Unleash Your Creativity with Python Image Processing!

Python is a cool and modern choice for these types of image processing tasks due to its growing popularity as a scientific programming language and the free availability of many recent image processing libraries in its ecosystem.

Open Source Computer Vision Library or simply OpenCV is one of the most widely used libraries for computer vision applications. It is a great choice to perform computationally intensive computer vision tasks and used in the following examples.

import cv2

color = cv2.imread('bratusnet.jpeg', 0)
cv2.imwrite('bratusnet-gray.jpeg', color)
print('image converted')

OUT: image converted

import os
import cv2

images = os.listdir('img')
for image in images:
  # print(image)
  gray = cv2.imread(f'img/{image}', 0)
  # print(gray)
  cv2.imwrite(f'grayscaled/gray-{image}', gray)

print('images are converted')  

OUT: images are converted

import cv2

#scale=1/10
def calculate_size(scale_percentage, width, height):
  new_width = int(width * scale_percentage / 10)
  new_height = int(height * scale_percentage / 10)
  print("New Dimensions:", new_width, new_height)
  return (new_width, new_height)


def resize(image_path, scale_percentage, resized_path):
  image = cv2.imread(image_path)
  new_dim = calculate_size(scale_percentage, image.shape[1], image.shape[0])
  resized_image = cv2.resize(image, new_dim)
  cv2.imwrite(resized_path, resized_image)

resize('bratusnet.jpeg', 10, 'resized-bratusnet.jpeg')

print("Image resized")

OUT: New Dimensions: 438 779
Image resized

import cv2
import os

#scale=1/10
def calculate_size(scale_percentage, width, height):
  new_width = int(width * scale_percentage / 10)
  new_height = int(height * scale_percentage / 10)
 #print("New Dimensions:", new_width, new_height)
  return (new_width, new_height)


def resize(image_path, scale_percentage, resized_path):
  image = cv2.imread(image_path)
  new_dim = calculate_size(scale_percentage, image.shape[1], image.shape[0])
  resized_image = cv2.resize(image, new_dim)
  cv2.imwrite(resized_path, resized_image)

images = os.listdir('images')
for image in images:
  resize(f'images/{image}', 10, f'resized/resized-{image}')

print("Images are resized")

OUT: Images are resized

import cv2

#you will need faces.xml cascade classifier for face detection
image = cv2.imread('humans.jpeg', 1)
face_cascade = cv2.CascadeClassifier('faces.xml')

faces = face_cascade.detectMultiScale(image, 1.1, 4)
# print(faces)

for (x, y, w, h) in faces:
  cv2.rectangle(image, (x, y), (x+w, y+h), (255, 255, 255), 4)

cv2.imwrite('faces_detected.jpeg', image)
print('image with faces created')

OUT: image with faces created

The background of original image should be monochrome or approximately the same color.

import cv2
import numpy as np

foreground = cv2.imread("source.jpeg")
background = cv2.imread("back.jpeg")

# print(foreground[40, 40])
width = foreground.shape[1]
height = foreground.shape[0]
# print(foreground.shape)


resized_background = cv2.resize(background, (width, height))

for i in range(width):
  for j in range(height):
    pixel = foreground[j, i]
    # print(type(pixel))
    if np.any(pixel == [1, 255, 0]):
      foreground[j, i] = resized_background[j, i]

cv2.imwrite('output.jpeg', foreground)

print('background added !!!')
    

OUT: background added !!!

import cv2

# Load the two images
image = cv2.imread('original.jpeg')
watermark = cv2.imread('watermark.png')

# print(watermark.shape)

x = image.shape[1] - watermark.shape[1]
y = image.shape[0] - watermark.shape[0]

watermark_place = image[y:, x:]
cv2.imwrite('watermark_place.jpeg', watermark_place) 
# print(watermark_place.shape)

blend = cv2.addWeighted(watermark_place, 0.5, watermark, 0.5, 0)
cv2.imwrite('mix.jpeg', blend)

image[y:, x:] = blend
cv2.imwrite('origin-watermark.jpeg', image)

print('watermark added !!!')

OUT: watermark added !!!