Develop Reference

Sending RGB image data from Python numpy to a browser HTML page

I need to send realtime image RGB data (in Numpy format) to a HTML page in a browser (web-based GUI), through HTTP.
The following code works with the well-known multipart/x-mixed-replace trick: run this and access http://127.0.0.1:5000/video_feed: you will see a video in the browser.
from flask import Flask, render_template, Response
import numpy as np, cv2
app = Flask('')
def gen_frames():
while True:
img = np.random.randint(0, 255, size=(1000, 1000, 3))
ret, buf = cv2.imencode('.jpg', img)
frame = buf.tobytes()
yield (b'--frame\r\nContent-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
#app.route('/video_feed')
def video_feed():
return Response(gen_frames(), mimetype='multipart/x-mixed-replace; boundary=frame')
app.run()
However, according to my benchmark, the real performance bottleneck is the cv2.imencode('.jpg', img).
In my real application, if I just generate the image, the CPU is ~ 1% for Python.
When I imencode(...), the CPU jumps to 25%, and 15% for Chrome.
I also tried with PNG format but it's similar.
Question: how to efficiently send RGB image data from a numpy array (example: 1000 x 1000 pixels x 3 colors because of RGB) to a browser HTML page?
(Without compression/decompression it might be better, but how?)
Here is the benchmark
FPS CPU PYTHON CPU CHROME
PNG 10.8 20 % 10 %
JPG 14 23 % 12 %
JPG 10.7 16 % 10 % (with time.sleep to match PNG 10.8 fps)
BMP 19 17 % 23 %
BMP 10.8 8 % 12 % (with time.sleep to match PNG 10.8 fps)

Try using the PILLOW module instead and see if that improves performance. My benchmark shows that each iteration of the gen_frames() generator function based on PILLOW requires less than half the CPU of the CV2 version.
from flask import Flask, render_template, Response
from PIL import Image
import numpy as np
from io import BytesIO
app = Flask('')
def gen_frames():
while True:
img = np.random.randint(0, 255, size=(1000, 1000, 3), dtype=np.uint8)
rgb_image = Image.fromarray(img, 'RGB')
buf = BytesIO()
rgb_image.save(buf, 'JPEG')
frame = buf.getbuffer()
yield (b'--frame\r\nContent-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
#app.route('/video_feed')
def video_feed():
return Response(gen_frames(), mimetype='multipart/x-mixed-replace; boundary=frame')
app.run()

According to the docs you could check if the default optimization is turned on:
Default Optimization in OpenCV
Many of the OpenCV functions are optimized using SSE2, AVX, etc. It contains the unoptimized code also. So if our system support these features, we should exploit them (almost all modern day processors support them). It is enabled by default while compiling. So OpenCV runs the optimized code if it is enabled, otherwise it runs the unoptimized code. You can use cv.useOptimized() to check if it is enabled/disabled and cv.setUseOptimized() to enable/disable it.
So try this:
In [5]: cv.useOptimized() # check for optimization
Out[5]: False
In [7]: cv.setUseOptimized(True) # turn it on if not already turned on

As it stands AVI or MP4 compression would be good quality even for movies, but the compression itself takes too much CPU time to perform it on live data.
If some arbitrary protocol/format were created, one would not just have to program the server but also the client to consume this protocol/format. Therefore still some standard solution should be preferred.
I believe you can find a compromise between compression and CPU load in video conferencing systems, where the live camera data needs to be compressed and streamed via the network. With that in mind I believe here are sources of information that can help pursuing the topic:
https://www.reachcambridge.com/wp-content/uploads/wp_videocompression_33085_en_0809_lo.pdf
https://cs.haifa.ac.il/~nimrod/Compression/Video/V3h261-2005.pdf
Open Source Video Conferencing software like Jitsi Meet or Apache OpenMeetings.

Maybe you can try encoding it as base64 for compression of the video/image and then send it to the browser with the base64 mime type e.g.data:image/jpeg;base64.

Heroku web dyno running Django app not releasing memory

I'm struggling with a memory issue on Heroku when running a Django application (with gunicorn).
I have the following code that takes a user-uploaded image, removes all EXIF data, and returns the image ready for it to be uploaded to S3. This is used both as a form data cleaner and when reading base64 data into memory.
def sanitise_image(img): # img is InMemoryUploadedFile
try:
image = Image.open(img)
except IOError:
return None
# Move all pixel data into a new PIL image
data = list(image.getdata())
image_without_exif = Image.new(image.mode, image.size)
image_without_exif.putdata(data)
# Create new file with image_without_exif instead of input image.
thumb_io = StringIO.StringIO()
image_without_exif.save(thumb_io, format=image.format)
io_len = thumb_io.len
thumb_file = InMemoryUploadedFile(thumb_io, None, strip_tags(img.name), img.content_type,
io_len, None)
# DEL AND CLOSE EVERYTHING
thumb_file.seek(0)
img.close()
del img
thumb_io.close()
image_without_exif.close()
del image_without_exif
image.close()
del image
return thumb_file
I basically take an InMemoryUploadedFile and return a new one with just the pixel data.
del and closes may be redundant, but they represent my attempt to fix the situation where Heroku memory usage keeps growing and is not released every time this function terminates, even remaining overnight:
Running this on localhost with Guppy and following the tutorial, there are no remaining InMemoryUploadedFiles nor StringIOs nor PIL Image left in the heap, leaving me puzzled.
My suspicion is Python does not release the memory back to the OS, as I've read in multiple threads on SO. Has anyone played around with InMemoryUploadedFile and can give me an explanation as to why this memory is not being released?
When I do not perform this sanitisation, the issue does not occur.
Thanks a lot!

I think the issue is creating the temporary list object:
data = list(image.getdata())
Try:
image_without_exif.putdata(image.getdata())
This is why I think that is the issue:
>>> images = [Image.new('RGBA', (100, 100)) for _ in range(100)]
Python memory usage increased ~4Mb.
>>> get_datas = [image.getdata() for image in images]
No memory increase.
>>> pixel_lists = [list(image.getdata()) for image in images]
Python memory usage increased by ~85Mb.
You probably don't want to make getdata() into a list unless you need the numbers explicitly. From the Pillow docs:
Note that the sequence object returned by this method is an internal PIL data type, which only supports certain sequence operations. To convert it to an ordinary sequence (e.g. for printing), use list(im.getdata()).

I found my own answer eventually. Huge thanks to Ryan Tran for pointing me in the right direction. list() does indeed cause the leak.
Using the equivalent split() and merge() method (docs) this is the updated code:
with Image.open(img) as image:
comp = image.split()
image_without_exif = Image.merge(image.mode, comp)
thumb_io = StringIO.StringIO()
image_without_exif.save(thumb_io, format=image.format)
io_len = thumb_io.len
clean_img = InMemoryUploadedFile(thumb_io, None, strip_tags(img.name), img.content_type,
io_len, None)
clean_img.seek(0)
return clean_img

Improving copying bytes from an Image

I have the following minimal code that gets the bytes from an image:
import Image
im = Image.open("kitten.png")
im_data = [pix for pixdata in im.getdata() for pix in pixdata]
This is rather slow (I have gigabytes of images to process) so how could this be sped up? I'm also unfamiliar with what exactly that code is trying to do. All my data is 1280 x 960 x 8-bit RGB, so I can ignore corner cases, etc.
(FYI, the full code is here - I've already replaced the ImageFile loop with the above Image.open().)

You can try
scipy.ndimage.imread()

If you mean speeding up by algorythamically i can suggest you accessing file with multiple threads simultaneously (only if you don't have a connection between processing sequence)
divide file logically by few sections and access each part simultaneously with threads (you have to put your operation inside a function and call it with threads)
here is a link to tutorial about threading in python
threding in python

I solved my problem, I think:
>>> [pix for pixdata in im.getdata() for pix in pixdata] ==
numpy.ndarray.tolist(numpy.ndarray.flatten(numpy.asarray(im)))
True
This cuts down the runtime by half, and with a bit of bash magic I can run the conversion on the 56 directories in parallel.

Difficulty with handling very large image using VIPS

I'm writing a Python(3.4.3) program that uses VIPS(8.1.1) on Ubuntu 14.04 LTS to read many small tiles using multiple threads and put them together into a large image.
In a very simple test :
from concurrent.futures import ThreadPoolExecutor
from multiprocessing import Lock
from gi.repository import Vips
canvas = Vips.Image.black(8000,1000,bands=3)
def do_work(x):
img = Vips.Image.new_from_file('part.tif') # RGB tiff image
with lock:
canvas = canvas.insert(img, x*1000, 0)
with ThreadPoolExecutor(max_workers=8) as executor:
for x in range(8):
executor.submit(do_work, x)
canvas.write_to_file('complete.tif')
I get correct result. In my full program, the work for each thread involves read binary from a source file, turn them into tiff format, read the image data and insert into canvas. It seems to work but when I try to examine the result, I ran into trouble. Because the image is extremely large(~50000*100000 pixels), I couldn't save the entire image in one file, so I tried
canvas = canvas.resize(.5)
canvas.write_to_file('test.jpg')
This takes extremely long time, and the resulting jpeg has only black pixels. If I do resize three times, the program get killed. I also tried
canvas.extract_area(20000,40000,2000,2000).write_to_file('test.tif')
This results in error message segmentation fault(core dumped) but it does save an image. There are image contents in it, but they seem to be in the wrong place.
I'm wondering what the problem could be?
Below are the codes for the complete program. The same logic was also implemented using OpenCV + sharedmem (sharedmem handled the multiprocessing part) and it worked without a problem.
import os
import subprocess
import pickle
from multiprocessing import Lock
from concurrent.futures import ThreadPoolExecutor
import threading
import numpy as np
from gi.repository import Vips
lock = Lock()
def read_image(x):
with open(file_name, 'rb') as fin:
fin.seek(sublist[x]['dataStartPos'])
temp_array = np.fromfile(fin, dtype='int8', count=sublist[x]['dataSize'])
name_base = os.path.join(rd_path, threading.current_thread().name + 'tempimg')
with open(name_base + '.jxr', 'wb') as fout:
temp_array.tofile(fout)
subprocess.call(['./JxrDecApp', '-i', name_base + '.jxr', '-o', name_base + '.tif'])
temp_img = Vips.Image.new_from_file(name_base + '.tif')
with lock:
global canvas
canvas = canvas.insert(temp_img, sublist[x]['XStart'], sublist[x]['YStart'])
def assemble_all(filename, ramdisk_path, scene):
global canvas, sublist, file_name, rd_path, tilesize_x, tilesize_y
file_name = filename
rd_path = ramdisk_path
file_info = fetch_pickle(filename) # A custom function
# this info includes where to begin reading image data, image size and coordinates
tilesize_x = file_info['sBlockList_P0'][0]['XSize']
tilesize_y = file_info['sBlockList_P0'][0]['YSize']
sublist = [item for item in file_info['sBlockList_P0'] if item['SStart'] == scene]
max_x = max([item['XStart'] for item in file_info['sBlockList_P0']])
max_y = max([item['YStart'] for item in file_info['sBlockList_P0']])
canvas = Vips.Image.black((max_x+tilesize_x), (max_y+tilesize_y), bands=3)
with ThreadPoolExecutor(max_workers=4) as executor:
for x in range(len(sublist)):
executor.submit(read_image, x)
return canvas
The above module (imported as mcv) is called in the driver script :
canvas = mcv.assemble_all(filename, ramdisk_path, 0)
To examine the content, I used
canvas.extract_area(25000, 40000, 2000, 2000).write_to_file('test_vips1.jpg')

I think your problem has to do with the way libvips calculates pixels.
In systems like OpenCV, images are huge areas of memory. You perform a series of operations, and each operation modifies a memory image in some way.
libvips is not like this, though the interface looks similar. In libvips, when you perform an operation on an image, you are actually just adding a new section to a pipeline. It's only when you finally connect the output to some sink (a file on disk, or a region of memory you want filled with image data, or an area of the display) that libvips will actually do any calculations. libvips will then use a recursive algorithm to run a large set of worker threads up and down the whole length of the pipeline, evaluating all of the operations you created at the same time.
To make an analogy with programming languages, systems like OpenCV are imperative, libvips is functional.
The good thing about the way libvips does things is that it can see the whole pipeline at once and it can optimise away most of the memory use and make good use of your CPU. The bad thing is that long sequences of operations can need large amounts of stack to evaluate (whereas with systems like OpenCV you are more likely to be bounded by image size). In particular, the recursive system used by libvips to evaluate means that pipeline length is limited by the C stack, about 2MB on many operating systems.
Here's a simple test program that does more or less what you are doing:
#!/usr/bin/python3
import sys
import pyvips
if len(sys.argv) < 4:
print "usage: %s image-in image-out n" % sys.argv[0]
print " make an n x n grid of image-in"
sys.exit(1)
tile = pyvips.Image.new_from_file(sys.argv[1])
outfile = sys.argv[2]
size = int(sys.argv[3])
img = pyvips.Image.black(size * tile.width, size * tile.height, bands=3)
for y in range(size):
for x in range(size):
img = img.insert(tile, x * size, y * size)
# we're not interested in huge files for this test, just write a small patch
img.crop(10, 10, 100, 100).write_to_file(outfile)
You run it like this:
time ./bigjoin.py ~/pics/k2.jpg out.tif 2
real 0m0.176s
user 0m0.144s
sys 0m0.031s
It loads k2.jpg (a 2k x 2k JPG image), repeats that image into a 2 x 2 grid, and saves a small part of it. This program will work well with very large images, try removing the crop and running as:
./bigjoin.py huge.tif out.tif[bigtiff] 10
and it'll copy the huge tiff image 100 times into a REALLY huge tiff file. It'll be quick and use little memory.
However, this program will become very unhappy with small images being copied many times. For example, on this machine (a Mac), I can run:
./bigjoin.py ~/pics/k2.jpg out.tif 26
But this fails:
./bigjoin.py ~/pics/k2.jpg out.tif 28
Bus error: 10
With a 28 x 28 output, that's 784 tiles. The way we've built the image, repeatedly inserting a single tile, that's a pipeline 784 operations long -- long enough to cause a stack overflow. On my Ubuntu laptop I can get pipelines up to about 2,900 operations long before it starts failing.
There's a simple way to fix this program: build a wide rather than a deep pipeline. Instead of inserting a single image each time, make a set of strips, then join the strips. Now the pipeline depth will be proportional to the square root of the number of tiles. For example:
img = pyvips.Image.black(size * tile.width, size * tile.height, bands=3)
for y in range(size):
strip = pyvips.Image.black(size * tile.width, tile.height, bands=3)
for x in range(size):
strip = strip.insert(tile, x * size, 0)
img = img.insert(strip, 0, y * size)
Now I can run:
./bigjoin2.py ~/pics/k2.jpg out.tif 200
Which is 40,000 images joined together.

making a memory only fileobject in python with pyfilesystem

I have written a motion detection/ video program using opencv2 which saves video output for x seconds. if motion in detected during that time, the output is saved as an alternate named file, but if not motion is detected then the file is overwritten. To avoid needless wear on a flash based memory system, I want to write the file to the RAM, and if motion is detected then save it to the non-volatile memory.
I am trying to create this file in the RAM using pyfilesystem-fs.memoryfs
import numpy as np
import cv2, time, os, threading, thread
from Tkinter import *
from fs.memoryfs import MemoryFS
class stuff:
mem=MemoryFS()
output = mem.createfile('output.avi')
rectime=0
delay=0
kill=0
cap = cv2.VideoCapture(0)
#out = cv2.VideoWriter('C:\motion\\output.avi',cv2.cv.CV_FOURCC('F','M','P','4'), 30, (640,480),True)
out = cv2.VideoWriter(output, cv2.cv.CV_FOURCC('F','M','P','4'), 30, (640,480),True)
This is the motion detection part
if value > 100:
print "saving"
movement=time.time()
while time.time()<int(movement)+stuff.rectime:
stuff.out.write(frame)
ret, frame = stuff.cap.read()
if stuff.out.isOpened() is True:
stuff.out.release()
os.rename(stuff.output, 'c:\motion\\' + time.strftime('%m-%d-%y_%H-%M-%S') + '.avi')
the os.rename function returns TypeError must be string, not None
I'm clearly using memoryfs incorrectly, but cannot find any examples of its use.
EDIT
I use the following line to open the file object and write to it
stuff.out.open(stuff.output, cv2.cv.CV_FOURCC(*'FMP4'),24,(640,480),True)
however this returns False , I'm not sure but it appears it can't open the file object.

To move your file form MemoryFS to real file system, you should read orig file and write it to dest file, something like
with mem.open('output.avi', 'b') as orig:
with open('c:\\motion\\' + time.strftime('%m-%d-%y_%H-%M-%S') + '.avi')) as dest:
dest.write(orig.read())