I'am trying to write a script that gives me the dimension of the Cover art in a mp3 file, the furthest I've is via Mutgen doing:
audiofile = mutagen.File(wavefile, easy=False)
print(audiofile.tags)
but then from that raw output how can I extract the dimensions like 400x400
You can use stagger and PIL, i.e.:
import stagger, io, traceback
from PIL import Image
try:
mp3 = stagger.read_tag('Menuetto.mp3')
im = Image.open(io.BytesIO(mp3[stagger.id3.APIC][0].data))
print(im.size)
# (300, 300)
# im.save("cover.jpg") # save cover to file
except:
pass
print(traceback.format_exc())
Related
So I'm trying to decode a QR code image using code from this S.O. answer. Here's the adapted code:
import cv2
# Name of the QR Code Image file
filename = r"C:\temp\2021-12-14_162414.png"
# read the QRCODE image
image = cv2.imread(filename)
# initialize the cv2 QRCode detector
detector = cv2.QRCodeDetector()
# detect and decode
data, vertices_array, binary_qrcode = detector.detectAndDecode(image)
# if there is a QR code
# print the data
if vertices_array is not None:
print("QRCode data:")
print(data)
else:
print("There was some error")
(This is the whole program; I was still experimenting.)
The PNG file itself is really small, just 43 KB in size, with resolution of 290x290 (24 bpp) containing just the QR Code.
However, I keep getting the error:
Traceback (most recent call last):
File "C:/Repos/tesqr/decod-cv2.py", line 10, in <module>
data, vertices_array, binary_qrcode = detector.detectAndDecode(image)
cv2.error: OpenCV(4.5.4) D:\a\opencv-python\opencv-python\opencv\modules\core\src\alloc.cpp:73: error: (-4:Insufficient memory) Failed to allocate 54056250000 bytes in function 'cv::OutOfMemoryError'
Why is alloc.cpp asking for 54 GB of RAM ???
I'm new with OpenCV, so please help me troubleshoot what went wrong.
The library I'm using is:
$ pip3 freeze | grep opencv
opencv-contrib-python-headless==4.5.4.60
the input image:
Short Answer:
Try WeChatQRCode
Long Answer:
There are several open memory issues about decoding with QRCodeDetector. I hope that in future versions it will be fixed. Meanwhile you can try WeChatQRCode also from cv2.
WeChatQRCode includes two CNN-based models: A object detection model and a super resolution model. Object detection model is applied to detect QRCode with the bounding box. super resolution model is applied to zoom in QRCode when it is small.
Your code modified:
import cv2
# Name of the QR Code Image file
filename = "2021-12-14_162414.png"
# read the QRCODE image
image = cv2.imread(filename)
# initialize the cv2 QRCode detector
detector =cv2.wechat_qrcode_WeChatQRCode(detector_prototxt_path = "detect.prototxt", detector_caffe_model_path = "detect.caffemodel", super_resolution_prototxt_path = "sr.prototxt", super_resolution_caffe_model_path = "sr.caffemodel")
# detect and decode
data, vertices_array = detector.detectAndDecode(image)
# if there is a QR code
# print the data
if vertices_array is not None:
print("QRCode data:")
print(data)
else:
print("There was some error")
Output:
QRCode data:
('PK\x03\x04\n',)
As you can see, it needs prototxt and caffemodel files. You can find them here.
I'm trying to write a simple time lapse desktop video recorder which you can use to append to the same file over multiple sessions, and the following code works, however it requires encoding with ffmpeg to set an fps that isn't a default 25. I want to avoid using cv2 so i'm going the long way around this problem.
So what would be a way to write the video to include an fps setting of some sort, without having to re-encode with ffmpeg.
import fpstimer
from typing import BinaryIO
from io import BytesIO
from PIL import Image, ImageGrab
timer = fpstimer.FPSTimer(0.2)
def video(chunk: bytes, file_handler: BinaryIO):
file_handler.write(chunk)
while True:
try:
frame = ImageGrab.grab().convert('RGB')
buf = BytesIO()
frame.save(buf, format='PNG')
byte_im = buf.getvalue()
with open('output.mp4', "ab") as fh:
video(byte_im, fh)
timer.sleep()
except KeyboardInterrupt:
print('done')
raise
I am building up a library called hips where one module is involved with fetching tile images and storing them on disk. The problem here is that I fetch a tile from a remote URL and save it using scipy.misc.imsave function in a temporary directory. The saved file size is 41.0 kB, however, if I save the file manually from the remote URL, its size is 119.7 kB.
I have copied the failed test case below:
def test_fetch_read_write_jpg(self, tmpdir):
meta = HipsTileMeta( ... )
url = 'http://alasky.unistra.fr/2MASS/H/Norder6/Dir30000/Npix30889.jpg'
tile = HipsTile.fetch(meta, url)
filename = str(tmpdir / 'Npix30889.jpg')
tile.write(filename)
tile2 = HipsTile.read(meta, filename=filename)
print(tile.data.shape)
print(tile2.data.shape)
assert tile == tile2
Here is the failed assertion:
----------------------------------Captured stdout call--------------------------------------
(512, 512, 3)
(512, 512, 3)
False
The code involved with tile storing is shown below:
from scipy.misc import imsave
def write(self, filename: str = None) -> None:
path = Path(filename) if filename else self.meta.full_path
imsave(str(path), self.data)
I also tried saving the file using PIL.Image library, using this code:
from PIL import Image
image = Image.fromarray(self.data)
image.save(str(path))
But, it produces the same results. I tried printing out the tile data at index [0][0] which came to be [10, 10, 10] for both cases. Also, I displayed the image using matplotlib, and the results were identical. But, I can't figure out the reason for the reduction in size / quality.
JPEG is a lossy format. If you write an image to a JPEG file and then read it back, you won't, in general, get back the same data.
For lossless image storage, you could use PNG.
I have a stored picture on my computer. I open it using the Python Image module. Then I crop this image into several pieces using this module. To conclude, I would like to upload the image via a POST request on a website.
Because that small images are PIL object, I converted each of them into StringIO to be able to send the form without having to save them on my PC.
Unfortunately, I encounter an error, whereas if the images are physically stored on my PC, there is no problem. I do not understand why.
You can visit the website here: http://www.noelshack.com/api.php
This is a very basic API that returns the link to the uploaded picture.
In my case, the problem is that returns nothing, at the end of the second image (no problem for the first).
Here is the programming code to crop the image into 100 pieces.
import requests
import Image
import StringIO
import os
image = Image.open("test.jpg")
width, height = image.size
images = []
for i in range(10):
for j in range(10):
crop = image.crop((i * 10, j * 10, (i + 1) * 10, (j + 1) * 10))
images.append(crop)
The function to upload an image:
def upload(my_file):
api_url = 'http://www.noelshack.com/api.php'
r = requests.post(api_url, files={'fichier': my_file})
if not 'www.noelshack.com' in r.text:
raise Exception(r.text)
return r.text
Now we have two possibilities. The first is to save each of the 100 images on disk and upload them.
if not os.path.exists("directory"):
os.makedirs("directory")
i = 0
for img in images:
img.save("directory/" + str(i) + ".jpg")
i += 1
for file in os.listdir("directory"):
with open("directory/" + file, "rb") as f:
print upload(f)
It works like a charm, but it is not very convenient. So, I thought to use StringIO.
for img in images:
my_file = StringIO.StringIO()
img.save(my_file, "JPEG")
print upload(my_file.getvalue())
# my_file.close() -> Does not change anything
The first link is printed, but the function raise the exception then.
I think the problem lies in the img.save(), because the same kind of for loop was not working to save to disk and then upload. In addition, if you add a time.sleep(1) between the uploads, it seems to work.
Any help would be welcome please, because I'm really stuck.
my_file.getvalue() returns a string. What you need is a file-like object, which my_file already is. And file like objects have a cursor, so to speak, which says where to read from or write to. So, if you do my_file.seek(0) before the upload, it should get fixed.
modify the code to:
for img in images:
my_file = StringIO.StringIO()
img.save(my_file, "JPEG")
my_file.seek(0)
print upload(my_file)
I am looking to create base64 inline encoded data of images for display in a table using canvases. Python generates and creates the web page dynamically. As it stands python uses the Image module to create thumbnails. After all of the thumbnails are created Python then generates base64 data of each thumbnail and puts the b64 data into hidden spans on the user's webpage. A user then clicks check marks by each thumbnail relative to their interest. They then create a pdf file containing their selected images by clicking a generate pdf button. The JavaScript using jsPDF generates the hidden span b64 data to create the image files in the pdf file and then ultimately the pdf file.
I am looking to hopefully shave down Python script execution time and minimize some disk I/O operations by generating the base64 thumbnail data in memory while the script executes.
Here is an example of what I would like to accomplish.
import os, sys
import Image
size = 128, 128
im = Image.open("/original/image/1.jpeg")
im.thumbnail(size)
thumb = base64.b64encode(im)
This doesn't work sadly, get a TypeErorr -
TypeError: must be string or buffer, not instance
Any thoughts on how to accomplish this?
You first need to save the image again in JPEG format; using the im.tostring() method would otherwise return raw image data that no browser would recognize:
from io import BytesIO
output = BytesIO()
im.save(output, format='JPEG')
im_data = output.getvalue()
This you can then encode to base64:
image_data = base64.b64encode(im_data)
if not isinstance(image_data, str):
# Python 3, decode from bytes to string
image_data = image_data.decode()
data_url = 'data:image/jpg;base64,' + image_data
Here is one I made with this method:
data:image/jpg;base64,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
Unfortunately the Markdown parser doesn't let me use this as an actual image, but you can see it in action in a snippet instead:
<img src="data:image/jpg;base64,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"/>
In Python 3, you may need to use BytesIO:
from io import BytesIO
...
outputBuffer = BytesIO()
bg.save(outputBuffer, format='JPEG')
bgBase64Data = outputBuffer.getvalue()
# http://stackoverflow.com/q/16748083/2603230
return 'data:image/jpeg;base64,' + base64.b64encode(bgBase64Data).decode()
thumb = base64.b64encode(im.tostring())
I think would work
I use PNG when I save to the buffer. With JPEG the numpy arrays are a bit different.
import base64
import io
import numpy as np
from PIL import Image
image_path = 'dog.jpg'
img2 = np.array(Image.open(image_path))
# Numpy -> b64
buffered = io.BytesIO()
Image.fromarray(img2).save(buffered, format="PNG")
b64image = base64.b64encode(buffered.getvalue())
# b64 -> Numpy
img = np.array(Image.open(io.BytesIO(base64.b64decode(b64image))))
print(img.shape)
np.testing.assert_almost_equal(img, img2)
Note that it will be slower.