Develop Reference

Where is the bottleneck in my image manipulation code?

I wrote this script to do some image processing on a large number of PNG files (around 1500 in total). They are organized into subdirectories.
That's my code:
from PIL import Image
import os
path = "/Some/given/path"
file_list = []
counter = 1
for root, dirs, files in os.walk(path):
for file in files:
if file.endswith(".png"):
temp_file = {"path": os.path.join(root, file), "name": file}
file_list.append(temp_file)
for curr_file in file_list:
img = Image.open(curr_file["path"])
img = img.convert("RGBA")
val = list(img.getdata())
new_data = []
for item in val:
if item[3] == 0:
new_data.append(item)
else:
new_data.append((0, 0, 0, 255))
img.putdata(new_data)
file_name = "transform" + str(counter) + ".png"
replaced_text = curr_file["name"]
new_file_name = curr_file["path"].replace(replaced_text, file_name)
img.save(new_file_name)
counter += 1
The folder structure is as follows:
Source folder
-- folder__1
-- image_1.png
-- image_2.png
-- image_3.png
-- folder__2
-- image_3.png
-- image_5.png
-- folder__3
-- image_6.png
When testing on individual images, the image processing takes only a few seconds. However, when running the script, it takes around an hour to process 15 images. Any suggestions on where I'm messing up?

The main issue is located here:
new_data = []
for item in val:
if item[3] == 0:
new_data.append(item)
else:
new_data.append((0, 0, 0, 255))
img.putdata(new_data) # <--
You don't need to update the content of img for each pixel, if you're collecting the complete new_data anyway. So, just move that line outside the loop:
new_data = []
for item in val:
if item[3] == 0:
new_data.append(item)
else:
new_data.append((0, 0, 0, 255))
img.putdata(new_data) # <--
Now, get rid of iterating all pixels at all by using NumPy and its vectorization capabilities:
from PIL import Image
import os
import numpy as np # <--
path = "/Some/given/path"
file_list = []
counter = 1
for root, dirs, files in os.walk(path):
for file in files:
if file.endswith(".png"):
temp_file = {"path": os.path.join(root, file), "name": file}
file_list.append(temp_file)
for curr_file in file_list:
img = Image.open(curr_file["path"])
img = img.convert("RGBA")
img = np.array(img) # <--
img[img[..., 3] != 0] = (0, 0, 0, 255) # <--
img = Image.fromarray(img) # <--
file_name = "transform" + str(counter) + ".png"
replaced_text = curr_file["name"]
new_file_name = curr_file["path"].replace(replaced_text, file_name)
img.save(new_file_name)
counter += 1
Basically, you set all pixels with alpha channel not equal to 0 to (0, 0, 0, 255). That's the NumPy one-liner you see there. The line before and after are just for transformation from Pillow Image to NumPy array and vice versa.
EDIT: If you don't want to have NumPy in your code, you could also get rid of the loops by using Pillow's point function, cf. this tutorial:
from PIL import Image
import os
path = "/Some/given/path"
file_list = []
counter = 1
for root, dirs, files in os.walk(path):
for file in files:
if file.endswith(".png"):
temp_file = {"path": os.path.join(root, file), "name": file}
file_list.append(temp_file)
for curr_file in file_list:
img = Image.open(curr_file["path"])
img = img.convert("RGBA")
source = img.split() # <--
mask = source[3].point(lambda i: i > 0 and 255) # <--
img.paste(Image.new("RGBA", img.size, (0, 0, 0, 255)), None, mask) # <--
file_name = "transform" + str(counter) + ".png"
replaced_text = curr_file["name"]
new_file_name = curr_file["path"].replace(replaced_text, file_name)
img.save(new_file_name)
counter += 1
----------------------------------------
System information
----------------------------------------
Platform: Windows-10-10.0.16299-SP0
Python: 3.9.1
NumPy: 1.20.2
Pillow: 8.1.2
----------------------------------------

You can use snakeviz library to profile your code -
Snakeviz - https://jiffyclub.github.io/snakeviz/
python -m cProfile -o program.prof my_program.py
Once the profile is generated you can visualise and see which function/which line is taking more time.
snakeviz program.prof

Python "for" loop does not iterate as many as it should

My python code does not iterate as many as it should. There are six image files in the working directory and len(f) also gives six. But the actual iteration of for loop stops after performing two loops.
import os
import cv2
import numpy as np
from matplotlib import pyplot as plt
path = "D:\\_my_python\\Image_histogram_equalization\\source_imgs"
os.chdir(path)
print("Current Working Directory: " , os.getcwd())
files = []
for r, d, f in os.walk(path):
for file in f:
if '.jpg' in file:
files.append(os.path.join(r, file))
print("Processing %d files.." %len(f))
count = 0
for f in files:
g = f[:f.find(".jpg")] + "_CLAHE20.jpg"
print("Converting %s to %s..." % (f, g))
img = cv2.imread(f)
img_yuv = cv2.cvtColor(img, cv2.COLOR_BGR2YUV)
clahe = cv2.createCLAHE(clipLimit=2, tileGridSize=(8,8))
img_yuv[:,:,0] = clahe.apply(img_yuv[:,:,0])
img_output = cv2.cvtColor(img_yuv, cv2.COLOR_YUV2BGR)
cv2.imwrite(g, img_output)
count = count + 1
else:
print("Process completed for %d files out of %d files. " % (count, len(f)))
It should run six loops because there are six images in the folder and len(f) also gives six.

Extract frames from video into specific folder

I want to extract frames from 3 videos into 3 different folder. Each folder has the frames of their corresponding video file. I am able to access my objective for only the 3rd video. How can I extract the frames for the first 2 videos as well
I have made the folders having names as per the video files till now. Developed the code for frame extraction but can extract only from the last video. Below is my code
import cv2
import glob
from glob import glob
import os
import shutil
def extractFrames(m,n):
if not os.path.exists:
os.makedirs(n)
vid_files=glob(m)
print(vid_files)
for v_f in range(len(vid_files)):
v1=os.path.basename(vid_files[v_f])
print(v1)
vid_name = os.path.splitext(v1)[0]
print(vid_name)
output = n +'\\video_' + vid_name
os.makedirs(output)
print(output)
vidcap = cv2.VideoCapture(vid_files[v_f])
print(vidcap)
success,image = vidcap.read()
seconds = 2
fps = vidcap.get(cv2.CAP_PROP_FPS) # Gets the frames per second
multiplier = fps * seconds
count=0
while success:
img_name = vid_name + '_f' + str(count) + ".jpg"
image_path = output + "/" + img_name
frameId = int(round(vidcap.get(1)))
success,image = vidcap.read()
if frameId % multiplier == 0:
cv2.imwrite(filename = image_path, img = image)
count+=1
vidcap.release()
cv2.destroyAllWindows()
print('finished processing video {0} with frames {1}'.format(vid_files[v_f], count))
return output
x=("C:\\Python36\\videos\\*.mp4")
y=("C:\\Python36\\videos\\videos_new")
z=extractFrames(x,y)
If there are 3 videos namely video1,video2,video3. I want to extract the corresponding frames into their specific folders i.e video1 folder,video2 folder, video3 folder. Currently I am able to extract the frames for only the 3rd video into folder video3. How can I do it for video1 and video2 as well

Your indentation on the part from vidcap = ... down is off. Therefor only the last file in the for-loop is used.
import cv2
import glob
from glob import glob
import os
import shutil
def extractFrames(m,n):
if not os.path.exists:
os.makedirs(n)
vid_files=glob(m)
print(vid_files)
for v_f in range(len(vid_files)):
v1=os.path.basename(vid_files[v_f])
print(v1)
vid_name = os.path.splitext(v1)[0]
print(vid_name)
output = n +'\\video_' + vid_name
os.makedirs(output)
print(output)
vidcap = cv2.VideoCapture(vid_files[v_f])
print(vidcap)
success,image = vidcap.read()
seconds = 2
fps = vidcap.get(cv2.CAP_PROP_FPS) # Gets the frames per second
multiplier = fps * seconds
count=0
while success:
img_name = vid_name + '_f' + str(count) + ".jpg"
image_path = output + "/" + img_name
frameId = int(round(vidcap.get(1)))
success,image = vidcap.read()
if frameId % multiplier == 0:
cv2.imwrite(filename = image_path, img = image)
count+=1
vidcap.release()
cv2.destroyAllWindows()
print('finished processing video {0} with frames {1}'.format(vid_files[v_f], count))
return output # indent this less
x=("C:\\Python36\\videos\\*.mp4")
y=("C:\\Python36\\videos\\videos_new")
z=extractFrames(x,y)

Converting images in a folder to grayscale using python and opencv and writing it to a specific folder

import glob
import cv2
import os
import numpy as np
from PIL import Image
images=[]
images=np.array(images)
path='C:\Users\Quantum\Desktop\test'
count=0
images = [cv2.imread(file,0) for file in glob.glob("E:\homework\Computer vision\Faces\*.jpg")]
for i in range(len(images)):
# im = Image.fromarray(images[i])
# cv2.imwrite(str(path) + '.jpg', images[count])
cv2.imwrite(os.path.join(path, 'pic.jpg'), images[count])
count+=1
Trying to select all the images from a folder and the images are getting selected and are converted to grayscale although I dont know how to write those images to a specific folder.Kindly help

#multiple image conversions
import cv2
import os,glob
from os import listdir,makedirs
from os.path import isfile,join
path = '/root/Desktop/Anil' # Source Folder
dstpath = '/root/Desktop/Anil2' # Destination Folder
try:
makedirs(dstpath)
except:
print ("Directory already exist, images will be written in same folder")
# Folder won't used
files = list(filter(lambda f: isfile(join(path,f)), listdir(path)))
for image in files:
try:
img = cv2.imread(os.path.join(path,image))
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
dstPath = join(dstpath,image)
cv2.imwrite(dstPath,gray)
except:
print ("{} is not converted".format(image))
for fil in glob.glob("*.jpg"):
try:
image = cv2.imread(fil)
gray_image = cv2.cvtColor(os.path.join(path,image), cv2.COLOR_BGR2GRAY) # convert to greyscale
cv2.imwrite(os.path.join(dstpath,fil),gray_image)
except:
print('{} is not converted')

import cv2
import glob, os, errno
# Replace mydir with the directory you want
mydir = r'C:\Users\Quantum\Desktop\testoutput'
#check if directory exist, if not create it
try:
os.makedirs(mydir)
except OSError as e:
if e.errno == errno.EEXIST:
raise
for fil in glob.glob("*.jpg"):
image = cv2.imread(fil)
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # convert to greyscale
cv2.imwrite(os.path.join(mydir,fil),gray_image) # write to location with same name

import os,cv2
path = r'C:\Users\me\Desktop\folder' # Source Folder
dstpath = r'C:\Users\me\Desktop\desfolder' # Destination Folder
try:
makedirs(dstpath)
except:
print ("Directory already exist, images will be written in asme folder")
# Folder won't used
files = os.listdir(path)
for image in files:
img = cv2.imread(os.path.join(path,image))
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
cv2.imwrite(os.path.join(dstpath,image),gray)

import cv2
from os import listdir,makedirs
from os.path import isfile,join
path = r'C:\Users\fakabbir.amin\Desktop\pdfop' # Source Folder
dstpath = r'C:\Users\fakabbir.amin\Desktop\testfolder' # Destination Folder
try:
makedirs(dstpath)
except:
print ("Directory already exist, images will be written in asme folder")
# Folder won't used
files = [f for f in listdir(path) if isfile(join(path,f))]
for image in files:
try:
img = cv2.imread(os.path.join(path,image))
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
dstPath = join(dstpath,image)
cv2.imwrite(dstPath,gray)
except:
print ("{} is not converted".format(image))
This code snippet will take all the images from path and write into another folder mentioned in dstpath.

Loading all images using imread from a given folder

Loading and saving images in OpenCV is quite limited, so... what is the preferred ways to load all images from a given folder? Should I search for files in that folder with .png or .jpg extensions, store the names and use imread with every file? Or is there a better way?

Why not just try loading all the files in the folder? If OpenCV can't open it, oh well. Move on to the next. cv2.imread() returns None if the image can't be opened. Kind of weird that it doesn't raise an exception.
import cv2
import os
def load_images_from_folder(folder):
images = []
for filename in os.listdir(folder):
img = cv2.imread(os.path.join(folder,filename))
if img is not None:
images.append(img)
return images

I used skimage. You can create a collection and access elements the standard way, i.e. col[index]. This will give you the RGB values.
from skimage.io import imread_collection
#your path
col_dir = 'cats/*.jpg'
#creating a collection with the available images
col = imread_collection(col_dir)

import glob
cv_img = []
for img in glob.glob("Path/to/dir/*.jpg"):
n= cv2.imread(img)
cv_img.append(n)`

If all images are of the same format:
import cv2
import glob
images = [cv2.imread(file) for file in glob.glob('path/to/files/*.jpg')]
For reading images of different formats:
import cv2
import glob
imdir = 'path/to/files/'
ext = ['png', 'jpg', 'gif'] # Add image formats here
files = []
[files.extend(glob.glob(imdir + '*.' + e)) for e in ext]
images = [cv2.imread(file) for file in files]

you can use glob function to do this. see the example
import cv2
import glob
for img in glob.glob("path/to/folder/*.png"):
cv_img = cv2.imread(img)

You can also use matplotlib for this, try this out:
import matplotlib.image as mpimg
def load_images(folder):
images = []
for filename in os.listdir(folder):
img = mpimg.imread(os.path.join(folder, filename))
if img is not None:
images.append(img)
return images

import os
import cv2
rootdir = "directory path"
for subdir, dirs, files in os.walk(rootdir):
for file in files:
frame = cv2.imread(os.path.join(subdir, file))

To add onto the answer from Rishabh and make it able to handle files that are not images that are found in the folder.
import matplotlib.image as mpimg
images = []
folder = './your/folder/'
for filename in os.listdir(folder):
try:
img = mpimg.imread(os.path.join(folder, filename))
if img is not None:
images.append(img)
except:
print('Cant import ' + filename)
images = np.asarray(images)

Here is a simple script that feature opencv, scikit image, and glob
#!C:\Users\test\anaconda3\envs\data_aquisition\python.exe
import glob
import argparse
from timeit import default_timer as timer
import skimage
from skimage.io import imread_collection
import cv2
def get_args():
parser = argparse.ArgumentParser(
description='script that test the fastest image loading methods')
parser.add_argument('src_path', help = "diractorry that contains the ims")
parser.add_argument('extension', help = "extension of the images",choices=['jpg','png','webp'])
return parser.parse_args()
def load_imgs_scikit_image_collection(path:str):
#creating a collection with the available images
col = imread_collection(path)
print('loaded: ',len(col),' imgs')
return col
def load_imgs_scikit_image_glob(path):
imgs = []
for img in glob.glob(path):
imgs.append(skimage.io.imread(img))
return imgs
def load_image_opencv(path:str):
imgs = []
for f in glob.glob(path):
imgs.extend(cv2.imread(f))
return imgs
def load_image_opencv_glob(path:str):
filenames = glob.glob(path)
filenames.sort()
images = [cv2.imread(img) for img in filenames]
return images
def laod_images_opencv_extisions(path):
ext = [".jpg",".gif",".png",".tga",".webp"] # Add image formats here
files = []
images = []
[files.extend(glob.glob(path + '/*' + e)) for e in ext]
images.extend([cv2.imread(file) for file in files])
return images
def laod_images_ski_extisions(path):
ext = [".jpg",".gif",".png",".tga",".webp"] # Add image formats here
files = []
images = []
[files.extend(glob.glob(path + '/*' + e)) for e in ext]
images.extend([skimage.io.imread(file) for file in files])
return images
def show_image(img):
window_name = 'image'
cv2.imshow(window_name, img)
cv2.waitKey(0)
cv2.destroyAllWindows()
def main():
args = get_args()
dir = args.src_path+'/*.'+args.extension
start = timer()
imgs=load_imgs_scikit_image_collection(dir)
end = timer()
print('scikit_image image collection',end - start) #time 0.08381089999999991
show_image(imgs[2])
start = timer()
load_imgs_scikit_image_glob(dir)
end = timer()
print('scikit_image and glob',end - start) #time 16.627431599999998
# dir = args.src_path+'\\.*'+args.extension
start = timer()
imgs_opencv = load_image_opencv_glob(dir) #time 10.9856656
end = timer()
print('opencv glob',end - start)
show_image(imgs_opencv[2])
start = timer()
valid_imgs_opencv = laod_images_opencv_extisions(args.src_path) #time 11.318516700000004
end = timer()
print('opencv glob extensions',end - start)
show_image(valid_imgs_opencv[2])
start = timer()
valid_imgs_opencv = laod_images_ski_extisions(args.src_path) #time 15.939870800000001
end = timer()
print('scikit_image glob extensions',end - start)
show_image(valid_imgs_opencv[2])
main()
Command to run script: python best_image_loader.py D:\data\dataset\radar_dome\manual png
png is used to load only png files.
Output
loaded: 876 imgs
scikit_image image collection 0.08248239999999996
scikit_image and glob 14.939381200000001
opencv glob 10.9708085
opencv glob extensions 10.974014100000005
scikit_image glob extensions 14.877048600000002

your_path = 'your_path'
ext = ['*.jpg', '*.png', '*.gif'] # Add image formats here
images = []
not_copy = 0
for item in [your_path + '/' + e for e in ext]:
images += glob(item)