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        "description": "# PyTurboJPEG\nA Python wrapper of libjpeg-turbo for decoding and encoding JPEG image.\n\n## Prerequisites\n- [libjpeg-turbo](https://github.com/libjpeg-turbo/libjpeg-turbo/releases)\n- [numpy](https://github.com/numpy/numpy)\n\n## Example\n\n```python\nimport cv2\nfrom turbojpeg import TurboJPEG, TJPF_GRAY, TJSAMP_GRAY, TJFLAG_PROGRESSIVE, TJFLAG_FASTUPSAMPLE, TJFLAG_FASTDCT\n\n# specifying library path explicitly\n# jpeg = TurboJPEG(r'D:\\turbojpeg.dll')\n# jpeg = TurboJPEG('/usr/lib64/libturbojpeg.so')\n# jpeg = TurboJPEG('/usr/local/lib/libturbojpeg.dylib')\n\n# using default library installation\njpeg = TurboJPEG()\n\n# decoding input.jpg to BGR array\nin_file = open('input.jpg', 'rb')\nbgr_array = jpeg.decode(in_file.read())\nin_file.close()\ncv2.imshow('bgr_array', bgr_array)\ncv2.waitKey(0)\n\n# decoding input.jpg to BGR array with fast upsample and fast DCT. (i.e. fastest speed but lower accuracy)\nin_file = open('input.jpg', 'rb')\nbgr_array = jpeg.decode(in_file.read(), flags=TJFLAG_FASTUPSAMPLE|TJFLAG_FASTDCT)\nin_file.close()\ncv2.imshow('bgr_array', bgr_array)\ncv2.waitKey(0)\n\n# direct rescaling 1/2 while decoding input.jpg to BGR array\nin_file = open('input.jpg', 'rb')\nbgr_array_half = jpeg.decode(in_file.read(), scaling_factor=(1, 2))\nin_file.close()\ncv2.imshow('bgr_array_half', bgr_array_half)\ncv2.waitKey(0)\n\n# getting possible scaling factors for direct rescaling\nscaling_factors = jpeg.scaling_factors\n\n# decoding JPEG image properties\nin_file = open('input.jpg', 'rb')\nwidth, height, jpeg_subsample, jpeg_colorspace = jpeg.decode_header(in_file.read())\nin_file.close()\n\n# decoding input.jpg to YUV array\nin_file = open('input.jpg', 'rb')\nbuffer_array, plane_sizes = jpeg.decode_to_yuv(in_file.read())\nin_file.close()\n\n# decoding input.jpg to YUV planes\nin_file = open('input.jpg', 'rb')\nplanes = jpeg.decode_to_yuv_planes(in_file.read())\nin_file.close()\n\n# encoding BGR array to output.jpg with default settings.\nout_file = open('output.jpg', 'wb')\nout_file.write(jpeg.encode(bgr_array))\nout_file.close()\n\n# encoding BGR array to output.jpg with TJSAMP_GRAY subsample.\nout_file = open('output_gray.jpg', 'wb')\nout_file.write(jpeg.encode(bgr_array, jpeg_subsample=TJSAMP_GRAY))\nout_file.close()\n\n# encoding BGR array to output.jpg with quality level 50. \nout_file = open('output_quality_50.jpg', 'wb')\nout_file.write(jpeg.encode(bgr_array, quality=50))\nout_file.close()\n\n# encoding BGR array to output.jpg with quality level 100 and progressive entropy coding.\nout_file = open('output_quality_100_progressive.jpg', 'wb')\nout_file.write(jpeg.encode(bgr_array, quality=100, flags=TJFLAG_PROGRESSIVE))\nout_file.close()\n\n# decoding input.jpg to grayscale array\nin_file = open('input.jpg', 'rb')\ngray_array = jpeg.decode(in_file.read(), pixel_format=TJPF_GRAY)\nin_file.close()\ncv2.imshow('gray_array', gray_array)\ncv2.waitKey(0)\n\n# scale with quality but leaves out the color conversion step\nin_file = open('input.jpg', 'rb')\nout_file = open('scaled_output.jpg', 'wb')\nout_file.write(jpeg.scale_with_quality(in_file.read(), scaling_factor=(1, 4), quality=70))\nout_file.close()\nin_file.close()\n\n# lossless crop image\nout_file = open('lossless_cropped_output.jpg', 'wb')\nout_file.write(jpeg.crop(open('input.jpg', 'rb').read(), 8, 8, 320, 240))\nout_file.close()\n```\n\n```python\n# using PyTurboJPEG with ExifRead to transpose an image if the image has an EXIF Orientation tag.\n#\n# pip install PyTurboJPEG -U\n# pip install exifread -U\n\nimport cv2\nimport numpy as np\nimport exifread\nfrom turbojpeg import TurboJPEG\n\ndef transposeImage(image, orientation):\n    \"\"\"See Orientation in https://www.exif.org/Exif2-2.PDF for details.\"\"\"\n    if orientation == None: return image\n    val = orientation.values[0]\n    if val == 1: return image\n    elif val == 2: return np.fliplr(image)\n    elif val == 3: return np.rot90(image, 2)\n    elif val == 4: return np.flipud(image)\n    elif val == 5: return np.rot90(np.flipud(image), -1)\n    elif val == 6: return np.rot90(image, -1)\n    elif val == 7: return np.rot90(np.flipud(image))\n    elif val == 8: return np.rot90(image)\n\n# using default library installation\nturbo_jpeg = TurboJPEG()\n# open jpeg file\nin_file = open('foobar.jpg', 'rb')\n# parse orientation\norientation = exifread.process_file(in_file).get('Image Orientation', None)\n# seek file position back to 0 before decoding JPEG image\nin_file.seek(0)\n# start to decode the JPEG file\nimage = turbo_jpeg.decode(in_file.read())\n# transpose image based on EXIF Orientation tag\ntransposed_image = transposeImage(image, orientation)\n# close the file since it's no longer needed.\nin_file.close()\n\ncv2.imshow('transposed_image', transposed_image)\ncv2.waitKey(0)\n```\n\n## Installation\n\n### macOS\n- brew install jpeg-turbo\n- pip install -U git+git://github.com/lilohuang/PyTurboJPEG.git\n\n### Windows \n- Download [libjpeg-turbo official installer](https://sourceforge.net/projects/libjpeg-turbo/files) \n- pip install -U git+git://github.com/lilohuang/PyTurboJPEG.git\n\n### Linux\n- RHEL/CentOS/Fedora\n  - Download [libjpeg-turbo.repo](https://libjpeg-turbo.org/pmwiki/uploads/Downloads/libjpeg-turbo.repo) to /etc/yum.repos.d/\n  - sudo yum install libjpeg-turbo-official\n  - pip install -U git+git://github.com/lilohuang/PyTurboJPEG.git\n\n- Ubuntu\n  - sudo apt-get update\n  - sudo apt-get install libturbojpeg\n  - pip install -U git+git://github.com/lilohuang/PyTurboJPEG.git\n\n## Benchmark \n\n### macOS\n- macOS Sierra 10.12.6\n- Intel(R) Core(TM) i5-3210M CPU @ 2.50GHz\n- opencv-python 3.4.0.12 (pre-built)\n- turbo-jpeg 1.5.3 (pre-built)\n\n| Function              | Wall-clock time |\n| ----------------------|-----------------|\n| cv2.imdecode() \u00a0 \u00a0 \u00a0  |   0.528 sec \u00a0   |\n| TurboJPEG.decode()    |   0.191 sec     |\n| cv2.imencode() \u00a0 \u00a0 \u00a0  |   0.875 sec \u00a0   |\n| TurboJPEG.encode()    |   0.176 sec     |\n\n### Windows \n- Windows 7 Ultimate 64-bit\n- Intel(R) Xeon(R) E3-1276 v3 CPU @ 3.60 GHz\n- opencv-python 3.4.0.12 (pre-built)\n- turbo-jpeg 1.5.3 (pre-built)\n\n| Function              | Wall-clock time |\n| ----------------------|-----------------|\n| cv2.imdecode() \u00a0 \u00a0 \u00a0  |   0.358 sec \u00a0   |\n| TurboJPEG.decode()    |   0.135 sec     |\n| cv2.imencode() \u00a0 \u00a0 \u00a0  |   0.581 sec \u00a0   |\n| TurboJPEG.encode()    |   0.140 sec     |\n\n\n",
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