├── .gitignore ├── CHANGELOG ├── CNAME ├── LICENSE ├── README.md ├── _config.yml ├── deepdreamer.py ├── deepdreamer ├── __init__.py ├── deepdreamer.py └── images2gif.py └── requirements.txt /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | 5 | # C extensions 6 | *.so 7 | 8 | # Distribution / packaging 9 | .Python 10 | env/ 11 | venv/ 12 | build/ 13 | develop-eggs/ 14 | dist/ 15 | downloads/ 16 | eggs/ 17 | .eggs/ 18 | lib/ 19 | lib64/ 20 | parts/ 21 | sdist/ 22 | var/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | 27 | # PyInstaller 28 | # Usually these files are written by a python script from a template 29 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 30 | *.manifest 31 | *.spec 32 | 33 | # Installer logs 34 | pip-log.txt 35 | pip-delete-this-directory.txt 36 | 37 | # Unit test / coverage reports 38 | htmlcov/ 39 | .tox/ 40 | .coverage 41 | .coverage.* 42 | .cache 43 | nosetests.xml 44 | coverage.xml 45 | *,cover 46 | 47 | # Translations 48 | *.mo 49 | *.pot 50 | 51 | # Django stuff: 52 | *.log 53 | 54 | # Sphinx documentation 55 | docs/_build/ 56 | 57 | # PyBuilder 58 | target/ 59 | *.caffemodel 60 | *.prototxt 61 | *.protxt 62 | log.txt 63 | *.jpg 64 | *.jpeg 65 | -------------------------------------------------------------------------------- /CHANGELOG: -------------------------------------------------------------------------------- 1 | Changelog 2 | ========= 3 | v0.0.2a, 04/11/2017 4 | * Changed the script to use Python 3. 5 | * Works with Caffe 1.0.0. 6 | 7 | v0.0.1a, historical entry 8 | * Initial release in 2015. 9 | -------------------------------------------------------------------------------- /CNAME: -------------------------------------------------------------------------------- 1 | deepdreamer.fq.nz -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | {one line to give the program's name and a brief idea of what it does.} 635 | Copyright (C) {year} {name of author} 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | {project} Copyright (C) {year} {fullname} 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | 676 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # [Deep Dreamer](https://deepdreamer.fq.nz/) 2 | Easy to configure Python program that make use of [Google's DeepDream](https://github.com/google/deepdream/) 3 | 4 | * [Requirements](#requirements) 5 | * [Installation](#installation) 6 | * [Usage](#usage) 7 | * [Configuration options](#configuration-options) 8 | * [Examples](#examples) 9 | 10 | ## Requirements 11 | * Python 3 12 | * [NumPy](https://pypi.python.org/pypi/numpy) 13 | * [SciPy](https://pypi.python.org/pypi/scipy/) 14 | * [Pillow](https://pypi.python.org/pypi/Pillow/) 15 | * [Caffe](http://caffe.berkeleyvision.org/) 16 | * [FFmpeg](https://www.ffmpeg.org/) (Optional, required for videos.) 17 | 18 | ## Installation 19 | 1. Install [NumPy](https://pypi.python.org/pypi/numpy), [SciPy](https://pypi.python.org/pypi/scipy/), [Pillow](https://pypi.python.org/pypi/Pillow/) and [Caffe](http://caffe.berkeleyvision.org/). *On Ubuntu 17.10 installing caffe will usually install all other dependencies.* 20 | 2. Clone this project. `git clone https://github.com/kesara/deepdreamer.git` 21 | 3. Go to project directory. `cd deepdeamer` 22 | 4. Download **deploy.prototxt** from [bvlc_googlenet](https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet) into the project directory. 23 | 5. Add line `force_backward: true` to **deploy.prototxt** file. 24 | 6. Download **bvlc_googlenet.caffemodel** from [bvlc_googlenet](https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet) into the project directory. 25 | 7. (Optional) Download MIT's "Places" neural net, download the **Places205-GoogLeNet** from [their website](http://places.csail.mit.edu/downloadCNN.html). You need the **deploy_places205.protxt** and **googlelet_places205_train_iter_2400000.caffemodel** files from the archive. 26 | 27 | ## Usage 28 | * Just deep dreaming 29 | `python3 deepdreamer.py image.jpg` 30 | * Create a deepdream gif 31 | `python3 deepdreamer.py --gif true image.jpg` 32 | * Create a deepdream video (requires ffmpeg) 33 | `python3 deepdreamer.py --video video.mp4` 34 | 35 | ## Configuration options 36 | ``` 37 | usage: deepdreamer.py [-h] [--zoom {true,false}] [--scale SCALE] 38 | [--dreams DREAMS] [--itern ITERN] [--octaves OCTAVES] 39 | [--octave-scale OCTAVE_SCALE] [--layers LAYERS] 40 | [--clip {true,false}] [--gpuid GPUID] 41 | [--network {bvlc_googlenet,googlenet_place205}] 42 | [--gif {true,false}] [--reverse {true,false}] 43 | [--duration DURATION] [--loop {true,false}] 44 | [--framerate FRAMERATE] [--list-layers] [--video VIDEO] 45 | [image] 46 | 47 | positional arguments: 48 | image 49 | 50 | optional arguments: 51 | -h, --help show this help message and exit 52 | --gpuid GPUID enable GPU with id GPUID (default: disabled) 53 | --zoom {true,false} zoom dreams (default: true) 54 | --scale SCALE scale coefficient for zoom (default: 0.05) 55 | --dreams DREAMS number of images (default: 100) 56 | --itern ITERN dream iterations (default: 10) 57 | --octaves OCTAVES dream octaves (default: 4) 58 | --octave-scale OCTAVE_SCALE 59 | dream octave scale (default: 1.4) 60 | --layers LAYERS dream layers (default: inception_4c/output) 61 | --clip {true,false} clip dreams (default: true) 62 | --network {bvlc_googlenet,googlenet_place205} 63 | choose the network to use (default: bvlc_googlenet) 64 | --gif {true,false} make a gif (default: false) 65 | --reverse {true,false} 66 | make a reverse gif (default: false) 67 | --duration DURATION gif frame duration in seconds (default: 0.1) 68 | --loop {true,false} enable gif loop (default: false) 69 | --framerate FRAMERATE 70 | framerate for video (default: 24) 71 | --list-layers list layers 72 | --video VIDEO video file 73 | ``` 74 | 75 | ## Examples 76 | ![Deepdream](https://i.imgur.com/Auikelk.jpg) 77 | ![Deepdream](https://i.imgur.com/Ox1B8wf.gif) 78 | ![Deepdream](https://i.imgur.com/llUZ7Ll.gif) 79 | ![Deepdream](https://i.imgur.com/41GVLNC.gif) 80 | -------------------------------------------------------------------------------- /_config.yml: -------------------------------------------------------------------------------- 1 | theme: jekyll-theme-cayman -------------------------------------------------------------------------------- /deepdreamer.py: -------------------------------------------------------------------------------- 1 | ############################################################################### 2 | # Deep Dreamer 3 | # Author: Kesara Rathnayake ( kesara [at] kesara [dot] lk ) 4 | ############################################################################### 5 | 6 | from argparse import ArgumentParser 7 | import sys 8 | 9 | from deepdreamer.deepdreamer import deepdream, deepdream_video, list_layers 10 | 11 | 12 | def main(): 13 | try: 14 | parser = ArgumentParser(description="Deep dreamer") 15 | parser.add_argument( 16 | "--zoom", choices=["true", "false"], default="true", 17 | help="zoom dreams (default: true)") 18 | parser.add_argument( 19 | "--scale", type=float, default=0.05, 20 | help="scale coefficient for zoom (default: 0.05)") 21 | parser.add_argument( 22 | "--dreams", type=int, default=100, 23 | help="number of images (default: 100)") 24 | parser.add_argument( 25 | "--itern", type=int, default=10, 26 | help="dream iterations (default: 10)") 27 | parser.add_argument( 28 | "--octaves", type=int, default=4, 29 | help="dream octaves (default: 4)") 30 | parser.add_argument( 31 | "--octave-scale", type=float, default=1.4, 32 | help="dream octave scale (default: 1.4)") 33 | parser.add_argument( 34 | "--layers", type=str, default="inception_4c/output", 35 | help="dream layers (default: inception_4c/output)") 36 | parser.add_argument( 37 | "--clip", choices=["true", "false"], default="true", 38 | help="clip dreams (default: true)") 39 | parser.add_argument( 40 | "--network", choices=['bvlc_googlenet', 'googlenet_place205'], 41 | default='bvlc_googlenet', 42 | help="choose the network to use (default: bvlc_googlenet)") 43 | parser.add_argument( 44 | "--gif", choices=["true", "false"], default="false", 45 | help="make a gif (default: false)") 46 | parser.add_argument( 47 | "--reverse", choices=["true", "false"], default="false", 48 | help="make a reverse gif (default: false)") 49 | parser.add_argument( 50 | "--duration", type=float, default=0.1, 51 | help="gif frame duration in seconds (default: 0.1)") 52 | parser.add_argument( 53 | "--loop", choices=["true", "false"], default="false", 54 | help="enable gif loop (default: false)") 55 | parser.add_argument( 56 | "--framerate", type=int, default=24, 57 | help="framerate for video (default: 24)") 58 | parser.add_argument( 59 | "--gpuid", type=int, default=-1, 60 | help="enable GPU with id GPUID (default: disabled)") 61 | group = parser.add_mutually_exclusive_group(required=True) 62 | group.add_argument("image", nargs="?") 63 | group.add_argument( 64 | "--list-layers", action="store_true", help="list layers") 65 | group.add_argument( 66 | "--video", type=str, help="video file") 67 | args = parser.parse_args() 68 | if args.list_layers: 69 | list_layers(network=args.network) 70 | elif args.video: 71 | clip = True 72 | if args.clip == "false": 73 | clip = False 74 | deepdream_video( 75 | args.video, iter_n=args.itern, octave_n=args.octaves, 76 | octave_scale=args.octave_scale, end=args.layers, clip=clip, 77 | network=args.network, frame_rate=args.framerate) 78 | else: 79 | zoom = True 80 | if args.zoom == "false": 81 | zoom = False 82 | clip = True 83 | if args.clip == "false": 84 | clip = False 85 | gif = False 86 | if args.gif == "true": 87 | gif = True 88 | reverse = False 89 | if args.reverse == "true": 90 | reverse = True 91 | loop = False 92 | if args.loop == "true": 93 | loop = True 94 | gpu = False 95 | if args.gpuid >= 0: 96 | gpu = True 97 | deepdream( 98 | args.image, zoom=zoom, scale_coefficient=args.scale, 99 | irange=args.dreams, iter_n=args.itern, octave_n=args.octaves, 100 | octave_scale=args.octave_scale, end=args.layers, clip=clip, 101 | network=args.network, gif=gif, reverse=reverse, 102 | duration=args.duration, loop=loop, gpu=gpu, gpuid=args.gpuid) 103 | except Exception as e: 104 | print("Error: {}".format(e)) 105 | sys.exit(2) 106 | 107 | 108 | if __name__ == "__main__": 109 | main() 110 | -------------------------------------------------------------------------------- /deepdreamer/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kesara/deepdreamer/9e1a3deebb677878a12c6c25178a78a8be230556/deepdreamer/__init__.py -------------------------------------------------------------------------------- /deepdreamer/deepdreamer.py: -------------------------------------------------------------------------------- 1 | ############################################################################### 2 | # Deep Dreamer 3 | # Based on https://github.com/google/deepdream/blob/master/dream.ipynb 4 | # Author: Kesara Rathnayake ( kesara [at] kesara [dot] lk ) 5 | ############################################################################### 6 | 7 | from os import mkdir, listdir 8 | from subprocess import PIPE, Popen 9 | 10 | import numpy as np 11 | from caffe import Classifier, set_device, set_mode_gpu 12 | from deepdreamer.images2gif import writeGif 13 | from scipy.ndimage import affine_transform, zoom 14 | from PIL.Image import fromarray as img_fromarray, open as img_open 15 | import logging 16 | 17 | logging.basicConfig( 18 | filename='log.txt', 19 | format='%(asctime)s %(message)s', 20 | datefmt='%m/%d/%Y %I:%M:%S %p', 21 | level=logging.NOTSET) 22 | 23 | 24 | def _select_network(netname): 25 | if netname == 'bvlc_googlenet': 26 | NET_FN = "deploy.prototxt" # Make sure force_backward: true 27 | PARAM_FN = "bvlc_googlenet.caffemodel" 28 | CHANNEL_SWAP = (2, 1, 0) 29 | # ImageNet mean, training set dependent 30 | CAFFE_MEAN = np.float32([104.0, 116.0, 122.0]) 31 | return NET_FN, PARAM_FN, CHANNEL_SWAP, CAFFE_MEAN 32 | elif netname == 'googlenet_place205': 33 | # TODO: refit SWAP and MEAN for places205? These work for now. 34 | NET_FN = "deploy_places205.protxt" # Make sure force_backward: true 35 | PARAM_FN = "googlelet_places205_train_iter_2400000.caffemodel" 36 | CHANNEL_SWAP = (2, 1, 0) 37 | # ImageNet mean, training set dependent 38 | CAFFE_MEAN = np.float32([104.0, 116.0, 122.0]) 39 | return NET_FN, PARAM_FN, CHANNEL_SWAP, CAFFE_MEAN 40 | else: 41 | print("Error: network {} not implemented".format(netname)) 42 | 43 | 44 | def _preprocess(net, img): 45 | return np.float32(np.rollaxis(img, 2)[::-1]) - net.transformer.mean["data"] 46 | 47 | 48 | def _deprocess(net, img): 49 | return np.dstack((img + net.transformer.mean["data"])[::-1]) 50 | 51 | 52 | def _make_step( 53 | net, step_size=1.5, end="inception_4c/output", jitter=32, clip=True): 54 | """ Basic gradient ascent step. """ 55 | 56 | src = net.blobs["data"] 57 | dst = net.blobs[end] 58 | 59 | ox, oy = np.random.randint(-jitter, jitter+1, 2) 60 | 61 | # apply jitter shift 62 | src.data[0] = np.roll(np.roll(src.data[0], ox, -1), oy, -2) 63 | 64 | net.forward(end=end) 65 | dst.diff[:] = dst.data # specify the optimization objective 66 | net.backward(start=end) 67 | g = src.diff[0] 68 | # apply normalized ascent step to the input image 69 | src.data[:] += step_size/np.abs(g).mean() * g 70 | # unshift image 71 | src.data[0] = np.roll(np.roll(src.data[0], -ox, -1), -oy, -2) 72 | 73 | if clip: 74 | bias = net.transformer.mean["data"] 75 | src.data[:] = np.clip(src.data, -bias, 255-bias) 76 | 77 | 78 | def _deepdream( 79 | net, base_img, iter_n=10, octave_n=4, octave_scale=1.4, 80 | end="inception_4c/output", clip=True, **step_params): 81 | # prepare base images for all octaves 82 | octaves = [_preprocess(net, base_img)] 83 | 84 | for i in range(octave_n-1): 85 | octaves.append(zoom( 86 | octaves[-1], (1, 1.0/octave_scale, 1.0/octave_scale), order=1)) 87 | 88 | src = net.blobs["data"] 89 | 90 | # allocate image for network-produced details 91 | detail = np.zeros_like(octaves[-1]) 92 | 93 | for octave, octave_base in enumerate(octaves[::-1]): 94 | h, w = octave_base.shape[-2:] 95 | if octave > 0: 96 | # upscale details from the previous octave 97 | h1, w1 = detail.shape[-2:] 98 | detail = zoom(detail, (1, 1.0*h/h1, 1.0*w/w1), order=1) 99 | 100 | src.reshape(1, 3, h, w) # resize the network's input image size 101 | src.data[0] = octave_base+detail 102 | 103 | for i in range(iter_n): 104 | _make_step(net, end=end, clip=clip, **step_params) 105 | 106 | # visualization 107 | vis = _deprocess(net, src.data[0]) 108 | if not clip: # adjust image contrast if clipping is disabled 109 | vis = vis*(255.0/np.percentile(vis, 99.98)) 110 | 111 | # extract details produced on the current octave 112 | detail = src.data[0]-octave_base 113 | 114 | # returning the resulting image 115 | return _deprocess(net, src.data[0]) 116 | 117 | 118 | def _output_video_dir(video): 119 | return "{}_images".format(video) 120 | 121 | 122 | def _extract_video(video): 123 | output_dir = _output_video_dir(video) 124 | mkdir(output_dir) 125 | output = Popen( 126 | "ffmpeg -loglevel quiet -i {} -f image2 {}/img_%4d.jpg".format( 127 | video, output_dir), shell=True, stdout=PIPE).stdout.read() 128 | 129 | 130 | def _create_video(video, frame_rate=24): 131 | output_dir = _output_video_dir(video) 132 | output = Popen(( 133 | "ffmpeg -loglevel quiet -r {} -f image2 -pattern_type glob " 134 | "-i \"{}/img_*.jpg\" {}.mp4").format( 135 | frame_rate, output_dir, video), 136 | shell=True, stdout=PIPE).stdout.read() 137 | 138 | 139 | def list_layers(network="bvlc_googlenet"): 140 | # Load DNN model 141 | NET_FN, PARAM_FN, CHANNEL_SWAP, CAFFE_MEAN = _select_network(network) 142 | net = Classifier( 143 | NET_FN, PARAM_FN, mean=CAFFE_MEAN, channel_swap=CHANNEL_SWAP) 144 | net.blobs.keys() 145 | 146 | 147 | def deepdream( 148 | img_path, zoom=True, scale_coefficient=0.05, irange=100, iter_n=10, 149 | octave_n=4, octave_scale=1.4, end="inception_4c/output", clip=True, 150 | network="bvlc_googlenet", gif=False, reverse=False, duration=0.1, 151 | loop=False, gpu=False, gpuid=0): 152 | img = np.float32(img_open(img_path)) 153 | s = scale_coefficient 154 | h, w = img.shape[:2] 155 | 156 | if gpu: 157 | print("Enabling GPU {}...".format(gpuid)) 158 | set_device(gpuid) 159 | set_mode_gpu() 160 | 161 | # Select, load DNN model 162 | NET_FN, PARAM_FN, CHANNEL_SWAP, CAFFE_MEAN = _select_network(network) 163 | net = Classifier( 164 | NET_FN, PARAM_FN, mean=CAFFE_MEAN, channel_swap=CHANNEL_SWAP) 165 | 166 | img_pool = [img_path] 167 | 168 | # Save settings used in a log file 169 | logging.info(( 170 | "{} zoom={}, scale_coefficient={}, irange={}, iter_n={}, " 171 | "octave_n={}, octave_scale={}, end={}, clip={}, network={}, gif={}, " 172 | "reverse={}, duration={}, loop={}").format( 173 | img_path, zoom, scale_coefficient, irange, iter_n, octave_n, 174 | octave_scale, end, clip, network, gif, reverse, duration, loop)) 175 | 176 | print("Dreaming...") 177 | for i in range(irange): 178 | img = _deepdream( 179 | net, img, iter_n=iter_n, octave_n=octave_n, 180 | octave_scale=octave_scale, end=end, clip=clip) 181 | img_fromarray(np.uint8(img)).save("{}_{}.jpg".format( 182 | img_path, i)) 183 | if gif: 184 | img_pool.append("{}_{}.jpg".format(img_path, i)) 185 | print("Dream {} saved.".format(i)) 186 | if zoom: 187 | img = affine_transform( 188 | img, [1-s, 1-s, 1], [h*s/2, w*s/2, 0], order=1) 189 | if gif: 190 | print("Creating gif...") 191 | frames = None 192 | if reverse: 193 | frames = [img_open(f) for f in img_pool[::-1]] 194 | else: 195 | frames = [img_open(f) for f in img_pool] 196 | writeGif( 197 | "{}.gif".format(img_path), frames, duration=duration, 198 | repeat=loop) 199 | print("gif created.") 200 | 201 | 202 | def deepdream_video( 203 | video, iter_n=10, octave_n=4, octave_scale=1.4, 204 | end="inception_4c/output", clip=True, network="bvlc_googlenet", 205 | frame_rate=24): 206 | 207 | # Select, load DNN model 208 | NET_FN, PARAM_FN, CHANNEL_SWAP, CAFFE_MEAN = _select_network(network) 209 | net = Classifier( 210 | NET_FN, PARAM_FN, mean=CAFFE_MEAN, channel_swap=CHANNEL_SWAP) 211 | 212 | print("Extracting video...") 213 | _extract_video(video) 214 | 215 | output_dir = _output_video_dir(video) 216 | images = listdir(output_dir) 217 | 218 | print("Dreaming...") 219 | for image in images: 220 | image = "{}/{}".format(output_dir, image) 221 | img = np.float32(img_open(image)) 222 | img = _deepdream( 223 | net, img, iter_n=iter_n, octave_n=octave_n, 224 | octave_scale=octave_scale, end=end, clip=clip) 225 | img_fromarray(np.uint8(img)).save(image) 226 | 227 | print("Creating dream video...") 228 | _create_video(video, frame_rate) 229 | print("Dream video created.") 230 | -------------------------------------------------------------------------------- /deepdreamer/images2gif.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # Copyright (C) 2012, Almar Klein, Ant1, Marius van Voorden 3 | # 4 | # This code is subject to the (new) BSD license: 5 | # 6 | # Redistribution and use in source and binary forms, with or without 7 | # modification, are permitted provided that the following conditions are met: 8 | # * Redistributions of source code must retain the above copyright 9 | # notice, this list of conditions and the following disclaimer. 10 | # * Redistributions in binary form must reproduce the above copyright 11 | # notice, this list of conditions and the following disclaimer in the 12 | # documentation and/or other materials provided with the distribution. 13 | # * Neither the name of the nor the 14 | # names of its contributors may be used to endorse or promote products 15 | # derived from this software without specific prior written permission. 16 | # 17 | # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 18 | # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 19 | # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE 20 | # ARE DISCLAIMED. IN NO EVENT SHALL BE LIABLE FOR ANY 21 | # DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES 22 | # (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; 23 | # LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND 24 | # ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT 25 | # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS 26 | # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 27 | 28 | """ Module images2gif 29 | 30 | Provides functionality for reading and writing animated GIF images. 31 | Use writeGif to write a series of numpy arrays or PIL images as an 32 | animated GIF. Use readGif to read an animated gif as a series of numpy 33 | arrays. 34 | 35 | Note that since July 2004, all patents on the LZW compression patent have 36 | expired. Therefore the GIF format may now be used freely. 37 | 38 | Acknowledgements 39 | ---------------- 40 | 41 | Many thanks to Ant1 for: 42 | * noting the use of "palette=PIL.Image.ADAPTIVE", which significantly 43 | improves the results. 44 | * the modifications to save each image with its own palette, or optionally 45 | the global palette (if its the same). 46 | 47 | Many thanks to Marius van Voorden for porting the NeuQuant quantization 48 | algorithm of Anthony Dekker to Python (See the NeuQuant class for its 49 | license). 50 | 51 | Many thanks to Alex Robinson for implementing the concept of subrectangles, 52 | which (depening on image content) can give a very significant reduction in 53 | file size. 54 | 55 | This code is based on gifmaker (in the scripts folder of the source 56 | distribution of PIL) 57 | 58 | 59 | Usefull links 60 | ------------- 61 | * http://tronche.com/computer-graphics/gif/ 62 | * http://en.wikipedia.org/wiki/Graphics_Interchange_Format 63 | * http://www.w3.org/Graphics/GIF/spec-gif89a.txt 64 | 65 | """ 66 | # todo: This module should be part of imageio (or at least based on) 67 | 68 | import os 69 | import time 70 | 71 | try: 72 | import PIL 73 | from PIL import Image 74 | from PIL.GifImagePlugin import getheader, getdata 75 | except ImportError: 76 | PIL = None 77 | 78 | try: 79 | import numpy as np 80 | except ImportError: 81 | np = None 82 | 83 | 84 | def get_cKDTree(): 85 | try: 86 | from scipy.spatial import cKDTree 87 | except ImportError: 88 | cKDTree = None 89 | return cKDTree 90 | 91 | 92 | # getheader gives a 87a header and a color palette (two elements in a list). 93 | # getdata()[0] gives the Image Descriptor up to (including) "LZW min code size" 94 | # getdatas()[1:] is the image data itself in chuncks of 256 bytes (well 95 | # technically the first byte says how many bytes follow, after which that 96 | # amount (max 255) follows). 97 | 98 | def checkImages(images): 99 | """ checkImages(images) 100 | Check numpy images and correct intensity range etc. 101 | The same for all movie formats. 102 | """ 103 | # Init results 104 | images2 = [] 105 | 106 | for im in images: 107 | if PIL and isinstance(im, PIL.Image.Image): 108 | # We assume PIL images are allright 109 | images2.append(im) 110 | 111 | elif np and isinstance(im, np.ndarray): 112 | # Check and convert dtype 113 | if im.dtype == np.uint8: 114 | images2.append(im) # Ok 115 | elif im.dtype in [np.float32, np.float64]: 116 | im = im.copy() 117 | im[im < 0] = 0 118 | im[im > 1] = 1 119 | im *= 255 120 | images2.append(im.astype(np.uint8)) 121 | else: 122 | im = im.astype(np.uint8) 123 | images2.append(im) 124 | # Check size 125 | if im.ndim == 2: 126 | pass # ok 127 | elif im.ndim == 3: 128 | if im.shape[2] not in [3, 4]: 129 | raise ValueError('This array can not represent an image.') 130 | else: 131 | raise ValueError('This array can not represent an image.') 132 | else: 133 | raise ValueError('Invalid image type: ' + str(type(im))) 134 | 135 | # Done 136 | return images2 137 | 138 | 139 | def intToBin(i): 140 | """ Integer to two bytes """ 141 | # devide in two parts (bytes) 142 | i1 = i % 256 143 | i2 = int(i / 256) 144 | # make string (little endian) 145 | return chr(i1) + chr(i2) 146 | 147 | 148 | class GifWriter: 149 | 150 | """ GifWriter() 151 | 152 | Class that contains methods for helping write the animated GIF file. 153 | 154 | """ 155 | 156 | def getheaderAnim(self, im): 157 | """ getheaderAnim(im) 158 | 159 | Get animation header. To replace PILs getheader()[0] 160 | 161 | """ 162 | bb = "GIF89a" 163 | bb += intToBin(im.size[0]) 164 | bb += intToBin(im.size[1]) 165 | bb += "\x87\x00\x00" 166 | return bb 167 | 168 | def getImageDescriptor(self, im, xy=None): 169 | """ getImageDescriptor(im, xy=None) 170 | 171 | Used for the local color table properties per image. 172 | Otherwise global color table applies to all frames irrespective of 173 | whether additional colors comes in play that require a redefined 174 | palette. Still a maximum of 256 color per frame, obviously. 175 | 176 | Written by Ant1 on 2010-08-22 177 | Modified by Alex Robinson in Janurari 2011 to implement subrectangles. 178 | 179 | """ 180 | 181 | # Defaule use full image and place at upper left 182 | if xy is None: 183 | xy = (0, 0) 184 | 185 | # Image separator, 186 | bb = '\x2C' 187 | 188 | # Image position and size 189 | bb += intToBin(xy[0]) # Left position 190 | bb += intToBin(xy[1]) # Top position 191 | bb += intToBin(im.size[0]) # image width 192 | bb += intToBin(im.size[1]) # image height 193 | 194 | # packed field: local color table flag1, interlace0, sorted table0, 195 | # reserved00, lct size111=7=2^(7+1)=256. 196 | bb += '\x87' 197 | 198 | # LZW minimum size code now comes later, begining of [image data] 199 | # blocks 200 | return bb 201 | 202 | def getAppExt(self, loops=float('inf')): 203 | """ getAppExt(loops=float('inf')) 204 | 205 | Application extention. This part specifies the amount of loops. 206 | If loops is 0 or inf, it goes on infinitely. 207 | 208 | """ 209 | 210 | if loops == 0 or loops == float('inf'): 211 | loops = 2**16 - 1 212 | # bb = "" # application extension should not be used 213 | # (the extension interprets zero loops 214 | # to mean an infinite number of loops) 215 | # Mmm, does not seem to work 216 | if True: 217 | bb = "\x21\xFF\x0B" # application extension 218 | bb += "NETSCAPE2.0" 219 | bb += "\x03\x01" 220 | bb += intToBin(loops) 221 | bb += '\x00' # end 222 | return bb 223 | 224 | def getGraphicsControlExt( 225 | self, duration=0.1, dispose=2, transparent_flag=0, 226 | transparency_index=0): 227 | """ getGraphicsControlExt(duration=0.1, dispose=2) 228 | 229 | Graphics Control Extension. A sort of header at the start of 230 | each image. Specifies duration and transparancy. 231 | 232 | Dispose 233 | ------- 234 | * 0 - No disposal specified. 235 | * 1 - Do not dispose. The graphic is to be left in place. 236 | * 2 - Restore to background color. The area used by the graphic 237 | must be restored to the background color. 238 | * 3 - Restore to previous. The decoder is required to restore the 239 | area overwritten by the graphic with what was there prior to 240 | rendering the graphic. 241 | * 4-7 -To be defined. 242 | 243 | """ 244 | 245 | bb = '\x21\xF9\x04' 246 | # low bit 1 == transparency, 247 | bb += chr(((dispose & 3) << 2) | (transparent_flag & 1)) 248 | # 2nd bit 1 == user input , next 3 bits, the low two of which are used, 249 | # are dispose. 250 | bb += intToBin(int(duration * 100)) # in 100th of seconds 251 | bb += chr(transparency_index) # transparency index 252 | bb += '\x00' # end 253 | return bb 254 | 255 | def handleSubRectangles(self, images, subRectangles): 256 | """ handleSubRectangles(images) 257 | 258 | Handle the sub-rectangle stuff. If the rectangles are given by the 259 | user, the values are checked. Otherwise the subrectangles are 260 | calculated automatically. 261 | 262 | """ 263 | image_info = [im.info for im in images] 264 | if isinstance(subRectangles, (tuple, list)): 265 | # xy given directly 266 | 267 | # Check xy 268 | xy = subRectangles 269 | if xy is None: 270 | xy = (0, 0) 271 | if hasattr(xy, '__len__'): 272 | if len(xy) == len(images): 273 | xy = [xxyy for xxyy in xy] 274 | else: 275 | raise ValueError("len(xy) doesn't match amount of images.") 276 | else: 277 | xy = [xy for im in images] 278 | xy[0] = (0, 0) 279 | 280 | else: 281 | # Calculate xy using some basic image processing 282 | 283 | # Check Numpy 284 | if np is None: 285 | raise RuntimeError("Need Numpy to use auto-subRectangles.") 286 | 287 | # First make numpy arrays if required 288 | for i in range(len(images)): 289 | im = images[i] 290 | if isinstance(im, Image.Image): 291 | tmp = im.convert() # Make without palette 292 | a = np.asarray(tmp) 293 | if len(a.shape) == 0: 294 | raise MemoryError( 295 | "Too little memory to convert PIL image to array") 296 | images[i] = a 297 | 298 | # Determine the sub rectangles 299 | images, xy = self.getSubRectangles(images) 300 | 301 | # Done 302 | return images, xy, image_info 303 | 304 | def getSubRectangles(self, ims): 305 | """ getSubRectangles(ims) 306 | 307 | Calculate the minimal rectangles that need updating each frame. 308 | Returns a two-element tuple containing the cropped images and a 309 | list of x-y positions. 310 | 311 | Calculating the subrectangles takes extra time, obviously. However, 312 | if the image sizes were reduced, the actual writing of the GIF 313 | goes faster. In some cases applying this method produces a GIF faster. 314 | 315 | """ 316 | 317 | # Check image count 318 | if len(ims) < 2: 319 | return ims, [(0, 0) for i in ims] 320 | 321 | # We need numpy 322 | if np is None: 323 | raise RuntimeError("Need Numpy to calculate sub-rectangles. ") 324 | 325 | # Prepare 326 | ims2 = [ims[0]] 327 | xy = [(0, 0)] 328 | t0 = time.time() 329 | 330 | # Iterate over images 331 | prev = ims[0] 332 | for im in ims[1:]: 333 | 334 | # Get difference, sum over colors 335 | diff = np.abs(im - prev) 336 | if diff.ndim == 3: 337 | diff = diff.sum(2) 338 | # Get begin and end for both dimensions 339 | X = np.argwhere(diff.sum(0)) 340 | Y = np.argwhere(diff.sum(1)) 341 | # Get rect coordinates 342 | if X.size and Y.size: 343 | x0, x1 = X[0], X[-1] + 1 344 | y0, y1 = Y[0], Y[-1] + 1 345 | else: # No change ... make it minimal 346 | x0, x1 = 0, 2 347 | y0, y1 = 0, 2 348 | 349 | # Cut out and store 350 | im2 = im[y0:y1, x0:x1] 351 | prev = im 352 | ims2.append(im2) 353 | xy.append((x0, y0)) 354 | 355 | # Done 356 | # print('%1.2f seconds to determine subrectangles of %i images' % 357 | # (time.time()-t0, len(ims2)) ) 358 | return ims2, xy 359 | 360 | def convertImagesToPIL(self, images, dither, nq=0, images_info=None): 361 | """ convertImagesToPIL(images, nq=0) 362 | 363 | Convert images to Paletted PIL images, which can then be 364 | written to a single animaged GIF. 365 | 366 | """ 367 | 368 | # Convert to PIL images 369 | images2 = [] 370 | for im in images: 371 | if isinstance(im, Image.Image): 372 | images2.append(im) 373 | elif np and isinstance(im, np.ndarray): 374 | if im.ndim == 3 and im.shape[2] == 3: 375 | im = Image.fromarray(im, 'RGB') 376 | elif im.ndim == 3 and im.shape[2] == 4: 377 | # im = Image.fromarray(im[:,:,:3],'RGB') 378 | self.transparency = True 379 | im = Image.fromarray(im[:, :, :4], 'RGBA') 380 | elif im.ndim == 2: 381 | im = Image.fromarray(im, 'L') 382 | images2.append(im) 383 | 384 | # Convert to paletted PIL images 385 | images, images2 = images2, [] 386 | if nq >= 1: 387 | # NeuQuant algorithm 388 | for im in images: 389 | im = im.convert("RGBA") # NQ assumes RGBA 390 | nqInstance = NeuQuant(im, int(nq)) # Learn colors from image 391 | if dither: 392 | im = im.convert("RGB").quantize( 393 | palette=nqInstance.paletteImage(), 394 | colors=255) 395 | else: 396 | im = nqInstance.quantize( 397 | im, 398 | colors=255) # Use to quantize the image itself 399 | 400 | self.transparency = True # since NQ assumes transparency 401 | if self.transparency: 402 | alpha = im.split()[3] 403 | mask = Image.eval(alpha, lambda a: 255 if a <= 128 else 0) 404 | im.paste(255, mask=mask) 405 | images2.append(im) 406 | else: 407 | # Adaptive PIL algorithm 408 | AD = Image.ADAPTIVE 409 | # for index,im in enumerate(images): 410 | for i in range(len(images)): 411 | im = images[i].convert('RGB').convert( 412 | 'P', 413 | palette=AD, 414 | dither=dither, 415 | colors=255) 416 | if self.transparency: 417 | alpha = images[i].split()[3] 418 | mask = Image.eval(alpha, lambda a: 255 if a <= 128 else 0) 419 | im.paste(255, mask=mask) 420 | images2.append(im) 421 | 422 | # Done 423 | return images2 424 | 425 | def writeGifToFile(self, fp, images, durations, loops, xys, disposes): 426 | """ writeGifToFile(fp, images, durations, loops, xys, disposes) 427 | 428 | Given a set of images writes the bytes to the specified stream. 429 | 430 | """ 431 | 432 | # Obtain palette for all images and count each occurance 433 | palettes, occur = [], [] 434 | for im in images: 435 | palettes.append(im.palette.getdata()[1]) 436 | for palette in palettes: 437 | occur.append(palettes.count(palette)) 438 | 439 | # Select most-used palette as the global one (or first in case no max) 440 | globalPalette = palettes[occur.index(max(occur))] 441 | 442 | # Init 443 | frames = 0 444 | firstFrame = True 445 | 446 | for im, palette in zip(images, palettes): 447 | 448 | if firstFrame: 449 | # Write header 450 | 451 | # Gather info 452 | header = self.getheaderAnim(im) 453 | appext = self.getAppExt(loops) 454 | 455 | # Write 456 | fp.write(header) 457 | fp.write(globalPalette) 458 | fp.write(appext) 459 | 460 | # Next frame is not the first 461 | firstFrame = False 462 | 463 | if True: 464 | # Write palette and image data 465 | 466 | # Gather info 467 | data = getdata(im) 468 | imdes, data = data[0], data[1:] 469 | 470 | transparent_flag = 0 471 | if self.transparency: 472 | transparent_flag = 1 473 | 474 | graphext = self.getGraphicsControlExt( 475 | durations[frames], disposes[frames], 476 | transparent_flag=transparent_flag, transparency_index=255) 477 | 478 | # Make image descriptor suitable for using 256 local color 479 | # palette 480 | lid = self.getImageDescriptor(im, xys[frames]) 481 | 482 | # Write local header 483 | if (palette != globalPalette) or (disposes[frames] != 2): 484 | # Use local color palette 485 | fp.write(graphext) 486 | fp.write(lid) # write suitable image descriptor 487 | fp.write(palette) # write local color table 488 | fp.write('\x08') # LZW minimum size code 489 | else: 490 | # Use global color palette 491 | fp.write(graphext) 492 | fp.write(imdes) # write suitable image descriptor 493 | 494 | # Write image data 495 | for d in data: 496 | fp.write(d) 497 | 498 | # Prepare for next round 499 | frames = frames + 1 500 | 501 | fp.write(";") # end gif 502 | return frames 503 | 504 | 505 | # Exposed functions 506 | 507 | def writeGif(filename, images, duration=0.1, repeat=True, dither=False, 508 | nq=0, subRectangles=True, dispose=None): 509 | """ writeGif(filename, images, duration=0.1, repeat=True, dither=False, 510 | nq=0, subRectangles=True, dispose=None) 511 | 512 | Write an animated gif from the specified images. 513 | 514 | Parameters 515 | ---------- 516 | filename : string 517 | The name of the file to write the image to. 518 | images : list 519 | Should be a list consisting of PIL images or numpy arrays. 520 | The latter should be between 0 and 255 for integer types, and 521 | between 0 and 1 for float types. 522 | duration : scalar or list of scalars 523 | The duration for all frames, or (if a list) for each frame. 524 | repeat : bool or integer 525 | The amount of loops. If True, loops infinitetely. 526 | dither : bool 527 | Whether to apply dithering 528 | nq : integer 529 | If nonzero, applies the NeuQuant quantization algorithm to create 530 | the color palette. This algorithm is superior, but slower than 531 | the standard PIL algorithm. The value of nq is the quality 532 | parameter. 1 represents the best quality. 10 is in general a 533 | good tradeoff between quality and speed. When using this option, 534 | better results are usually obtained when subRectangles is False. 535 | subRectangles : False, True, or a list of 2-element tuples 536 | Whether to use sub-rectangles. If True, the minimal rectangle that 537 | is required to update each frame is automatically detected. This 538 | can give significant reductions in file size, particularly if only 539 | a part of the image changes. One can also give a list of x-y 540 | coordinates if you want to do the cropping yourself. The default 541 | is True. 542 | dispose : int 543 | How to dispose each frame. 1 means that each frame is to be left 544 | in place. 2 means the background color should be restored after 545 | each frame. 3 means the decoder should restore the previous frame. 546 | If subRectangles==False, the default is 2, otherwise it is 1. 547 | 548 | """ 549 | 550 | # Check PIL 551 | if PIL is None: 552 | raise RuntimeError("Need PIL to write animated gif files.") 553 | 554 | # Check images 555 | images = checkImages(images) 556 | 557 | # Instantiate writer object 558 | gifWriter = GifWriter() 559 | # init transparency flag used in GifWriter functions 560 | gifWriter.transparency = False 561 | 562 | # Check loops 563 | if repeat is False: 564 | loops = 1 565 | elif repeat is True: 566 | loops = 0 # zero means infinite 567 | else: 568 | loops = int(repeat) 569 | 570 | # Check duration 571 | if hasattr(duration, '__len__'): 572 | if len(duration) == len(images): 573 | duration = [d for d in duration] 574 | else: 575 | raise ValueError("len(duration) doesn't match amount of images.") 576 | else: 577 | duration = [duration for im in images] 578 | 579 | # Check subrectangles 580 | if subRectangles: 581 | images, xy, images_info = gifWriter.handleSubRectangles( 582 | images, subRectangles) 583 | defaultDispose = 1 # Leave image in place 584 | else: 585 | # Normal mode 586 | xy = [(0, 0) for im in images] 587 | defaultDispose = 2 # Restore to background color. 588 | 589 | # Check dispose 590 | if dispose is None: 591 | dispose = defaultDispose 592 | if hasattr(dispose, '__len__'): 593 | if len(dispose) != len(images): 594 | raise ValueError("len(xy) doesn't match amount of images.") 595 | else: 596 | dispose = [dispose for im in images] 597 | 598 | # Make images in a format that we can write easy 599 | images = gifWriter.convertImagesToPIL(images, dither, nq) 600 | 601 | # Write 602 | fp = open(filename, 'wb') 603 | try: 604 | gifWriter.writeGifToFile(fp, images, duration, loops, xy, dispose) 605 | finally: 606 | fp.close() 607 | 608 | 609 | def readGif(filename, asNumpy=True): 610 | """ readGif(filename, asNumpy=True) 611 | 612 | Read images from an animated GIF file. Returns a list of numpy 613 | arrays, or, if asNumpy is false, a list if PIL images. 614 | 615 | """ 616 | 617 | # Check PIL 618 | if PIL is None: 619 | raise RuntimeError("Need PIL to read animated gif files.") 620 | 621 | # Check Numpy 622 | if np is None: 623 | raise RuntimeError("Need Numpy to read animated gif files.") 624 | 625 | # Check whether it exists 626 | if not os.path.isfile(filename): 627 | raise IOError('File not found: ' + str(filename)) 628 | 629 | # Load file using PIL 630 | pilIm = PIL.Image.open(filename) 631 | pilIm.seek(0) 632 | 633 | # Read all images inside 634 | images = [] 635 | try: 636 | while True: 637 | # Get image as numpy array 638 | tmp = pilIm.convert() # Make without palette 639 | a = np.asarray(tmp) 640 | if len(a.shape) == 0: 641 | raise MemoryError( 642 | "Too little memory to convert PIL image to array") 643 | # Store, and next 644 | images.append(a) 645 | pilIm.seek(pilIm.tell() + 1) 646 | except EOFError: 647 | pass 648 | 649 | # Convert to normal PIL images if needed 650 | if not asNumpy: 651 | images2 = images 652 | images = [] 653 | for index, im in enumerate(images2): 654 | tmp = PIL.Image.fromarray(im) 655 | images.append(tmp) 656 | 657 | # Done 658 | return images 659 | 660 | 661 | class NeuQuant: 662 | 663 | """ NeuQuant(image, samplefac=10, colors=256) 664 | 665 | samplefac should be an integer number of 1 or higher, 1 666 | being the highest quality, but the slowest performance. 667 | With avalue of 10, one tenth of all pixels are used during 668 | training. This value seems a nice tradeof between speed 669 | and quality. 670 | 671 | colors is the amount of colors to reduce the image to. This 672 | should best be a power of two. 673 | 674 | See also: 675 | http://members.ozemail.com.au/~dekker/NEUQUANT.HTML 676 | 677 | License of the NeuQuant Neural-Net Quantization Algorithm 678 | --------------------------------------------------------- 679 | 680 | Copyright (c) 1994 Anthony Dekker 681 | Ported to python by Marius van Voorden in 2010 682 | 683 | NEUQUANT Neural-Net quantization algorithm by Anthony Dekker, 1994. 684 | See "Kohonen neural networks for optimal colour quantization" 685 | in "network: Computation in Neural Systems" Vol. 5 (1994) pp 351-367. 686 | for a discussion of the algorithm. 687 | See also http://members.ozemail.com.au/~dekker/NEUQUANT.HTML 688 | 689 | Any party obtaining a copy of these files from the author, directly or 690 | indirectly, is granted, free of charge, a full and unrestricted 691 | irrevocable, world-wide, paid up, royalty-free, nonexclusive right and 692 | license to deal in this software and documentation files (the "Software"), 693 | including without limitation the rights to use, copy, modify, merge, 694 | publish, distribute, sublicense, and/or sell copies of the Software, and 695 | to permit persons who receive copies from any such party to do so, with 696 | the only requirement being that this copyright notice remain intact. 697 | 698 | """ 699 | 700 | NCYCLES = None # Number of learning cycles 701 | NETSIZE = None # Number of colours used 702 | SPECIALS = None # Number of reserved colours used 703 | BGCOLOR = None # Reserved background colour 704 | CUTNETSIZE = None 705 | MAXNETPOS = None 706 | 707 | INITRAD = None # For 256 colours, radius starts at 32 708 | RADIUSBIASSHIFT = None 709 | RADIUSBIAS = None 710 | INITBIASRADIUS = None 711 | RADIUSDEC = None # Factor of 1/30 each cycle 712 | 713 | ALPHABIASSHIFT = None 714 | INITALPHA = None # biased by 10 bits 715 | 716 | GAMMA = None 717 | BETA = None 718 | BETAGAMMA = None 719 | 720 | network = None # The network itself 721 | colormap = None # The network itself 722 | 723 | netindex = None # For network lookup - really 256 724 | 725 | bias = None # Bias and freq arrays for learning 726 | freq = None 727 | 728 | pimage = None 729 | 730 | # Four primes near 500 - assume no image has a length so large 731 | # that it is divisible by all four primes 732 | PRIME1 = 499 733 | PRIME2 = 491 734 | PRIME3 = 487 735 | PRIME4 = 503 736 | MAXPRIME = PRIME4 737 | 738 | pixels = None 739 | samplefac = None 740 | 741 | a_s = None 742 | 743 | def setconstants(self, samplefac, colors): 744 | self.NCYCLES = 100 # Number of learning cycles 745 | self.NETSIZE = colors # Number of colours used 746 | self.SPECIALS = 3 # Number of reserved colours used 747 | self.BGCOLOR = self.SPECIALS - 1 # Reserved background colour 748 | self.CUTNETSIZE = self.NETSIZE - self.SPECIALS 749 | self.MAXNETPOS = self.NETSIZE - 1 750 | 751 | self.INITRAD = self.NETSIZE / 8 # For 256 colours, radius starts at 32 752 | self.RADIUSBIASSHIFT = 6 753 | self.RADIUSBIAS = 1 << self.RADIUSBIASSHIFT 754 | self.INITBIASRADIUS = self.INITRAD * self.RADIUSBIAS 755 | self.RADIUSDEC = 30 # Factor of 1/30 each cycle 756 | 757 | self.ALPHABIASSHIFT = 10 # Alpha starts at 1 758 | self.INITALPHA = 1 << self.ALPHABIASSHIFT # biased by 10 bits 759 | 760 | self.GAMMA = 1024.0 761 | self.BETA = 1.0 / 1024.0 762 | self.BETAGAMMA = self.BETA * self.GAMMA 763 | 764 | self.network = np.empty( 765 | (self.NETSIZE, 3), dtype='float64') # The network itself 766 | self.colormap = np.empty( 767 | (self.NETSIZE, 4), dtype='int32') # The network itself 768 | 769 | self.netindex = np.empty( 770 | 256, 771 | dtype='int32') # For network lookup - really 256 772 | 773 | self.bias = np.empty( 774 | self.NETSIZE, 775 | dtype='float64') # Bias and freq arrays for learning 776 | self.freq = np.empty(self.NETSIZE, dtype='float64') 777 | 778 | self.pixels = None 779 | self.samplefac = samplefac 780 | 781 | self.a_s = {} 782 | 783 | def __init__(self, image, samplefac=10, colors=256): 784 | 785 | # Check Numpy 786 | if np is None: 787 | raise RuntimeError("Need Numpy for the NeuQuant algorithm.") 788 | 789 | # Check image 790 | if image.size[0] * image.size[1] < NeuQuant.MAXPRIME: 791 | raise IOError("Image is too small") 792 | if image.mode != "RGBA": 793 | raise IOError("Image mode should be RGBA.") 794 | 795 | # Initialize 796 | self.setconstants(samplefac, colors) 797 | self.pixels = np.fromstring(image.tostring(), np.uint32) 798 | self.setUpArrays() 799 | 800 | self.learn() 801 | self.fix() 802 | self.inxbuild() 803 | 804 | def writeColourMap(self, rgb, outstream): 805 | for i in range(self.NETSIZE): 806 | bb = self.colormap[i, 0] 807 | gg = self.colormap[i, 1] 808 | rr = self.colormap[i, 2] 809 | outstream.write(rr if rgb else bb) 810 | outstream.write(gg) 811 | outstream.write(bb if rgb else rr) 812 | return self.NETSIZE 813 | 814 | def setUpArrays(self): 815 | self.network[0, 0] = 0.0 # Black 816 | self.network[0, 1] = 0.0 817 | self.network[0, 2] = 0.0 818 | 819 | self.network[1, 0] = 255.0 # White 820 | self.network[1, 1] = 255.0 821 | self.network[1, 2] = 255.0 822 | 823 | # RESERVED self.BGCOLOR # Background 824 | 825 | for i in range(self.SPECIALS): 826 | self.freq[i] = 1.0 / self.NETSIZE 827 | self.bias[i] = 0.0 828 | 829 | for i in range(self.SPECIALS, self.NETSIZE): 830 | p = self.network[i] 831 | p[:] = (255.0 * (i - self.SPECIALS)) / self.CUTNETSIZE 832 | 833 | self.freq[i] = 1.0 / self.NETSIZE 834 | self.bias[i] = 0.0 835 | 836 | # Omitted: setPixels 837 | 838 | def altersingle(self, alpha, i, b, g, r): 839 | """Move neuron i towards biased (b,g,r) by factor alpha""" 840 | n = self.network[i] # Alter hit neuron 841 | n[0] -= (alpha * (n[0] - b)) 842 | n[1] -= (alpha * (n[1] - g)) 843 | n[2] -= (alpha * (n[2] - r)) 844 | 845 | def geta(self, alpha, rad): 846 | try: 847 | return self.a_s[(alpha, rad)] 848 | except KeyError: 849 | length = rad * 2 - 1 850 | mid = length / 2 851 | q = np.array(list(range(mid - 1, -1, -1)) + list(range(-1, mid))) 852 | a = alpha * (rad * rad - q * q) / (rad * rad) 853 | a[mid] = 0 854 | self.a_s[(alpha, rad)] = a 855 | return a 856 | 857 | def alterneigh(self, alpha, rad, i, b, g, r): 858 | if i - rad >= self.SPECIALS - 1: 859 | lo = i - rad 860 | start = 0 861 | else: 862 | lo = self.SPECIALS - 1 863 | start = (self.SPECIALS - 1 - (i - rad)) 864 | 865 | if i + rad <= self.NETSIZE: 866 | hi = i + rad 867 | end = rad * 2 - 1 868 | else: 869 | hi = self.NETSIZE 870 | end = (self.NETSIZE - (i + rad)) 871 | 872 | a = self.geta(alpha, rad)[start:end] 873 | 874 | p = self.network[lo + 1:hi] 875 | p -= np.transpose(np.transpose(p - np.array([b, g, r])) * a) 876 | 877 | def contest(self, b, g, r): 878 | """ Search for biased BGR values 879 | Finds closest neuron (min dist) and updates self.freq 880 | finds best neuron (min dist-self.bias) and returns position 881 | for frequently chosen neurons, self.freq[i] is high and 882 | self.bias[i] is negative 883 | self.bias[i] = self.GAMMA*((1/self.NETSIZE)-self.freq[i])""" 884 | i, j = self.SPECIALS, self.NETSIZE 885 | dists = abs(self.network[i:j] - np.array([b, g, r])).sum(1) 886 | bestpos = i + np.argmin(dists) 887 | biasdists = dists - self.bias[i:j] 888 | bestbiaspos = i + np.argmin(biasdists) 889 | self.freq[i:j] *= (1 - self.BETA) 890 | self.bias[i:j] += self.BETAGAMMA * self.freq[i:j] 891 | self.freq[bestpos] += self.BETA 892 | self.bias[bestpos] -= self.BETAGAMMA 893 | return bestbiaspos 894 | 895 | def specialFind(self, b, g, r): 896 | for i in range(self.SPECIALS): 897 | n = self.network[i] 898 | if n[0] == b and n[1] == g and n[2] == r: 899 | return i 900 | return -1 901 | 902 | def learn(self): 903 | biasRadius = self.INITBIASRADIUS 904 | alphadec = 30 + ((self.samplefac - 1) / 3) 905 | lengthcount = self.pixels.size 906 | samplepixels = lengthcount / self.samplefac 907 | delta = samplepixels / self.NCYCLES 908 | alpha = self.INITALPHA 909 | 910 | i = 0 911 | rad = biasRadius >> self.RADIUSBIASSHIFT 912 | if rad <= 1: 913 | rad = 0 914 | 915 | print("Beginning 1D learning: samplepixels = %1.2f rad = %i" % 916 | (samplepixels, rad)) 917 | step = 0 918 | pos = 0 919 | if lengthcount % NeuQuant.PRIME1 != 0: 920 | step = NeuQuant.PRIME1 921 | elif lengthcount % NeuQuant.PRIME2 != 0: 922 | step = NeuQuant.PRIME2 923 | elif lengthcount % NeuQuant.PRIME3 != 0: 924 | step = NeuQuant.PRIME3 925 | else: 926 | step = NeuQuant.PRIME4 927 | 928 | i = 0 929 | printed_string = '' 930 | while i < samplepixels: 931 | if i % 100 == 99: 932 | tmp = '\b' * len(printed_string) 933 | printed_string = str((i + 1) * 100 / samplepixels) + "%\n" 934 | print(tmp + printed_string) 935 | p = self.pixels[pos] 936 | r = (p >> 16) & 0xff 937 | g = (p >> 8) & 0xff 938 | b = (p) & 0xff 939 | 940 | if i == 0: # Remember background colour 941 | self.network[self.BGCOLOR] = [b, g, r] 942 | 943 | j = self.specialFind(b, g, r) 944 | if j < 0: 945 | j = self.contest(b, g, r) 946 | 947 | if j >= self.SPECIALS: # Don't learn for specials 948 | a = (1.0 * alpha) / self.INITALPHA 949 | self.altersingle(a, j, b, g, r) 950 | if rad > 0: 951 | self.alterneigh(a, rad, j, b, g, r) 952 | 953 | pos = (pos + step) % lengthcount 954 | 955 | i += 1 956 | if i % delta == 0: 957 | alpha -= alpha / alphadec 958 | biasRadius -= biasRadius / self.RADIUSDEC 959 | rad = biasRadius >> self.RADIUSBIASSHIFT 960 | if rad <= 1: 961 | rad = 0 962 | 963 | finalAlpha = (1.0 * alpha) / self.INITALPHA 964 | print("Finished 1D learning: final alpha = %1.2f!" % finalAlpha) 965 | 966 | def fix(self): 967 | for i in range(self.NETSIZE): 968 | for j in range(3): 969 | x = int(0.5 + self.network[i, j]) 970 | x = max(0, x) 971 | x = min(255, x) 972 | self.colormap[i, j] = x 973 | self.colormap[i, 3] = i 974 | 975 | def inxbuild(self): 976 | previouscol = 0 977 | startpos = 0 978 | for i in range(self.NETSIZE): 979 | p = self.colormap[i] 980 | q = None 981 | smallpos = i 982 | smallval = p[1] # Index on g 983 | # Find smallest in i..self.NETSIZE-1 984 | for j in range(i + 1, self.NETSIZE): 985 | q = self.colormap[j] 986 | if q[1] < smallval: # Index on g 987 | smallpos = j 988 | smallval = q[1] # Index on g 989 | 990 | q = self.colormap[smallpos] 991 | # Swap p (i) and q (smallpos) entries 992 | if i != smallpos: 993 | p[:], q[:] = q, p.copy() 994 | 995 | # smallval entry is now in position i 996 | if smallval != previouscol: 997 | self.netindex[previouscol] = (startpos + i) >> 1 998 | for j in range(previouscol + 1, smallval): 999 | self.netindex[j] = i 1000 | previouscol = smallval 1001 | startpos = i 1002 | self.netindex[previouscol] = (startpos + self.MAXNETPOS) >> 1 1003 | for j in range(previouscol + 1, 256): # Really 256 1004 | self.netindex[j] = self.MAXNETPOS 1005 | 1006 | def paletteImage(self): 1007 | """ PIL weird interface for making a paletted image: create an image 1008 | which already has the palette, and use that in Image.quantize. This 1009 | function returns this palette image. """ 1010 | if self.pimage is None: 1011 | palette = [] 1012 | for i in range(self.NETSIZE): 1013 | palette.extend(self.colormap[i][:3]) 1014 | 1015 | palette.extend([0] * (256 - self.NETSIZE) * 3) 1016 | 1017 | # a palette image to use for quant 1018 | self.pimage = Image.new("P", (1, 1), 0) 1019 | self.pimage.putpalette(palette) 1020 | return self.pimage 1021 | 1022 | def quantize(self, image): 1023 | """ Use a kdtree to quickly find the closest palette colors for the 1024 | pixels """ 1025 | if get_cKDTree(): 1026 | return self.quantize_with_scipy(image) 1027 | else: 1028 | print('Scipy not available, falling back to slower version.') 1029 | return self.quantize_without_scipy(image) 1030 | 1031 | def quantize_with_scipy(self, image): 1032 | w, h = image.size 1033 | px = np.asarray(image).copy() 1034 | px2 = px[:, :, :3].reshape((w * h, 3)) 1035 | 1036 | cKDTree = get_cKDTree() 1037 | kdtree = cKDTree(self.colormap[:, :3], leafsize=10) 1038 | result = kdtree.query(px2) 1039 | colorindex = result[1] 1040 | print("Distance: %1.2f" % (result[0].sum() / (w * h))) 1041 | px2[:] = self.colormap[colorindex, :3] 1042 | 1043 | return Image.fromarray(px).convert( 1044 | "RGB").quantize(palette=self.paletteImage()) 1045 | 1046 | def quantize_without_scipy(self, image): 1047 | """" This function can be used if no scipy is availabe. 1048 | It's 7 times slower though. 1049 | """ 1050 | w, h = image.size 1051 | px = np.asarray(image).copy() 1052 | memo = {} 1053 | for j in range(w): 1054 | for i in range(h): 1055 | key = (px[i, j, 0], px[i, j, 1], px[i, j, 2]) 1056 | try: 1057 | val = memo[key] 1058 | except KeyError: 1059 | val = self.convert(*key) 1060 | memo[key] = val 1061 | px[i, j, 0], px[i, j, 1], px[i, j, 2] = val 1062 | return Image.fromarray(px).convert( 1063 | "RGB").quantize(palette=self.paletteImage()) 1064 | 1065 | def convert(self, *color): 1066 | i = self.inxsearch(*color) 1067 | return self.colormap[i, :3] 1068 | 1069 | def inxsearch(self, r, g, b): 1070 | """Search for BGR values 0..255 and return colour index""" 1071 | dists = (self.colormap[:, :3] - np.array([r, g, b])) 1072 | a = np.argmin((dists * dists).sum(1)) 1073 | return a 1074 | 1075 | 1076 | if __name__ == '__main__': 1077 | im = np.zeros((200, 200), dtype=np.uint8) 1078 | im[10:30, :] = 100 1079 | im[:, 80:120] = 255 1080 | im[-50:-40, :] = 50 1081 | 1082 | images = [im * 1.0, im * 0.8, im * 0.6, im * 0.4, im * 0] 1083 | writeGif('lala3.gif', images, duration=0.5, dither=0) 1084 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | #caffe 2 | numpy 3 | Pillow 4 | scipy 5 | --------------------------------------------------------------------------------