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Preprocessing images for QR detection in python

I used Zbar and OpenCV to read the QR code in the image below but both failed to detect it. For ZBar, I use pyzbar library as the python wrapper. There are images that QR is detected correctly and images really similar to the successful ones that fail. My phone camera can read the QR code in the uploaded image which means it is a valid one. Below is the code snippet:
from pyzbar.pyzbar import decode
from pyzbar.pyzbar import ZBarSymbol
import cv2
# zbar
results = decode(cv2.imread(image_path), symbols=[ZBarSymbol.QRCODE])
print(results)
# opencv
qr_decoder = cv2.QRCodeDetector()
data, bbox, rectified_image = qr_decoder.detectAndDecode(cv2.imread(image_path))
print(data, bbox)
What type of pre-processing will help to increase the rate of success for detecting QR codes?

zbar, which does some preprocessing, does not detect the QR code, which you can test running zbarimg image.jpg.
Good binarization is useful here. I got this to work using the kraken.binarization.nlbin() function of the Kraken library. The library is for OCR, but works very well for QR codes, too, by using non-linear processing. The Kraken binarization code is here.
Here is the code for the sample:
from kraken import binarization
from PIL import Image
from pyzbar.pyzbar import decode
from pyzbar.pyzbar import ZBarSymbol
image_path = "image.jpg"
# binarization using kraken
im = Image.open(image_path)
bw_im = binarization.nlbin(im)
# zbar
decode(bw_im, symbols=[ZBarSymbol.QRCODE])
[Decoded(data=b'DE-AAA002065', type='QRCODE', rect=Rect(left=1429, top=361, width=300, height=306), polygon=[Point(x=1429, y=361), Point(x=1429, y=667), Point(x=1729, y=667), Point(x=1723, y=365)])]
The following picture shows the clear image of the QR code after binarization:

I had a similar issue, and Seanpue's answer got me on the right track for this problem. Since I was already using the OpenCV library for image processing rather than PIL, I used it to perform Otsu's Binarization using the directions in an OpenCV tutorial on Image Thresholding. Here's my code:
import cv2
from pyzbar.pyzbar import decode
from pyzbar.pyzbar import ZBarSymbol
image_path = "qr.jpg"
# preprocessing using opencv
im = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
blur = cv2.GaussianBlur(im, (5, 5), 0)
ret, bw_im = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
# zbar
decode(bw_im, symbols=[ZBarSymbol.QRCODE])
[Decoded(data=b'DE-AAA002065', type='QRCODE', rect=Rect(left=1429, top=362, width=300, height=305), polygon=[Point(x=1429, y=362), Point(x=1430, y=667), Point(x=1729, y=667), Point(x=1724, y=366)])]
Applying the gaussian blur is supposed to remove noise from the picture to make the binarization more effective, but for my application it didn't actually make much difference. What was vital was to convert the image to grayscale to make the threshold function work (done here by opening the file with the cv2.IMREAD_GRAYSCALE flag).

QReader use to work quite well for these cases.
from qreader import QReader
import cv2
if __name__ == '__main__':
# Initialize QReader
detector = QReader()
img = cv2.cvtColor(cv2.imread('92iKG.jpg'), cv2.COLOR_BGR2RGB)
# Detect and Decode the QR
print(detector.detect_and_decode(image=img))
This code output for this QR:
DE-AAA002065

Why does tesseract fail to read text off this simple image?

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

Text detection in Image with Pytesseract in Python

I am new to Pytesseract and currently in learning phase.
Here is the image:
from which I am trying to extract the text. The smaller text "movlmento, destmo" that I thought would be difficult to extract was easily extracted but the more bigger and visible text "TO" is not being extracted. I have searched all over but was not able to figure out a solution. Any help would be appreciated. Thank You!
Here is my code:
from PIL import Image
import pytesseract as p
p.pytesseract.tesseract_cmd = r"C:\Program Files (x86)\Tesseract-OCR\tesseract.exe"
img = Image.open('sample.jpg')
img = img.convert('1', dither=Image.NONE)
img2 = p.image_to_string(img)
print(img2)

Skimage SimilarityTransform spoils image

I have a code which recognizes faces on images (dlib implementation, 68 points)
I want to rotate a bit some images but after a get following trouble: my image becomes somehow spoiled
from skimage import io
from skimage import transform as tf
img = io.imread(f)
tform = tf.SimilarityTransform(rotation=np.deg2rad(10),translation=(10,12))
img = tf.warp(img,tform)
I plot image with 2 ways:
plt.imshow(img) #the right picture (matplotlib)
win = dlib.image_window() #the left picture (dlib)
win.set_image(img) #the left picture
As you can see dlib image is broken. Also algorithm which can find facial keypoints stopps working.
Without SimilarityTransform dlib works correctly.
Help me please! I want to rotate an image and to pass it to dlib

I found the solution.
I simply needed to convert image with img_as_ubyte
from skimage import img_as_ubyte
img = img_as_ubyte(tf.warp(img,tform))

PIL to OpenCV MAT causes color shift

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