In this Python OpenCV Tutorial, explain how to create a video using NumPy array and images.
Video From NumPy Array
import numpy as np
import cv2
import os
width = 1280
hieght = 720
channel = 3
fps = 30
sec = 5
# Syntax: VideoWriter_fourcc(c1, c2, c3, c4) # Concatenates 4 chars to a fourcc code
# cv2.VideoWriter_fourcc('M','J','P','G') or cv2.VideoWriter_fourcc(*'MJPG)
fourcc = cv2.VideoWriter_fourcc(*'MP42') # FourCC is a 4-byte code used to specify the video codec.
# A video codec is software or hardware that compresses and decompresses digital video.
# In the context of video compression, codec is a portmanteau of encoder and decoder,
# while a device that only compresses is typically called an encoder, and one that only
# decompresses is a decoder. Source - Wikipedia
#Syntax: cv2.VideoWriter( filename, fourcc, fps, frameSize )
video = cv2.VideoWriter('test.mp4', fourcc, float(fps), (width, hieght))
for frame_count in range(fps*sec):
img = np.random.randint(0,255, (hieght, width, channel), dtype = np.uint8)
video.write(img)
video.release()
Video from Images
import numpy as np
import cv2
import os
width = 1280
hieght = 720
channel = 3
fps = 10
sec = 5
fourcc = cv2.VideoWriter_fourcc(*'MP42')
video = cv2.VideoWriter('image_to_video.avi', fourcc, float(fps), (width, hieght))
directry = r'C:\Users\kashz\AI Life\AI Projects - IAIP, PTs (Web + Channel)\02 OpenCV\000 opencv tutorial\data\images'
img_name_list = os.listdir(directry)
for frame_count in range(fps*sec):
img_name = np.random.choice(img_name_list)
img_path = os.path.join(directry, img_name)
img = cv2.imread(img_path)
img_resize = cv2.resize(img, (width, hieght))
video.write(img_resize)
video.release()
Related
Continue Reading
By reading the image as a NumPy array ndarray, various image processing can be performed using NumPy functions.
By operating ndarray, you can get and set (change) pixel values, trim images, concatenate images, etc. Those who are familiar with NumPy can do various image processing without using libraries such as OpenCV.
Even when using OpenCV, OpenCV for Python treats image data as ndarray, so it is useful to know how to use NumPy (ndarray). In addition to
OpenCV, many libraries such as scikit-image treat images as ndarray.
This article describes the following contents.
Read and write images:
- How to read image file as NumPy array
ndarray - How to save NumPy array
ndarray as image file
Examples of image processing with NumPy (ndarray):
- Get and set (change) pixel values
- Generation of single color image and concatenation
- Negative-positive inversion (inversion of pixel value)
- Color reduction
- Binarization
- Gamma correction
- Trimming with slice
- Split with slice or function
- Paste with slice
- Alpha blending and masking
- Rotate and flip
Sample codes on this article use Pillow to read and save image files. If you want to use OpenCV, see the following article.
- Reading and saving image files with Python, OpenCV
(imread, imwrite)
See also the following article about Pillow. Simple operations such as reading, saving, resizing, and rotating images can be done by Pillow alone.
- How to use Pillow (PIL: Python Imaging Library)
How to read an image file as ndarray
Take the following image as an example.
Passing the image data read by PIL.Image.open() to np.array() returns 3D ndarray whose shape is (row (height), column (width), color (channel)).
from PIL import Image
import numpy as np
im = np.array(Image.open('data/src/lena.jpg'))
print(type(im))
#
print(im.dtype)
# uint8
print(im.shape)
# (225, 400, 3)
The order of colors (channels) is RGB (red, green, blue). Note that it is different from the case of reading with cv2.imread() of OpenCV.
- Convert BGR and RGB with Python, OpenCV (cvtColor)
If you convert the image to grayscale with convert('L') and then pass it to np.array(), it returns 2D ndarray whose shape is (row (height), column (width)).
im_gray = np.array(Image.open('data/src/lena.jpg').convert('L'))
print(im_gray.shape)
# (225, 400)
You can also get ndarray from PIL.Image with np.asarray(). np.array() returns a rewritable ndarray, while np.asarray() returns a non-rewritablendarray.
For np.array(), you can change the value of the
element (pixel).
print(im.flags.writeable)
# True
print(im[0, 0, 0])
# 109
im[0, 0, 0] = 0
print(im[0, 0, 0])
# 0
For np.asarray(), you cannot change value because rewriting is prohibited. It is possible to create a new ndarray based on the read ndarray.
im_as = np.asarray(Image.open('data/src/lena.jpg'))
print(type(im_as))
#
print(im_as.flags.writeable)
# False
# im_as[0, 0, 0] = 0
# ValueError: assignment destination is read-only
The data type dtype of the read ndarray is uint8 (8-bit unsigned integer).
If you want to process it as a floating point number float, you can convert it with astype() or specify the data type in the second argument of np.array() and np.asarray().
im_f = im.astype(np.float64)
print(im_f.dtype)
# float64
im_f = np.array(Image.open('data/src/lena.jpg'), np.float64)
print(im_f.dtype)
# float64
See the
following article for more information about the data type dtype in NumPy.
- NumPy: Cast ndarray to a specific dtype with astype()
How to save NumPy array ndarray as image file
Passing ndarray to Image.fromarray() returns PIL.Image. It can be saved as an image file with save() method. The format of the saved file is automatically determined
from the extension of the path passed in the argument of save().
pil_img = Image.fromarray(im)
print(pil_img.mode)
# RGB
pil_img.save('data/temp/lena_save_pillow.jpg')
A grayscale image (2D array) can also be passed to Image.fromarray(). mode automatically becomes 'L' (grayscale). It can be saved with save().
pil_img_gray = Image.fromarray(im_gray)
print(pil_img_gray.mode)
# L
pil_img_gray.save('data/temp/lena_save_pillow_gray.jpg')
If you just want to save it, you can write it in one line.
Image.fromarray(im).save('data/temp/lena_save_pillow.jpg')
Image.fromarray(im_gray).save('data/temp/lena_save_pillow_gray.jpg')
If the data type dtype of ndarray is float, etc., an error will occur, so it is necessary to convert to uint8.
# pil_img = Image.fromarray(im_f)
# TypeError: Cannot handle this data type
pil_img = Image.fromarray(im_f.astype(np.uint8))
pil_img.save('data/temp/lena_save_pillow.jpg')
Note that if
the pixel value is represented by 0.0 to 1.0, it is necessary to multiply by 255 and convert to uint8 and save.
With save(), parameters according to the format can be passed as arguments. See Image file format for details.
For example, in the case of JPG, you can pass the quality of the image to the argument quality. It ranges from 1 (the lowest) to
95 (the highest) and defaults to 75.
- How to use Pillow (PIL: Python Imaging Library)
Get and set (change) pixel values
You can get the value of a pixel by specifying the coordinates at the index [row, columns] of ndarray. Note that the order is y, x in xy coordinates. The origin is the upper left.
from PIL import Image
import numpy as np
im = np.array(Image.open('data/src/lena.jpg'))
print(im.shape)
# (225, 400, 3)
print(im[100, 150])
# [111 81 109]
print(type(im[100, 150]))
#
The above
example shows the value at (y, x) = (100, 150), i.e., the 100th row and 150th column of pixels. As mentioned above, the colors of the ndarray obtained using Pillow are in RGB order, so the result is (R, G, B) = (111, 81, 109).
You can also use unpacking to assign them to separate variables.
- Unpack a tuple and list in Python
R, G, B = im[100, 150]
print(R)
# 111
print(G)
# 81
print(B)
# 109
It is also possible to get the value by specifying the color.
print(im[100, 150, 0])
# 111
print(im[100, 150, 1])
# 81
print(im[100, 150, 2])
# 109
You can also change to a new value. You can change RGB all at once or change it with just a single color.
im[100, 150] = (0, 50, 100)
print(im[100, 150])
# [ 0 50 100]
im[100, 150, 0] = 150
print(im[100, 150])
# [150 50 100]
Generation of single color image and concatenation
Generate single-color images by setting other color values to 0, and concatenate them horizontally with np.concatenate(). You can also concatenate images using np.hstack() or np.c_[]
from PIL import Image
import numpy as np
im = np.array(Image.open('data/src/lena_square.png'))
im_R = im.copy()
im_R[:, :, (1, 2)] = 0
im_G = im.copy()
im_G[:, :, (0, 2)] = 0
im_B = im.copy()
im_B[:, :, (0, 1)] = 0
im_RGB = np.concatenate((im_R, im_G, im_B), axis=1)
# im_RGB = np.hstack((im_R, im_G, im_B))
# im_RGB = np.c_['1', im_R, im_G, im_B]
pil_img = Image.fromarray(im_RGB)
pil_img.save('data/dst/lena_numpy_split_color.jpg')
Negative-positive inversion (invert pixel value)
It is also easy to calculate and manipulate pixel values.
A negative-positive inverted image can be generated by subtracting the pixel value from the max value (255 for uint8).
import numpy as np
from PIL import Image
im = np.array(Image.open('data/src/lena_square.png').resize((256, 256)))
im_i = 255 - im
Image.fromarray(im_i).save('data/dst/lena_numpy_inverse.jpg')
Because the original size is too large, it is resized with resize() for convenience. The same applies to the following examples.
- Resize images with Python, Pillow
Color reduction
Cut off
the remainder of the division using // and multiply again, the pixel values become discrete, and the number of colors can be reduced.
import numpy as np
from PIL import Image
im = np.array(Image.open('data/src/lena_square.png').resize((256, 256)))
im_32 = im // 32 * 32
im_128 = im // 128 * 128
im_dec = np.concatenate((im, im_32, im_128), axis=1)
Image.fromarray(im_dec).save('data/dst/lena_numpy_dec_color.png')
Binarization
It is also possible to assign to black and white according to the threshold.
See the following
articles for details.
- Binarize image with Python, NumPy, OpenCV
Gamma correction
You can do anything you want with pixel values, such as multiplication,
division, exponentiation, etc.
You don't need to use the for loop because the entire image can be calculated as it is.
from PIL import Image
import numpy as np
im = np.array(Image.open('data/src/lena_square.png'))
im_1_22 = 255.0 * (im / 255.0)**(1 / 2.2)
im_22 = 255.0 * (im / 255.0)**2.2
im_gamma = np.concatenate((im_1_22, im, im_22), axis=1)
pil_img = Image.fromarray(np.uint8(im_gamma))
pil_img.save('data/dst/lena_numpy_gamma.jpg')
As a result of the calculation, the data type dtype of numpy.ndarray is converted to the floating point number float. Note that you need to convert it to uint8 when you save it.
Trimming with slice
By specifying an area with slice, you can trim it to a rectangle.
from PIL import Image
import numpy as np
im = np.array(Image.open('data/src/lena_square.png'))
print(im.shape)
# (512, 512, 3)
im_trim1 = im[128:384, 128:384]
print(im_trim1.shape)
# (256, 256, 3)
Image.fromarray(im_trim1).save('data/dst/lena_numpy_trim.jpg')
See the following article for more information on slicing for numpy.ndarray.
It may be convenient to define a function that specifies the upper left coordinates and the width and height of the area to be trimmed.
def trim(array, x, y, width, height):
return array[y:y + height, x:x+width]
im_trim2 = trim(im, 128, 192, 256, 128)
print(im_trim2.shape)
# (128, 256, 3)
Image.fromarray(im_trim2).save('data/dst/lena_numpy_trim2.jpg')
If you specify outside the size of the image, it will be ignored.
im_trim3 = trim(im, 128, 192, 512, 128)
print(im_trim3.shape)
# (128, 384, 3)
Image.fromarray(im_trim3).save('data/dst/lena_numpy_trim3.jpg')
Split with slice or function
You can also split the image by slicing.
from PIL import Image
import numpy as np
im = np.array(Image.open('data/src/lena_square.png').resize((256, 256)))
print(im.shape)
# (256, 256, 3)
im_0 = im[:, :100]
im_1 = im[:, 100:]
print(im_0.shape)
# (256, 100, 3)
print(im_1.shape)
# (256, 156, 3)
Image.fromarray(im_0).save('data/dst/lena_numpy_split_0.jpg')
Image.fromarray(im_1).save('data/dst/lena_numpy_split_1.jpg')
It is also possible to split the image with NumPy function.
np.hsplit() splits ndarray horizontally. If an integer value is specified for the second argument, ndarray is split equally.
- numpy.hsplit — NumPy v1.19
Manual
im_0, im_1 = np.hsplit(im, 2)
print(im_0.shape)
# (256, 128, 3)
print(im_1.shape)
# (256, 128, 3)
If a list is specified as the second argument, ndarray is split at the position of that value.
im_0, im_1, im_2 = np.hsplit(im, [100, 150])
print(im_0.shape)
# (256, 100, 3)
print(im_1.shape)
# (256, 50, 3)
print(im_2.shape)
# (256, 106, 3)
np.vsplit() splits ndarray vertically. The usage of np.vsplit() is the same as np.hsplit().
- numpy.vsplit — NumPy v1.19 Manual
When an integer value is specified as the second argument with np.hsplit() or np.vsplit(), an error is
raised if it cannot be split equally. np.array_split() adjusts the size appropriately and splits it.
- numpy.array_split — NumPy v1.19 Manual
# im_0, im_1, im_2 = np.hsplit(im, 3)
# ValueError: array split does not result in an equal division
im_0, im_1, im_2 = np.array_split(im, 3, axis=1)
print(im_0.shape)
# (256, 86, 3)
print(im_1.shape)
# (256, 85, 3)
print(im_2.shape)
# (256, 85, 3)
Paste with slice
Using slices, one array rectangle can be replaced with another array rectangle.
By using this, a part of the image or the entire image can be pasted to another image.
import numpy as np
from PIL import Image
src = np.array(Image.open('data/src/lena_square.png').resize((128, 128)))
dst = np.array(Image.open('data/src/lena_square.png').resize((256, 256))) // 4
dst_copy = dst.copy()
dst_copy[64:128, 128:192] = src[32:96, 32:96]
Image.fromarray(dst_copy).save('data/dst/lena_numpy_paste.jpg')
dst_copy = dst.copy()
dst_copy[64:192, 64:192] = src
Image.fromarray(dst_copy).save('data/dst/lena_numpy_paste_all.jpg')
Note that an error will occur if the size of the area specified on the left side differs from the size of the area specified on the right side.
Alpha blending and masking
By the operation for each element (= pixel) of the array, two images can be alpha-blended or composited
based on a mask image. See the following articles for details.
- Alpha blending and masking of images with Python, OpenCV, NumPy
Rotate and flip
There are also functions that rotate the array and flip it up, down, left, and right.
- OpenCV, NumPy: Rotate and flip image
Original image:
Rotated image:
Flipped image:
How do you read an image in an array in Python?
Using OpenCV Library to Convert images to NumPy array
imread() function is used to load the image and It also reads the given image (PIL image) in the NumPy array format. Then we need to convert the image color from BGR to RGB. imwrite() is used to save the image in the file.
How do I read an image into a NumPy array?
How to convert an image to a numpy array in Python.
image = PIL. Image. open("red_image.png").
image_array = np. array(image).
print(image_array[0][0]) print first pixel in image..
print(image_array. shape) print dimensions of image..
How do you store an image in an array in Python?
Use the Image.fromarray() Function to Save a NumPy Array as an Image..
Use the imageio.imwrite() Function to Save a NumPy Array as an Image..
Use the matplotlib.pyplot.imsave() Function to Save a NumPy Array as an Image..
Use the cv2.imwrite() Function to Save a NumPy Array as an Image..
What does CV Imread return?
imread() Loads an image from a file. The function imread loads an image from the specified file and returns it. If the image cannot be read (because of missing file, improper permissions, unsupported or invalid format), the function returns an empty matrix ( Mat::data==NULL ).
