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
Related
I am just starting out on python and I am attempting to create a code that does real-time OCR on a portion of my screen. I was certain this code would work, but it just throws me a bunch of tesseract errors. Does the image need to be saved for Tesseract to work? Is there a better OCR library for this task? The OpenCV part works perfectly and displays the image.
import numpy as np
import cv2
from PIL import ImageGrab
import pytesseract
while True:
orig_img = ImageGrab.grab(box)
np_im = np.array(orig_img)
img = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY)
text = pytesseract.image_to_string(img)
cv2.imshow('window',img)
if cv2.waitKey(25) & 0xFF == ord('q'):
cv2.destroyAllWindows()
print(text)
I fixed it. I was not aware that I needed to install tesseract in my pc. I also added
im = Image.fromarray(img)
im.save("img.png")
to save the 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
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))
I've been using OpenCV methods to get images from my camera. I'd like to decode QR codes from those images using the zbar library, but after I convert the images to PIL to be processed by zbar, it doesn't seem like the decoding is working.
import cv2.cv as cv
import zbar
from PIL import Image
cv.NamedWindow("camera", 1)
capture = cv.CaptureFromCAM(0)
while True:
img = cv.QueryFrame(capture)
cv.ShowImage("camera", img)
if cv.WaitKey(10) == 27:
break
# create a reader
scanner = zbar.ImageScanner()
# configure the reader
scanner.parse_config('enable')
# obtain image data
pil = Image.fromstring("L", cv.GetSize(img), img.tostring())
width, height = pil.size
raw = pil.tostring()
# wrap image data
image = zbar.Image(width, height, 'Y800', raw)
# scan the image for barcodes
scanner.scan(image)
# extract results
for symbol in image:
# do something useful with results
print 'decoded', symbol.type, 'symbol', '"%s"' % symbol.data
cv.DestroyAllWindows()
You don't need to convert the OpenCV image to a PIL image in order to use it with zbar... you can go straight from an OpenCV image to zbar and avoid using PIL completely.
Now I don't know how you do this when the image source is from a camera, but if you load an image from disk, all you have to do is this:
cv_img = cv.LoadImageM(image, cv.CV_LOAD_IMAGE_GRAYSCALE)
width = cv_img.width
height = cv_img.height
raw = cv_img.tostring()
# wrap image data
image = zbar.Image(width, height, 'Y800', raw)
# scan the image for barcodes
scanner.scan(image)
# extract results
for symbol in image:
# do something useful with results
print 'decoded', symbol.type, 'symbol', '"%s"' % symbol.data
It appears you basically have to do the following for this to work:
Use LoadImageM instead of LoadImage
Make sure the image is grayscale so use cv.CV_LOAD_IMAGE_GRAYSCALE
If you are working in python I suggest you take a look at SimpleCV. You can either crib our implementation of bar code reading or use the library yourself. Here is the source for pulling barcodes out with zbar.
I'm trying to use the Zbar library's QR code detection methods on images I extract with OpenCV's camera methods. Normally the QR code detection methods work with images (jpg, png, etc.) on my computer, but I guess the captured frames of OpenCV are different.
Is there a way of making the captured frame into a PIL Image?
Thank you.
from PIL import Image
import zbar
import cv2.cv as cv
capture = cv.CaptureFromCAM(1)
imgSize = cv.GetSize(cv.QueryFrame(capture))
img = cv.QueryFrame(capture)
#SOMETHING GOES HERE TO TURN FRAME INTO IMAGE
img = img.convert('L')
width, height = img.size
scanner = zbar.ImageScanner()
scanner.parse_config('enable')
zbar_img = zbar.Image(width, height, 'Y800', img.tostring())
# scan the image for barcodes
scanner.scan(zbar_img)
for symbol in zbar_img:
print symbol.data
With the python CV2, you can also do this:
import Image, cv2
cap = cv2.VideoCapture(0) # says we capture an image from a webcam
_,cv2_im = cap.read()
cv2_im = cv2.cvtColor(cv2_im,cv2.COLOR_BGR2RGB)
pil_im = Image.fromarray(cv2_im)
pil_im.show()
I think I may have found the answer. I'll edit later with results.
OpenCV to PIL Image
import Image, cv
cv_im = cv.CreateImage((320,200), cv.IPL_DEPTH_8U, 1)
pi = Image.fromstring("L", cv.GetSize(cv_im), cv_im.tostring())
Source: http://opencv.willowgarage.com/documentation/python/cookbook.html
Are you trying to obtain a RGB image? If that is the case, you need to change your parameters from this:
cv_im = cv.CreateImage((320,200), cv.IPL_DEPTH_8U, 1)
pi = Image.fromstring("L", cv.GetSize(cv_im), cv_im.tostring())
to that:
cv_im = cv.CreateImage((320,200), cv.IPL_DEPTH_8U, 3)
pi = Image.fromstring("RGB", cv.GetSize(cv_im), cv_im.tostring())
since it is documented almost nowhere, but the 'L' parameter of Image.fromstring is for 8-bit B&W images. Besides, you need to change the argument of your cv.CreateImage function from 1 (single channel image) to 3 (3 channels=RGB).
Hope it works for you.
Cheers
A simple way is to directly swap the channels. Suppose you are trying to convert a 3-channel image file between OpenCV format and PIL format. You can just use:
img[...,[0,2]]=img[...,[2,0]]
In this way, you won't be bothered with cv2.cvtColor as this function only works on images with certain depth.