OpenCV(cv2) Vs Pillow(PIL)

_ OpenCV is 1.4 Times faster than PIL _

Image is simply a matrix of pixels and each pixel is a single, square-shaped point of colored light. This can be explained quickly with a grayscaled image. grayscaled image is the image where each pixel represents different shades of a gray color.

Difference between OpenCV and PIL | Image by Author

I mostly use OpenCV to complete my tasks as I find it 1.4 times quicker than PIL.

Let’s see, how the image can be processed using both — OpenCV and PIL.

## Installation & importing

# cv2
pip install opencv-python
import cv2

---

# PIL
pip install Pillow
from PIL import Image, ImageEnhance

## Read the image

# Read/open a colorful image
pil_img = Image.open("your_image.jpg")  # RGB
cv2_img = cv2.imread("your_image.jpg")  # BGR

# Read/open a grayscale image:
pil_img = Image.open("your_image.jpg").convert("L")
cv2_img = cv2.imread("your_image.jpg", cv2.IMREAD_GRAYSCALE)

## Write/save an image

pil_img.save("new_image.jpg")
cv2.imwrite("new_image.jpg", cv2_img)

# Write/save a JPEG image with specific quality:
pil_img.save("new_image.jpg", quality=95)
cv2.imwrite("new_image.jpg", cv2_img, [int(cv2.IMWRITE_JPEG_QUALITY), 95])

## Conversion between both

# Pillow image to OpenCV image:
cv2_img = np.array(pil_img)
cv2_img = cv2.cvtColor(cv2_img, cv2.COLOR_RGB2BGR)
# OpenCV image to Pillow image
cv2_img = cv2.cvtColor(cv2_img, cv2.COLOR_BGR2RGB)
pil_img = Image.fromarray(cv2_img)
Note: OpenCV images are in BGR color format, while Pillow images are in RGB color format. So we have to manually convert the color format from one to another.

## Shape / Size

# cv2
if cv2_img.ndim == 2:
  height, width = cv2_img.shape
  depth = 1
else:
  height, width, depth = cv2_img.shape

# PIL
width, height = pil_img.size 
cv2_img = np.array(pil_img)
if cv2_img.ndim == 2:
  depth = 1
else:
  depth = cv2_img.shape[-1]
Note: It is hard to get the depth/channels directly from a Pillow image object, the easier way to do this would be to first convert it to an OpenCV image (ndarray) and then get the shape.

## Resize

# Resize without preserving the aspect ratio:
pil_img_resized = pil_img.resize((NEW_WIDTH, NEW_HEIGHT))
cv2_img_resized = cv2.resize(cv2_img, (NEW_WIDTH, NEW_HEIGHT))
Resize and preserve the aspect ratio:

# OpenCV:
scale_ratio = 0.6
width = int(img.shape[1] * scale_ratio)
height = int(img.shape[0] * scale_ratio)
dim = (width, height)
cv2_img_resized = cv2.resize(cv2_img, dim, interpolation=cv2.INTER_AREA)

# Pillow:
# scale ratio = min(max_width/width, max_height/height)
max_width = 256
max_height = 256
pil_img.thumbnail((max_width, max_height), Image.ANTIALIAS)

## RGBA to RGB

# Convert transparent pixels to white pixels (by pasting the RGBA image on a white RGB image).


#cv2
def cv2_RGBA2RGB(img):
  b, g, r, a = cv2.split(img)
  alpha = a / 255
  r = (255 * (1 - alpha) + r * alpha).astype(np.uint8)
  g = (255 * (1 - alpha) + g * alpha).astype(np.uint8)
  b = (255 * (1 - alpha) + b * alpha).astype(np.uint8)
  new_img = cv2.merge((b, g, r))
  return new_img

# PIL
def pil_RGBA2RGB(img):
  img.load() # for png.split()
  bg = Image.new("RGB", img.size, (255, 255, 255))
  bg.paste(img, mask=img.split()[3]) # 3 is the alpha channel
  return bg

## Read an image from a URL.

# without request headers

url = ''

# cv2
import cv2
import numpy as np
import requests
cv2_img = cv2.imdecode(np.asarray(requests.get(url, stream=True).content, dtype=np.uint8), cv2.IMREAD_UNCHANGED)

# PIL
importt io;
import requests
pil_img = Image.open(io.BytesIO(requests.get(url, stream=True).content))

## Base64 Conversions

# Read image file as base64:
import base64
with open("your_image.jpg", "rb") as f:
  base64_str = base64.b64encode(f.read())

# Conversion between Pillow & base64:
import base64
from io import BytesIO
from PIL import Image
def pil_to_base64(pil_img):
  img_buffer = BytesIO()
  pil_img.save(img_buffer, format='JPEG')
  byte_data = img_buffer.getvalue()
  base64_str = base64.b64encode(byte_data)
  return base64_str
def base64_to_pil(base64_str):
  pil_img = base64.b64decode(base64_str)
  pil_img = BytesIO(pil_img)
  pil_img = Image.open(pil_img)
  return pil_img

# Conversion between OpenCV & base64:
import base64
import numpy as np
import cv2
def cv2_base64(cv2_img):
  base64_str = cv2.imencode('.jpg', cv2_img)[1].tostring()
  base64_str = base64.b64encode(base64_str)
  return base64_str
def base64_cv2(base64_str):
  imgString = base64.b64decode(base64_str)
  nparr = np.fromstring(imgString, np.uint8)
  cv2_img= cv2.imdecode(nparr, cv2.IMREAD_COLOR)
  return cv2_img

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