A Python wrapper of libjpeg-turbo for decoding and encoding JPEG image.
import cv2
from turbojpeg import TurboJPEG, TJPF_GRAY, TJSAMP_GRAY, TJFLAG_PROGRESSIVE, TJFLAG_FASTUPSAMPLE, TJFLAG_FASTDCT
# specifying library path explicitly
# jpeg = TurboJPEG(r'D:\turbojpeg.dll')
# jpeg = TurboJPEG('/usr/lib64/libturbojpeg.so')
# jpeg = TurboJPEG('/usr/local/lib/libturbojpeg.dylib')
# using default library installation
jpeg = TurboJPEG()
# decoding input.jpg to BGR array
in_file = open('input.jpg', 'rb')
bgr_array = jpeg.decode(in_file.read())
in_file.close()
cv2.imshow('bgr_array', bgr_array)
cv2.waitKey(0)
# decoding input.jpg to BGR array with fast upsample and fast DCT. (i.e. fastest speed but lower accuracy)
in_file = open('input.jpg', 'rb')
bgr_array = jpeg.decode(in_file.read(), flags=TJFLAG_FASTUPSAMPLE|TJFLAG_FASTDCT)
in_file.close()
cv2.imshow('bgr_array', bgr_array)
cv2.waitKey(0)
# direct rescaling 1/2 while decoding input.jpg to BGR array
in_file = open('input.jpg', 'rb')
bgr_array_half = jpeg.decode(in_file.read(), scaling_factor=(1, 2))
in_file.close()
cv2.imshow('bgr_array_half', bgr_array_half)
cv2.waitKey(0)
# getting possible scaling factors for direct rescaling
scaling_factors = jpeg.scaling_factors
# decoding JPEG image properties
in_file = open('input.jpg', 'rb')
width, height, jpeg_subsample, jpeg_colorspace = jpeg.decode_header(in_file.read())
in_file.close()
# decoding input.jpg to YUV array
in_file = open('input.jpg', 'rb')
buffer_array, plane_sizes = jpeg.decode_to_yuv(in_file.read())
in_file.close()
# decoding input.jpg to YUV planes
in_file = open('input.jpg', 'rb')
planes = jpeg.decode_to_yuv_planes(in_file.read())
in_file.close()
# encoding BGR array to output.jpg with default settings.
out_file = open('output.jpg', 'wb')
out_file.write(jpeg.encode(bgr_array))
out_file.close()
# encoding BGR array to output.jpg with TJSAMP_GRAY subsample.
out_file = open('output_gray.jpg', 'wb')
out_file.write(jpeg.encode(bgr_array, jpeg_subsample=TJSAMP_GRAY))
out_file.close()
# encoding BGR array to output.jpg with quality level 50.
out_file = open('output_quality_50.jpg', 'wb')
out_file.write(jpeg.encode(bgr_array, quality=50))
out_file.close()
# encoding BGR array to output.jpg with quality level 100 and progressive entropy coding.
out_file = open('output_quality_100_progressive.jpg', 'wb')
out_file.write(jpeg.encode(bgr_array, quality=100, flags=TJFLAG_PROGRESSIVE))
out_file.close()
# decoding input.jpg to grayscale array
in_file = open('input.jpg', 'rb')
gray_array = jpeg.decode(in_file.read(), pixel_format=TJPF_GRAY)
in_file.close()
cv2.imshow('gray_array', gray_array)
cv2.waitKey(0)
# scale with quality but leaves out the color conversion step
in_file = open('input.jpg', 'rb')
out_file = open('scaled_output.jpg', 'wb')
out_file.write(jpeg.scale_with_quality(in_file.read(), scaling_factor=(1, 4), quality=70))
out_file.close()
in_file.close()
# lossless crop image
out_file = open('lossless_cropped_output.jpg', 'wb')
out_file.write(jpeg.crop(open('input.jpg', 'rb').read(), 8, 8, 320, 240))
out_file.close()
# in-place decoding input.jpg to BGR array
# here I use a 640x480 example (in practise, read the dimensions)
in_file = open('input.jpg', 'rb')
img_array = np.empty((640, 480, 3), dtype=np.uint8)
result = jpeg.decode(in_file.read(), dst=img_array)
in_file.close()
# return value is the img_array argument value
id(result) == id(img_array)
# True
# Optional: display the in-place array
# cv2.imshow('img_array', img_array)
# cv2.waitKey(0)
# in-place encoding with default settings.
buffer_size = jpeg.buffer_size(img_array)
dest_buf = bytearray(buffer_size)
result, n_byte = jpeg.encode(img_array, dst=dest_buf)
# return value is the dest_buf argument value
id(result) == id(dest_buf)
out_file = open('output.jpg', 'wb')
out_file.write(dest_buf[:n_byte])
out_file.close()
# using PyTurboJPEG with ExifRead to transpose an image if the image has an EXIF Orientation tag.
#
# pip install PyTurboJPEG -U
# pip install exifread -U
import cv2
import numpy as np
import exifread
from turbojpeg import TurboJPEG
def transposeImage(image, orientation):
"""See Orientation in https://www.exif.org/Exif2-2.PDF for details."""
if orientation == None: return image
val = orientation.values[0]
if val == 1: return image
elif val == 2: return np.fliplr(image)
elif val == 3: return np.rot90(image, 2)
elif val == 4: return np.flipud(image)
elif val == 5: return np.rot90(np.flipud(image), -1)
elif val == 6: return np.rot90(image, -1)
elif val == 7: return np.rot90(np.flipud(image))
elif val == 8: return np.rot90(image)
# using default library installation
turbo_jpeg = TurboJPEG()
# open jpeg file
in_file = open('foobar.jpg', 'rb')
# parse orientation
orientation = exifread.process_file(in_file).get('Image Orientation', None)
# seek file position back to 0 before decoding JPEG image
in_file.seek(0)
# start to decode the JPEG file
image = turbo_jpeg.decode(in_file.read())
# transpose image based on EXIF Orientation tag
transposed_image = transposeImage(image, orientation)
# close the file since it's no longer needed.
in_file.close()
cv2.imshow('transposed_image', transposed_image)
cv2.waitKey(0)
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4