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()

# 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)