one moment: please get familiar with matplotlib subplots.
Also, what is your problem that you are not iterating over images. You are calling img x-times.
Try to iterate over images as below:
for img in images:
mpplot.imshow(img)
mpplot.show()
I think what you need to add is mpplot.figure() before each mpplot.show(), this will open a new window for each image.
When loading a png image with PIL and OpenCV, there is a color shift. Black and white remain the same, but brown gets changed to blue.
I can't post the image because this site does not allow newbies to post images.
The code is written as below rather than use cv.LoadImageM, because in the real case the raw image is received over tcp.
Here is the code:
#! /usr/bin/env python
import sys
import cv
import cv2
import numpy as np
import Image
from cStringIO import StringIO
if __name__ == "__main__":
# load raw image from file
f = open('frame_in.png', "rb")
rawImage = f.read()
f.close()
#convert to mat
pilImage = Image.open(StringIO(rawImage));
npImage = np.array(pilImage)
cvImage = cv.fromarray(npImage)
#show it
cv.NamedWindow('display')
cv.MoveWindow('display', 10, 10)
cv.ShowImage('display', cvImage)
cv. WaitKey(0)
cv.SaveImage('frame_out.png', cvImage)
How can the color shift be fixed?
OpenCV's images have color channels arranged in the order BGR whereas PIL's is RGB. You will need to switch the channels like so:
import PIL.Image
import cv2
...
image = np.array(pilImage) # Convert PIL Image to numpy/OpenCV image representation
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) # You can use cv2.COLOR_RGBA2BGRA if you are sure you have an alpha channel. You will only have alpha channel if your image format supports transparency.
...
#Krish: Thanks for pointing out the bug. I didn't have time to test the code the last time.
Hope this helps.
Change
pilImage = Image.open(StringIO(rawImage))
to
pilImage = Image.open(StringIO(rawImage)).convert("RGB")
Light alchemist's answer did not work, but it did explain the issue. Wouldn't the reverse be screwed up by the Apha channel, i.e. it changes BRGA to AGRB. I would think Froyo's answer would solve it, but it did not change the displayed image at all. What did work was reversing the colors in OpenCV. I'm too much of a newbie to know why. They seem equivalent to me. Reversing the colors in numpy would be preferred as additional processing is planned in numpy. But thanks for the help, the answers steered me in the right direction.
pilImage = Image.open(StringIO(rawImage));
bgrImage = np.array(pilImage)
cvBgrImage = cv.fromarray(bgrImage)
# Reverse BGR
cvRgbImage = cv.CreateImage(cv.GetSize(cvBgrImage),8,3)
cv.CvtColor(cvBgrImage, cvRgbImage, cv.CV_BGR2RGB)
#show it
cv.ShowImage('display', cvRgbImage)
cv. WaitKey(30) # ms to allow display