Python Multiprocessing Download Images
Jammie Frodge
I've been working on code for basically this same thing. Right now the goal is just to replace white pixels with transparent ones, but it seems to replace the entire image so there is a bug somewhere...It doesn't get an error within the multiprocessing module anymore though, so maybe it could serve as an example of how to load a Queue and then have your worker processes work on it!
Yet, processing my image takes 23 seconds with threads and 170 seconds with multiprocessing!! I suspect this would come from the larger overhead needed to start Process objects, and the fact that my algorithm for processing each pixel is simple for now (just the if pixel[0] > 240 and pixel[1] > 240 and pixel[2] > 240: bit), so I'm likely not yielding the speed improvements that a complex pixel processing algorithm would get me. Also to note multiprocessing documentation
I have a python function that takes in an image path and outputs true or false depending whether the image is black or not. I want to process several images on the same machine and stop the process if even one of them is not black. I read a lot of multiprocessing in python, celery etc here, but I am not sure where to start.
I created a for loop that would loop through a directory of images and resize every image and then saves it to another directory. The code works but I'm trying to parallelize the process to make it faster.
For the purposes of this blog post we are utilizing multiprocessing to facilitate faster image hashing of an input dataset; however, you should use this function as a template for your own dataset processing.
Lines 36 and 37 determine the total number of images per process by dividing the number of image paths by the number of processes and taking the ceiling to ensure we use an integer value from here forward.
We examined multiprocessing with OpenCV through indexing a dataset of images for building an image hashing search engine; however, you can modify/implement your own process_images function to include your own functionality.
In this tutorial, we will look at how we can speed up scientific computations using multiprocessing in a real-world example. Specifically, we will detect the location of all nuclei within fluorescence microscopy images from the public MCF7 Cell Painting dataset released by the Broad Institute.
First, download the imaging data for the first plate of the Cell Painting dataset here to a folder called images/ and extract it. You should get a folder called images/Week1_22123/ filled with TIFF images. The images are named according to the following convention:
DAPI is a fluorescent dye that binds to DNA and will stain the nuclei in each image. Typically, DAPI signal will be captured in the first channel of the microscope. We'll first need to parse the image names to find images from the first channel (w1) so that we can detect the nuclei from only the DAPI images.
Here, we used glob and a pattern with wildcards to find the paths to all DAPI images. Apparently there are 240 DAPI images that we need to process! We can load the first image to see what we are working with.
Scikit-image is a Python package for image processing that we can use to detect nuclei in these DAPI images. A simple and effective approach is to smooth the image to reduce noise and then find all local maxima that are above a given intensity threshold. Let's write a function to do this on our example image.
In process_images1, we take in a list of image paths and use a list comprehension to load each image and detect nuclei. Using the %timeit magic command in Jupyter, we can see that this approach has an average execution time of 18 seconds.
What would happen if we read all the images into memory before detecting nuclei? Presumably, this approach would be faster because we wouldn't have to read the image data from disk during the %timeit command. Let's try it.
Wait, what? Why is this slower than also reading the images from disk with multiprocessing? If you read the documentation for the multiprocessing module closely, you will learn that data passed to workers of a Pool must be serialized via pickle. This serialization step creates some computational overhead. That means for Method 2, we were pickling path strings, but in this case, we were pickling entire images.
Nice! This is now slightly faster than Method 2 (as we may have originally expected). Notice that we use a global statement here to explicitly declare that this function uses the images array defined globally. While this is not strictly required, I have found it helpful to indicate that the _process_image_memory_fix function depends on some images array being available. We then map over all indices and allow each process to access the image it needs by indexing into the images array. This approach will only pickle integers instead of the images themselves.
Overall, multiprocessing drastically reduced the average execution times for this nuclei detection task. Interestingly, a naive approach to multiprocessing with images in memory actually increased the average execution time. By taking advantage of copy-on-write behavior, we were able to remove the significant serialization overhead of incurred when pickling whole images.
I really hope this article was helpful, so please let me know if you enjoyed it. The multiprocessing techniques presented here have helped me achieve ridiculous speed when processing images for our SCOUT paper. Perhaps in the future we can revisit this example but with GPU acceleration to finally achieve ludicrous speed.
I'm using the multiprocessing module in python to help speed up my main program. However I believe I might be doing something incorrectly, as I don't think the computations are happening quite in parallel.
I want my program to read in images from a video stream in the main process, and pass on the frames to two child processes that do computations on them and send text back (containing the results of the computations) to the main process.
I have the occasional need to resize a set of images. I used to usePhotoshop batch actions, then I used some droplets, and recently I'vebeen using a simple python script with PIL (Python Image Library)
Thanks to Python's [multiprocessinglibrary]( ) it wasvery easy to create a worker[pool]( -multiprocessing.pool)to handle the resizing. The results are impressive. The test task was toresize a folder of 350 jpeg files by 50% and save them to a folder.
There is one gotcha however, and that is that there does seem to be somesort of memory leak with the multiprocessing module. With just oneworker in the pool, you can see a steadily increasing memory use thatisn't present in the same PIL code that isn't run through themultiprocessing module. This is probably a manifestation of [thisbug]( )
Parallelism and concurrency are more generalized terms than multiprocessing and multithreading. Parallelism refers to the execution of multiple tasks that are actually being executed simultaneously. Concurrency refers to the execution of multiple tasks being interleaved, instead of each task being executed sequentially one after another. With concurrent executions of tasks, they can be executed in any order without the final outcome being different.
This Python program demonstrates how multiprocessing can boost the performance of a CPU-bound task. The objective of this program is to sum all numbers from 0 to 100,000,000, five times. This is certainly a CPU-bound task in that a single-core computing system could only complete this task faster by speeding up the CPU itself. However, with multiprocessing this program can take advantage of the multiple computing cores available to it on the local machine. This program uses the standard Python multiprocessing library to run the sum_all_numbers()function on five separate processes, in parallel. Running the program on my local machine is nearly five times faster using multiprocessing.
Both multithreading and multiprocessing allow Python code to run concurrently. Only multiprocessing will allow your code to be truly parallel. However, if your code is IO-heavy (like HTTP requests), then multithreading will still probably speed up your code.
To demonstrate concurrency in Python, we will write a small script to download the top popular images from Imgur. We will start with a version that downloads images sequentially, or one at a time. As a prerequisite, you will have to register an application on Imgur. If you do not have an Imgur account already, please create one first.
Let us start by creating a Python module, named download.py. This file will contain all the functions necessary to fetch the list of images and download them. We will split these functionalities into three separate functions:
Next, we will need to write a module that will use these functions to download the images, one by one. We will name this single.py. This will contain the main function of our first, naive version of the Imgur image downloader. The module will retrieve the Imgur client ID in the environment variable IMGUR_CLIENT_ID. It will invoke the setup_download_dir to create the download destination directory. Finally, it will fetch a list of images using the get_links function, filter out all GIF and album URLs, and then use download_link to download and save each of those images to the disk. Here is what single.py looks like:
This is almost the same as the previous one, with the exception that we now have a new class, DownloadWorker, which is a descendent of the Python Thread class. The run method has been overridden, which runs an infinite loop. On every iteration, it calls self.queue.get() to try and fetch a URL to from a thread-safe queue. It blocks until there is an item in the queue for the worker to process. Once the worker receives an item from the queue, it then calls the same download_link method that was used in the previous script to download the image to the images directory. After the download is finished, the worker signals the queue that that task is done. This is very important, because the Queue keeps track of how many tasks were enqueued. The call to queue.join() would block the main thread forever if the workers did not signal that they completed a task.
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