├── .gitignore ├── LICENSE ├── NikeMouth ├── README.md ├── imgs │ ├── nike.png │ ├── test.jpg │ └── test_out.jpg ├── main.py ├── normal2nike.py └── options.py ├── OneImage2Video ├── README_CN.md ├── imgs │ ├── contributions.png │ ├── contributions_120.png │ └── test.jpg ├── main.py ├── pixel_imgs │ ├── banana │ │ ├── 00.png │ │ └── 01.png │ └── github │ │ ├── 01.png │ │ ├── 02.png │ │ ├── 03.png │ │ ├── 04.png │ │ └── 05.png └── school_badge.py ├── PlayMusic ├── README.md ├── music │ ├── SchoolBell.txt │ └── TwinkleTwinkleLittleStar.txt └── notation.py ├── README.md ├── Util ├── __init__.py ├── array_operation.py ├── dsp.py ├── ffmpeg.py ├── image_processing.py ├── sound.py └── util.py └── imgs ├── DeepMosaics.jpg ├── GuiChu_余生一个浪.jpg ├── OneImage2Video_badapple.jpg ├── PlayMusic_小星星.jpg ├── Plogo.jpg ├── ShellPlayer_大威天龙.jpg └── ShellPlayer_教程.jpg /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | 131 | # myrules 132 | tmp/ 133 | result/ 134 | /DualVectorFoil 135 | /GuiChu 136 | /SkyScreen 137 | /Video 138 | /GhostWriter 139 | /pythontest.py 140 | /ShellPlayer 141 | /Plogo 142 | /test.py 143 | 144 | /PlayMusic/music 145 | /PlayMusic/test.py 146 | 147 | /OneImage2Video/pixel_imgs/university 148 | 149 | /NikeMouth/media 150 | /NikeMouth/test_media 151 | /NikeMouth/test.py 152 | 153 | *.mp4 154 | *.flv 155 | *.mp3 156 | *.wav 157 | *.exe -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2020 Hypo 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /NikeMouth/README.md: -------------------------------------------------------------------------------- 1 | # NikeMouth 2 | [更多有趣的代码](https://github.com/HypoX64/bilibili) 3 | ## 入门 4 | ### 前提要求 5 | * python3 6 | * [ffmpeg](http://ffmpeg.org/) 7 | 8 | ### 依赖 9 | 代码依赖于 opencv-python, face_recognition, 可以通过 pip install安装 10 | ```bash 11 | pip install opencv-python 12 | pip install face_recognition 13 | ``` 14 | ### 克隆这个仓库 15 | ```bash 16 | git clone https://github.com/HypoX64/bilibili.git 17 | cd bilibili/NikeMouth 18 | ``` 19 | ### 运行程序 20 | ```bash 21 | python main.py -m "图片的路径或目录" 22 | ``` 23 | image image
24 | 25 | ## 更多的参数 26 | 27 | | 选项 | 描述 | 默认值 | 28 | | :----------: | :------------------------: | :-------------------------------------: | 29 | | -m | 图片的路径或目录 | './imgs/test.jpg' | 30 | | -mode | 改变的模式，all_face　or only_mouth | all_face | 31 | | -o | 输出视频或图片image or video | image | 32 | | -r | 结果储存的目录 | ./result | 33 | | -i | 变换强度 | 1.0 | 34 | 35 | -------------------------------------------------------------------------------- /NikeMouth/imgs/nike.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/HypoX64/bilibili/992029667ad37d7d03131aa2c4c9923da6cca6f2/NikeMouth/imgs/nike.png -------------------------------------------------------------------------------- /NikeMouth/imgs/test.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/HypoX64/bilibili/992029667ad37d7d03131aa2c4c9923da6cca6f2/NikeMouth/imgs/test.jpg -------------------------------------------------------------------------------- /NikeMouth/imgs/test_out.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/HypoX64/bilibili/992029667ad37d7d03131aa2c4c9923da6cca6f2/NikeMouth/imgs/test_out.jpg -------------------------------------------------------------------------------- /NikeMouth/main.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | import numpy as np 4 | import cv2 5 | import normal2nike 6 | sys.path.append("..") 7 | from Util import util,ffmpeg 8 | from options import Options 9 | opt = Options().getparse() 10 | 11 | util.file_init(opt) 12 | 13 | if os.path.isdir(opt.media): 14 | files = util.Traversal(opt.media) 15 | else: 16 | files = [opt.media] 17 | 18 | for file in files: 19 | img = cv2.imread(file) 20 | h,w = img.shape[:2] 21 | if opt.output == 'image': 22 | img = normal2nike.convert(img,opt.size,opt.intensity,opt.aspect_ratio,opt.ex_move,opt.mode) 23 | cv2.imwrite(os.path.join(opt.result_dir,os.path.basename(file)), img) 24 | elif opt.output == 'video': 25 | frame = int(opt.time*opt.fps) 26 | for i in range(frame): 27 | tmp = normal2nike.convert(img,opt.size,i*opt.intensity/frame,opt.aspect_ratio, 28 | opt.ex_move,opt.mode)[:4*(h//4),:4*(w//4)] 29 | cv2.imwrite(os.path.join('./tmp/output_imgs','%05d' % i+'.jpg'), tmp) 30 | cv2.imwrite(os.path.join(opt.result_dir,os.path.basename(file)), tmp) 31 | ffmpeg.image2video( 32 | opt.fps, 33 | './tmp/output_imgs/%05d.jpg', 34 | None, 35 | os.path.join(opt.result_dir,os.path.splitext(os.path.basename(file))[0]+'.mp4')) 36 | 37 | # cv2.namedWindow('image', cv2.WINDOW_NORMAL) 38 | # cv2.imshow('image',img) 39 | # cv2.waitKey(0) 40 | # cv2.destroyAllWindows() -------------------------------------------------------------------------------- /NikeMouth/normal2nike.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import cv2 3 | import face_recognition 4 | 5 | 6 | # 繪製delaunay triangles 7 | def draw_delaunay(img, TriangleList, delaunary_color): 8 | 9 | size = img.shape 10 | r = (0, 0, size[1], size[0]) 11 | 12 | for t in TriangleList: 13 | pt1 = (t[0], t[1]) 14 | pt2 = (t[2], t[3]) 15 | pt3 = (t[4], t[5]) 16 | 17 | # if rect_contains(r, pt1) and rect_contains(r, pt2) and rect_contains(r, pt3): 18 | cv2.line(img, pt1, pt2, delaunary_color, 1) 19 | cv2.line(img, pt2, pt3, delaunary_color, 1) 20 | cv2.line(img, pt3, pt1, delaunary_color, 1) 21 | 22 | def get_Ms(t1,t2): 23 | Ms = [] 24 | for i in range(len(t1)): 25 | pts1 = np.array([[t1[i][0],t1[i][1]],[t1[i][2],t1[i][3]],[t1[i][4],t1[i][5]]]).astype(np.float32) 26 | pts2 = np.array([[t2[i][0],t2[i][1]],[t2[i][2],t2[i][3]],[t2[i][4],t2[i][5]]]).astype(np.float32) 27 | # print(pts1) 28 | Ms.append(cv2.getAffineTransform(pts1,pts2)) 29 | return Ms 30 | 31 | def delaunay(h,w,points): 32 | subdiv = cv2.Subdiv2D((0,0,w,h)) 33 | for i in range(len(points)): 34 | subdiv.insert(tuple(points[i])) 35 | TriangleList = subdiv.getTriangleList() 36 | return TriangleList 37 | 38 | def get_borderpoints(img): 39 | h,w = img.shape[:2] 40 | h,w = h-1,w-1 41 | points = [[0,0],[w//2,0],[w,0],[w,h//2],[w,h],[w//2,h],[0,h],[0,h//2]] 42 | return np.array(points) 43 | 44 | def get_masks(img,t): 45 | masks = np.zeros((len(t),img.shape[0],img.shape[1]), dtype=np.uint8) 46 | for i in range(len(t)): 47 | 48 | points = np.array([[t[i][0],t[i][1]],[t[i][2],t[i][3]],[t[i][4],t[i][5]]]).astype(np.int64) 49 | #print(points) 50 | masks[i] = cv2.fillConvexPoly(masks[i],points,(255)) 51 | cv2.line(masks[i], tuple(points[0]), tuple(points[1]), (255), 3) 52 | cv2.line(masks[i], tuple(points[0]), tuple(points[2]), (255), 3) 53 | cv2.line(masks[i], tuple(points[1]), tuple(points[2]), (255), 3) 54 | 55 | #masks[i] = cv2.drawContours(masks[i], points, contourIdx=0, color=255, thickness=2) 56 | return masks 57 | 58 | def changeTriangleList(t,point,move): 59 | t = t.astype(np.int64) 60 | for i in range(len(t)): 61 | if t[i][0]==point[0] and t[i][1]==point[1]: 62 | t[i][0],t[i][1] = t[i][0]+move[0],t[i][1]+move[1] 63 | elif t[i][2]==point[0] and t[i][3]==point[1]: 64 | t[i][2],t[i][3] = t[i][2]+move[0],t[i][3]+move[1] 65 | elif t[i][4]==point[0] and t[i][5]==point[1]: 66 | t[i][4],t[i][5] = t[i][4]+move[0],t[i][5]+move[1] 67 | return t 68 | 69 | def replace_delaunay(img,Ms,masks): 70 | img_new = np.zeros_like(img) 71 | h,w = img.shape[:2] 72 | for i in range(len(Ms)): 73 | # _img = img.copy() 74 | mask = cv2.merge([masks[i], masks[i], masks[i]]) 75 | mask_inv = cv2.bitwise_not(mask) 76 | tmp = cv2.warpAffine(img,Ms[i],(w,h),borderMode = cv2.BORDER_REFLECT_101,flags=cv2.INTER_CUBIC) 77 | tmp = cv2.bitwise_and(mask,tmp) 78 | img_new = cv2.bitwise_and(mask_inv,img_new) 79 | img_new = cv2.add(tmp,img_new) 80 | return img_new 81 | 82 | def get_nikemouth_landmark(src_landmark,alpha=1,aspect_ratio=1.0,mode = 'only_mouth'): 83 | 84 | 85 | nike = cv2.imread('./imgs/nike.png') 86 | landmark = face_recognition.face_landmarks(nike)[0] 87 | if mode == 'only_mouth': 88 | src_landmark = src_landmark[56:] 89 | nikemouth = np.array(landmark['top_lip']+landmark['bottom_lip']) 90 | else: 91 | src_landmark = src_landmark[25:] 92 | nikemouth = np.array(landmark['left_eyebrow']+landmark['right_eyebrow']+landmark['nose_bridge']+\ 93 | landmark['nose_tip']+landmark['left_eye']+landmark['right_eye']+landmark['top_lip']+\ 94 | landmark['bottom_lip']) 95 | 96 | # 中心置0 97 | nikemouth = nikemouth-[np.mean(nikemouth[:,0]),np.mean(nikemouth[:,1])] 98 | nikemouth[:,0] = nikemouth[:,0]*aspect_ratio 99 | # 获取嘴巴大小 100 | nikemouth_h = np.max(nikemouth[:,1])-np.min(nikemouth[:,1]) 101 | nikemouth_w = np.max(nikemouth[:,0])-np.min(nikemouth[:,0]) 102 | src_h = np.max(src_landmark[:,1])-np.min(src_landmark[:,1]) 103 | src_w = np.max(src_landmark[:,0])-np.min(src_landmark[:,0]) 104 | 105 | # 调整大小及位置 106 | beta = nikemouth_w/src_w 107 | nikemouth = alpha*nikemouth/beta+[np.mean(src_landmark[:,0]),np.mean(src_landmark[:,1])] 108 | return np.around(nikemouth,0) 109 | 110 | 111 | 112 | def convert(face,size=1,intensity=1,aspect_ratio=1.0,ex_move=[0,0],mode='all_face'): 113 | 114 | h,w = face.shape[:2] 115 | landmark = face_recognition.face_landmarks(face)[0] 116 | # print(landmark) 117 | landmark_src = np.array(landmark['chin']+landmark['left_eyebrow']+landmark['right_eyebrow']+\ 118 | landmark['nose_bridge']+landmark['nose_tip']+landmark['left_eye']+landmark['right_eye']+landmark['top_lip']+\ 119 | landmark['bottom_lip']) 120 | 121 | landmark_src = np.concatenate((get_borderpoints(face), landmark_src), axis=0) 122 | TriangleList_src = delaunay(h,w,landmark_src) 123 | TriangleList_dst = TriangleList_src.copy() 124 | 125 | 126 | nikemouth_landmark = get_nikemouth_landmark(landmark_src,alpha=size,aspect_ratio=aspect_ratio,mode = mode) 127 | if mode == 'only_mouth': 128 | for i in range(24): 129 | move = ex_move+(nikemouth_landmark[i]-landmark_src[56+i])*intensity 130 | TriangleList_dst = changeTriangleList(TriangleList_dst, landmark_src[56+i],move) 131 | else: 132 | for i in range(80-25): 133 | move = ex_move+(nikemouth_landmark[i]-landmark_src[25+i])*intensity 134 | TriangleList_dst = changeTriangleList(TriangleList_dst, landmark_src[25+i],move) 135 | 136 | Ms = get_Ms(TriangleList_src,TriangleList_dst) 137 | masks = get_masks(face, TriangleList_dst) 138 | face_new = replace_delaunay(face, Ms, masks) 139 | 140 | return face_new 141 | 142 | # # draw_delaunay(img, TriangleList, delaunary_color): 143 | 144 | # cv2.namedWindow('image', cv2.WINDOW_NORMAL) 145 | # cv2.imshow('image',face_new) 146 | # cv2.waitKey(0) 147 | # cv2.destroyAllWindows() -------------------------------------------------------------------------------- /NikeMouth/options.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | 3 | class Options(): 4 | def __init__(self): 5 | self.parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) 6 | self.initialized = False 7 | 8 | def initialize(self): 9 | self.parser.add_argument('-m','--media', type=str, default='./imgs/test.jpg',help='your image path or dir') 10 | self.parser.add_argument('-mode','--mode', type=str, default='all_face',help='all_face | only_mouth') 11 | self.parser.add_argument('-o','--output', type=str, default='image',help='output image or video') 12 | self.parser.add_argument('-t','--time', type=float, default=2.0,help='time of video') 13 | self.parser.add_argument('-f','--fps', type=float, default=25,help='fps of video') 14 | self.parser.add_argument('-s','--size', type=float, default=1.0,help='size of mouth') 15 | self.parser.add_argument('-i','--intensity', type=float, default=1.0,help='effect intensity') 16 | self.parser.add_argument('-a','--aspect_ratio', type=float, default=1.0,help='aspect ratio of mouth') 17 | self.parser.add_argument('-e','--ex_move', type=str, default='[0,0]',help='') 18 | self.parser.add_argument('-r','--result_dir', type=str, default='./result',help='') 19 | # self.parser.add_argument('--temp_dir', type=str, default='./tmp',help='') 20 | 21 | self.initialized = True 22 | 23 | def getparse(self): 24 | if not self.initialized: 25 | self.initialize() 26 | self.opt = self.parser.parse_args() 27 | 28 | self.opt.ex_move = eval(self.opt.ex_move) 29 | return self.opt 30 | 31 | -------------------------------------------------------------------------------- /OneImage2Video/README_CN.md: -------------------------------------------------------------------------------- 1 | # OneImage2Video 2 | 用一个或一些图片生成视频
3 | 4 | ## 入门 5 | ### 前提要求 6 | * Linux or Windows 7 | * python3 8 | * [ffmpeg](http://ffmpeg.org/) 9 | ```bash 10 | sudo apt-get install ffmpeg 11 | ``` 12 | ### 依赖 13 | 代码依赖于 opencv-python, 可以通过 pip install安装 14 | ```bash 15 | pip install opencv-python 16 | ``` 17 | ### 克隆这个仓库 18 | ```bash 19 | git clone https://github.com/HypoX64/bilibili.git 20 | cd bilibili/OneImage2Video 21 | ``` 22 | ### 运行程序 23 | * 在main.py中修改你的视频路径以及图片路径 24 | ```python 25 | # 在github看香蕉君 26 | # pixel_imgs_dir = './pixel_imgs/github' 27 | # pixel_imgs_resize = 0 # resize pixel_imgs, if 0, do not resize 28 | # output_pixel_num = 52 # how many pixels in the output video'width 29 | # video_path = '../Video/素材/香蕉君/香蕉君_3.mp4' 30 | # inverse = False 31 | ``` 32 | * run 33 | ```bash 34 | python main.py 35 | ``` 36 | 37 | 38 | -------------------------------------------------------------------------------- /OneImage2Video/imgs/contributions.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/HypoX64/bilibili/992029667ad37d7d03131aa2c4c9923da6cca6f2/OneImage2Video/imgs/contributions.png -------------------------------------------------------------------------------- /OneImage2Video/imgs/contributions_120.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/HypoX64/bilibili/992029667ad37d7d03131aa2c4c9923da6cca6f2/OneImage2Video/imgs/contributions_120.png -------------------------------------------------------------------------------- /OneImage2Video/imgs/test.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/HypoX64/bilibili/992029667ad37d7d03131aa2c4c9923da6cca6f2/OneImage2Video/imgs/test.jpg -------------------------------------------------------------------------------- /OneImage2Video/main.py: -------------------------------------------------------------------------------- 1 | import os 2 | import cv2 3 | import numpy as np 4 | 5 | import sys 6 | sys.path.append("..") 7 | from Util import util,ffmpeg 8 | ''' 9 | , 10 | ,+nnDDDDn1+ n@@@1 11 | +&MMn 1D@@@@@@@@@@@@@&n, 1@@@@@1 12 | D@@@@@, nM@@@@@@@@@@@@@@@@@@&+ 1@@@@@@@1 13 | 1M@@@@@@, +M@@@@D,,,,,,,,,,,1@@@@@D +D@@@@@@@@@D+ 14 | ,1nn, &@@@@@@@@nnnnnnnn1, +@@@@@@M&&&&1 ,&&&&M@@@@@@& ,nD&&M@@@@@@@@@@@@@@@@@M&&Dn, 15 | ,M@@@@1 ,@@@@@@@@@@@@@@@@@@@D @@@@@@@@@&1+, +1DM@@@@@@@@n &@@@@@@@@@@@@@@@@@@@@@@@@@@@& 16 | 1@@@@@1 ,@@@@@@@@@@@@@@@@@@@n n@@@@@@@D +n1 D1, +M@@@@@@M 1M@@@@@@@@@@@@@@@@@@@@@@@Mn 17 | 1@@@@@1 ,@@@@@@@@@@@@@@@@@@M D@@@@@@n ,M@@D ,@@@n ,@@@@@@@ 1M@@@@@@@@@@@@@@@@@@@Mn 18 | 1@@@@@1 ,@@@@@@@@@@@@@@@@@@+ 1@@@@@@, D@@@D ,@@@@ &@@@@@M D@@@@@@@@@@@@@@@@@D 19 | 1@@@@@1 ,@@@@@@@@@@@@@@@@@D M@@@@@1,&@@@D ,@@@@1,M@@@@@1 @@@@@@@@@@@@@@@@@, 20 | 1@@@@@1 ,@@@@@@@@@@@@@@@@@, +@@@@@@@@@@@D ,@@@@@@@@@@@D +@@@@@@@@@@@@@@@@@+ 21 | 1@@@@@1 ,@@@@@@@@@@@@@@@@n ,M@@@@@@@@@&+n@@@@@@@@@@n n@@@@@@@@@@@@@@@@@D 22 | D@@@@1 ,@@@@@@@@@@@@@@M1 1M@@@@@@@@@@@@@@@@@@D, M@@@@@MD1+1nM@@@@@@ 23 | ,++ ++++++++++++, +nM@@@@@@@@@@@MD1 nMMD1, 1DMMD 24 | ,+1nnnn1+, 25 | ''' 26 | 27 | # 在github看香蕉君 28 | # pixel_imgs_dir = './pixel_imgs/github' 29 | # pixel_imgs_resize = 0 # resize pixel_imgs, if 0, do not resize 30 | # output_pixel_num = 52 # how many pixels in the output video'width 31 | # video_path = '../Video/素材/香蕉君/香蕉君_3.mp4' 32 | # inverse = False 33 | 34 | # 用香蕉看香蕉君 35 | pixel_imgs_dir = './pixel_imgs/banana' 36 | pixel_imgs_resize = 40 # resize pixel_imgs, if 0, do not resize 37 | output_pixel_num = 48 # how many pixels in the output video'width 38 | video_path = '../Video/素材/香蕉君/香蕉君_3.mp4' 39 | inverse = True 40 | 41 | # ------------------------- Load Blocks ------------------------- 42 | pixels = [] 43 | pixel_paths = os.listdir(pixel_imgs_dir) 44 | pixel_paths.sort() 45 | if inverse: 46 | pixel_paths.reverse() 47 | for path in pixel_paths: 48 | pixel = cv2.imread(os.path.join(pixel_imgs_dir,path)) 49 | if pixel_imgs_resize != 0: 50 | pixel = cv2.resize(pixel,(pixel_imgs_resize,pixel_imgs_resize)) 51 | pixels.append(pixel) 52 | pixel_size = pixels[0].shape[0] 53 | if len(pixels)>2: 54 | level = 255//len(pixels) 55 | else: 56 | level = 32 57 | print(pixels[0].shape) 58 | 59 | # ------------------------- Prcessing Video ------------------------- 60 | fps,endtime,height,width = ffmpeg.get_video_infos(video_path) 61 | scale = height/width 62 | 63 | util.clean_tempfiles(False) 64 | util.makedirs('./tmp/vid2img') 65 | util.makedirs('./tmp/output_img') 66 | ffmpeg.video2image(video_path, './tmp/vid2img/%05d.png') 67 | ffmpeg.video2voice(video_path, './tmp/tmp.mp3') 68 | 69 | # ------------------------- Video2Block ------------------------- 70 | print('Video2Block...') 71 | img_names = os.listdir('./tmp/vid2img') 72 | img_names.sort() 73 | for img_name in img_names: 74 | img = cv2.imread(os.path.join('./tmp/vid2img',img_name)) 75 | img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) 76 | img = cv2.resize(img, (output_pixel_num,int(output_pixel_num*scale))) 77 | 78 | h,w = img.shape 79 | out_img = np.zeros((h*pixel_size,w*pixel_size,3), dtype = np.uint8) 80 | for i in range(h): 81 | for j in range(w): 82 | index = np.clip(img[i,j]//level,0,len(pixels)-1) 83 | out_img[i*pixel_size:(i+1)*pixel_size,j*pixel_size:(j+1)*pixel_size] = pixels[index] 84 | out_img = out_img[:(h*pixel_size//2)*2,:(w*pixel_size//2)*2] 85 | cv2.imwrite(os.path.join('./tmp/output_img',img_name), out_img) 86 | 87 | # ------------------------- Block2Video ------------------------- 88 | ffmpeg.image2video(fps, './tmp/output_img/%05d.png', './tmp/tmp.mp3', './result.mp4') 89 | -------------------------------------------------------------------------------- /OneImage2Video/pixel_imgs/banana/00.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/HypoX64/bilibili/992029667ad37d7d03131aa2c4c9923da6cca6f2/OneImage2Video/pixel_imgs/banana/00.png -------------------------------------------------------------------------------- /OneImage2Video/pixel_imgs/banana/01.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/HypoX64/bilibili/992029667ad37d7d03131aa2c4c9923da6cca6f2/OneImage2Video/pixel_imgs/banana/01.png -------------------------------------------------------------------------------- /OneImage2Video/pixel_imgs/github/01.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/HypoX64/bilibili/992029667ad37d7d03131aa2c4c9923da6cca6f2/OneImage2Video/pixel_imgs/github/01.png -------------------------------------------------------------------------------- /OneImage2Video/pixel_imgs/github/02.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/HypoX64/bilibili/992029667ad37d7d03131aa2c4c9923da6cca6f2/OneImage2Video/pixel_imgs/github/02.png -------------------------------------------------------------------------------- /OneImage2Video/pixel_imgs/github/03.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/HypoX64/bilibili/992029667ad37d7d03131aa2c4c9923da6cca6f2/OneImage2Video/pixel_imgs/github/03.png -------------------------------------------------------------------------------- /OneImage2Video/pixel_imgs/github/04.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/HypoX64/bilibili/992029667ad37d7d03131aa2c4c9923da6cca6f2/OneImage2Video/pixel_imgs/github/04.png -------------------------------------------------------------------------------- /OneImage2Video/pixel_imgs/github/05.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/HypoX64/bilibili/992029667ad37d7d03131aa2c4c9923da6cca6f2/OneImage2Video/pixel_imgs/github/05.png -------------------------------------------------------------------------------- /OneImage2Video/school_badge.py: -------------------------------------------------------------------------------- 1 | import os 2 | import cv2 3 | import numpy as np 4 | 5 | import sys 6 | sys.path.append("..") 7 | from Util import util,ffmpeg 8 | 9 | 10 | # 用校徽看badapple 11 | imgs_dir = './pixel_imgs/university/base' 12 | highlight_dir = './pixel_imgs/university/highlight' 13 | background_dir = './pixel_imgs/university/background' 14 | cut_size = 79 15 | pixel_resize = 0 # resize pixel_imgs, if 0, do not resize 16 | output_pixel_num = 18 # how many pixels in the output video'width 17 | video_path = '../Video/素材/bad_apple_bbkkbk/BadApple.flv' 18 | change_frame = 2 19 | # ------------------------- Load Blocks ------------------------- 20 | pixels = [] 21 | img_names = os.listdir(imgs_dir) 22 | img_names.sort() 23 | for name in img_names: 24 | img = cv2.imread(os.path.join(imgs_dir,name)) 25 | for h in range(img.shape[0]//cut_size): 26 | for w in range(img.shape[1]//cut_size): 27 | pixel = img[h*cut_size:(h+1)*cut_size,w*cut_size:(w+1)*cut_size] 28 | if pixel_resize != 0: 29 | pixel = cv2.resize(pixel,(pixel_resize,pixel_resize),interpolation=cv2.INTER_AREA) 30 | pixels.append(pixel) 31 | pixel_size = pixels[0].shape[0] 32 | 33 | # highlight 34 | img_names = os.listdir(highlight_dir) 35 | img_names.sort() 36 | for name in img_names: 37 | pixel = cv2.imread(os.path.join(highlight_dir,name)) 38 | pixel = cv2.resize(pixel,(pixel_size,pixel_size),interpolation=cv2.INTER_AREA) 39 | for i in range(10): 40 | pixels.append(pixel) 41 | 42 | pixels = np.array(pixels) 43 | 44 | 45 | # background 46 | background_name = os.listdir(background_dir)[0] 47 | background = cv2.imread(os.path.join(background_dir,background_name)) 48 | background = cv2.resize(background,(pixel_size,pixel_size),interpolation=cv2.INTER_AREA) 49 | 50 | 51 | # ------------------------- Prcessing Video ------------------------- 52 | fps,endtime,height,width = ffmpeg.get_video_infos(video_path) 53 | scale = height/width 54 | 55 | util.clean_tempfiles(False) 56 | util.makedirs('./tmp/vid2img') 57 | util.makedirs('./tmp/output_img') 58 | ffmpeg.video2image(video_path, './tmp/vid2img/%05d.png') 59 | ffmpeg.video2voice(video_path, './tmp/tmp.mp3') 60 | 61 | # ------------------------- Video2Block ------------------------- 62 | print('Video2Block...') 63 | img_names = os.listdir('./tmp/vid2img') 64 | img_names.sort() 65 | frame = 0 66 | for img_name in img_names: 67 | img = cv2.imread(os.path.join('./tmp/vid2img',img_name)) 68 | img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) 69 | img = cv2.resize(img, (output_pixel_num,int(output_pixel_num*scale)),interpolation=cv2.INTER_AREA) 70 | 71 | h,w = img.shape 72 | if frame %change_frame == 0: 73 | indexs = np.random.randint(0, pixels.shape[0]-1, (h,w)) 74 | out_img = np.zeros((h*pixel_size,w*pixel_size,3), dtype = np.uint8) 75 | for i in range(h): 76 | for j in range(w): 77 | #index = np.clip(img[i,j]//level,0,len(pixels)-1) 78 | if img[i,j] < 64: 79 | out_img[i*pixel_size:(i+1)*pixel_size,j*pixel_size:(j+1)*pixel_size] = pixels[indexs[i,j]] 80 | else: 81 | out_img[i*pixel_size:(i+1)*pixel_size,j*pixel_size:(j+1)*pixel_size] = background 82 | 83 | out_img = out_img[:(h*pixel_size//2)*2,:(w*pixel_size//2)*2] 84 | cv2.imwrite(os.path.join('./tmp/output_img',img_name), out_img) 85 | frame += 1 86 | # ------------------------- Block2Video ------------------------- 87 | ffmpeg.image2video(fps, './tmp/output_img/%05d.png', './tmp/tmp.mp3', './result.mp4') 88 | -------------------------------------------------------------------------------- /PlayMusic/README.md: -------------------------------------------------------------------------------- 1 | # 根据简谱自动生成音乐 2 | ## Example :TwinkleTwinkleLittleStar.txt 3 | ```python 4 | D 　　　 　#调号 5 | 2/4　　　　#节拍 6 | 108　　　　#播放速度 7 | 0.8　　　　#每个音符的相对持续时间 8 | 1,1　 #　do ,１拍 9 | 1,1　 #　do ,１拍 10 | 5,1　 #　so,１拍 11 | 5,1　 #　so,１拍 12 | 6,1　 #　la,１拍 13 | 6,1　 #　la,１拍 14 | 5,1　 #　so,１拍 15 | 1,0.5　 #　do ,0.5拍 16 | -,1　 #　-,１拍 17 | 1+　 #　升8度do,１拍 18 | 1--　 #　降两个8度do,１拍 19 | ``` -------------------------------------------------------------------------------- /PlayMusic/music/SchoolBell.txt: -------------------------------------------------------------------------------- 1 | C 2 | 4/4 3 | 50 4 | 1.5 5 | 3,1 6 | 1,1 7 | 2,1 8 | 5-,1 9 | -,1 10 | 5-,1 11 | 2,1 12 | 3,1 13 | 1,1 14 | -,1 15 | 3,1 16 | 2,1 17 | 1,1 18 | 5-,1 19 | -,1 20 | 5-,1 21 | 2,1 22 | 3,1 23 | 1,1 24 | -,1 25 | 1,1.25 26 | -,1 27 | 1,1.25 28 | -,1 29 | 1,1.25 30 | -------------------------------------------------------------------------------- /PlayMusic/music/TwinkleTwinkleLittleStar.txt: -------------------------------------------------------------------------------- 1 | D 2 | 2/4 3 | 108 4 | 0.8 5 | 1,1 6 | 1,1 7 | 5,1 8 | 5,1 9 | 6,1 10 | 6,1 11 | 5,1 12 | -,1 13 | 4,1 14 | 4,1 15 | 3,1 16 | 3,0.5 17 | 2,1 18 | 2,1 19 | 1,1 20 | -,1 21 | 5,1 22 | 5,1 23 | 4,1 24 | 4,1 25 | 3,1 26 | 3,1 27 | 2,1 28 | -,1 29 | 5,1 30 | 5,1 31 | 4,1 32 | 4,1 33 | 3,1 34 | 3,1 35 | 2,1 36 | -,1 37 | 1,1 38 | 1,1 39 | 5,1 40 | 5,1 41 | 6,1 42 | 6,1 43 | 5,1 44 | -,1 45 | 4,1 46 | 4,1 47 | 3,1 48 | 3,0.5 49 | 3,0.5 50 | 2,1 51 | 2,1 52 | 1,1 53 | -,1 -------------------------------------------------------------------------------- /PlayMusic/notation.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | import random 4 | import time 5 | import numpy as np 6 | import matplotlib.pylab as plt 7 | 8 | sys.path.append("..") 9 | from Util import util,ffmpeg,dsp,sound 10 | from Util import array_operation as arrop 11 | 12 | 13 | """音调对应的频率 14 | * C大调中就是1=C中C唱做do,而在1=D中D要唱作do 15 | * C D E F G A B -> do re mi fa so la xi 16 | * 暂时不支持C# D# F# A# 17 | """ 18 | NOTES = ['C','D','E','F','G','A','B'] 19 | base_pitch = [16.352,18.354,20.602,21.827,24.500,27.500,30.868] 20 | #Frequency in hertz (semitones above or below middle C) 21 | OPS = np.zeros(7*10) 22 | for i in range(10): 23 | for j in range(7): 24 | OPS[i*7+j] = base_pitch[j]*(2**i) 25 | 26 | def getfreq(note,PN): 27 | if note[0] == '-': 28 | return 0,1 29 | index = 4*7-1 + NOTES.index(PN) + int(note[0]) #1 = C or D..... 30 | if '+' in note: 31 | index += (len(note)-1)*7 32 | elif '-' in note: 33 | index -= (len(note)-1)*7 34 | freq = OPS[index] 35 | timescale = np.clip(1/(1.08**(index-33)),0,4.0) 36 | return freq,timescale 37 | 38 | def wave(f, fs, time, mode='sin'): 39 | length = int(fs*time) 40 | signal = dsp.wave(f, fs, time, mode) 41 | weight = np.zeros(length) 42 | weight[0:length//10] = np.hanning(length//10*2)[0:length//10] 43 | weight[length//10:] = np.hanning(int(length*0.9*2))[-(length-length//10):] 44 | return signal*weight 45 | 46 | 47 | """拍子强度 48 | strengths = {'2/4':['++',''], 49 | '3/4':['++','',''], 50 | '4/4':['++','','+',''], 51 | '3/8':['++','',''], 52 | '6/8':['++','','','+','','']} 53 | """ 54 | 55 | def getstrength(i,BN,x): 56 | if int(BN[0]) == 2: 57 | if i%2 == 0: 58 | x = x*1.25 59 | elif int(BN[0]) == 3: 60 | if i%3 == 0: 61 | x = x*1.25 62 | elif int(BN[0]) == 4: 63 | if i%4 == 0: 64 | x = x*1.25 65 | elif i%4 == 2: 66 | x = x*1.125 67 | return x 68 | 69 | def readscore(path): 70 | notations = {} 71 | notations['data'] =[] 72 | for i,line in enumerate(open(path),0): 73 | line = line.strip('\n') 74 | if i==0: 75 | notations['PN'] = line 76 | elif i == 1: 77 | notations['BN'] = line 78 | elif i == 2: 79 | notations['BPM'] = float(line) 80 | elif i == 3: 81 | notations['PNLT'] = float(line) 82 | else: 83 | if len(line)>2: 84 | beat = line.split(';') 85 | part = [] 86 | for i in range(len(beat)): 87 | part.append(beat[i].split('|')) 88 | notations['data'].append(part) 89 | return notations 90 | 91 | 92 | def notations2music(notations, mode = 'sin', isplot = False): 93 | BPM = notations['BPM'] 94 | BN = notations['BN'] 95 | PNLT = notations['PNLT'] 96 | interval = 60.0/BPM 97 | fs = 44100 98 | time = 0 99 | 100 | music = np.zeros(int(fs*(len(notations['data'])+8)*interval)) 101 | 102 | for section in range(len(notations['data'])): 103 | for beat in range(len(notations['data'][section])): 104 | 105 | _music = np.zeros(int(fs*PNLT*4)) 106 | for part in range(len(notations['data'][section][beat])): 107 | _note = notations['data'][section][beat][part].split(',')[0] 108 | freq,timescale = getfreq(_note,notations['PN']) 109 | # print(timescale) 110 | if freq != 0: 111 | # print(len(_music[:int(PNLT*timescale*fs)])) 112 | # print(len(wave(freq, fs, PNLT*timescale, mode = mode))) 113 | _music[:int(PNLT*timescale*fs)] += wave(freq, fs, PNLT*timescale, mode = mode) 114 | # print(notations['data'][section][beat][part]) 115 | 116 | music[int(time*fs):int(time*fs)+int(PNLT*fs*4)] += _music 117 | time += float(notations['data'][section][beat][0].split(',')[1])*interval 118 | # for i in range(len(notations['data'])): 119 | # for j in range(len(notations['data'][i])//2): 120 | # freq = getfreq(notations['data'][i][j*2],notations['PN']) 121 | # if freq != 0: 122 | # music[int(time*fs):int(time*fs)+int(PNLT*fs)] += wave(freq, fs, PNLT, mode = mode) 123 | # music[int(time*fs):int(time*fs)+int(PNLT*fs)] += getstrength(i,BN,wave(freq, fs, PNLT, mode = mode)) 124 | 125 | # if isplot: 126 | # plot_data = music[int(time*fs):int(time*fs)+int(PNLT*fs)] 127 | # plt.clf() 128 | # plt.subplot(221) 129 | # plt.plot(np.linspace(0, len(plot_data)/fs,len(plot_data)),plot_data) 130 | # plt.title('Current audio waveform') 131 | # plt.xlabel('Time') 132 | # plt.ylim((-1.5,1.5)) 133 | 134 | # plt.subplot(222) 135 | # _plot_data = plot_data[int(len(plot_data)*0.2):int(len(plot_data)*0.25)] 136 | # plt.plot(np.linspace(0, len(_plot_data)/fs,len(_plot_data)),_plot_data) 137 | # plt.title('Partial audio waveform') 138 | # plt.xlabel('Time') 139 | # plt.ylim((-1.5,1.5)) 140 | 141 | # plt.subplot(223) 142 | # f,k = dsp.showfreq(plot_data, 44100, 2000) 143 | # plt.plot(f,k) 144 | # plt.title('FFT') 145 | # plt.xlabel('Hz') 146 | # plt.ylim((-1000,10000)) 147 | # plt.pause(interval-0.125) 148 | 149 | #time += float(notations['data'][i][j*2+1])*interval 150 | 151 | return (arrop.sigmoid(music)-0.5)*65536 152 | 153 | notations = readscore('./music/SchoolBell.txt') 154 | print(notations) 155 | print(len(notations['data'])) 156 | # sin triangle square 157 | music = notations2music(notations,mode='sin',isplot=False) 158 | sound.playtest(music) 159 | 160 | # import threading 161 | # t=threading.Thread(target=sound.playtest,args=(music,)) 162 | # t.start() 163 | #music = notations2music(notations,mode='sin',isplot=True) 164 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # bilibili 2 | ### 代码长大了，会帮up主做视频了
3 | 4 | | 项目名(单击项目名进入项目页) | 演示(单击图片或文字观看视频) | 教程(单击图片或文字观看视频) | 5 | | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | 6 | | [DeepMosaics](https://github.com/HypoX64/DeepMosaics)
自动对视频/图片去码或打码 | / | [![image](./imgs/DeepMosaics.jpg)](https://www.bilibili.com/video/BV1LJ411c7Hg)
[一键去除照片和视频中的马赛克——Deepmosaics软件使用教程](https://www.bilibili.com/video/BV1LJ411c7Hg) | 7 | | [Plogo](https://github.com/HypoX64/Plogo)
Pornhub 风格的视频模板 | / | [![image](./imgs/Plogo.jpg)](https://www.bilibili.com/video/BV1gC4y1p7gC)
[教程\]5分钟快速制作一个自己专属的P站片头——模板分享](https://www.bilibili.com/video/BV1gC4y1p7gC) | 8 | | [ShellPlayer](https://github.com/HypoX64/ShellPlayer)
在终端中看视频，色彩斑斓且富有声音的 | [![image](./imgs/ShellPlayer_大威天龙.jpg)](https://www.bilibili.com/video/BV1dT4y1u76w)
[大威天龙](https://www.bilibili.com/video/BV1dT4y1u76w)
[鸡你太美](https://www.bilibili.com/video/BV1Ag4y1z7BB)
[黑人抬棺](https://www.bilibili.com/video/BV16V411C7xy) | [![image](./imgs/ShellPlayer_教程.jpg)](https://www.bilibili.com/video/BV18V411r7U1)
[【教程】如何一键将视频转化为彩色的字符动画](https://www.bilibili.com/video/BV18V411r7U1) | 9 | | [OneImage2Video](https://github.com/HypoX64/bilibili/tree/master/OneImage2Video)
用一个或一些图片生成视频 | [![image](./imgs/OneImage2Video_badapple.jpg)](https://www.bilibili.com/video/BV1dT4y1u76w)
[BadApple](https://www.bilibili.com/video/BV1w54y1z72s)
[香蕉君](https://www.bilibili.com/video/BV1Vg4y1v7s) | / | 10 | | [GuiChu](https://github.com/HypoX64/GuiChu)
鬼畜视频生成器 | [![image](./imgs/GuiChu_余生一个浪.jpg)](https://www.bilibili.com/video/BV1c54y1Q7RG)
[你猫和老鼠X余生一个浪](https://www.bilibili.com/video/BV1c54y1Q7RG)
[克 罗 地 亚 灭 蚊 狂 想 曲](https://www.bilibili.com/video/BV1ft4y1X7q6) | / | 11 | | [PlayMusic](./PlayMusic/README.md)
根据简谱自动生成音乐 | [![image](./imgs/PlayMusic_小星星.jpg)](https://www.bilibili.com/video/BV1Dv411z7H1)
[小星星](https://www.bilibili.com/video/BV1Dv411z7H1) | / | 12 | | [syseye](https://github.com/HypoX64/syseye)
轻量级深度学习服务器性能监视器 | / | / | 13 | 14 | -------------------------------------------------------------------------------- /Util/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/HypoX64/bilibili/992029667ad37d7d03131aa2c4c9923da6cca6f2/Util/__init__.py -------------------------------------------------------------------------------- /Util/array_operation.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | def interp(y,length): 4 | xp = np.linspace(0, len(y)-1,num = len(y)) 5 | fp = y 6 | x = np.linspace(0, len(y)-1,num = length) 7 | return np.interp(x, xp, fp) 8 | 9 | def sigmoid(x): 10 | return 1.0 / (1.0 + np.exp(-x)); 11 | 12 | def pad(data,padding): 13 | pad_data = np.zeros(padding, dtype=data.dtype) 14 | data = np.append(data, pad_data) 15 | return data 16 | 17 | def normliaze(data, mode = 'norm', sigma = 0, dtype=np.float32, truncated = 2): 18 | ''' 19 | mode: norm | std | maxmin | 5_95 20 | dtype : np.float64,np.float16... 21 | ''' 22 | data = data.astype(dtype) 23 | data_calculate = data.copy() 24 | if mode == 'norm': 25 | result = (data-np.mean(data_calculate))/sigma 26 | elif mode == 'std': 27 | mu = np.mean(data_calculate) 28 | sigma = np.std(data_calculate) 29 | result = (data - mu) / sigma 30 | elif mode == 'maxmin': 31 | result = (data-np.mean(data_calculate))/(max(np.max(data_calculate),np.abs(np.min(data_calculate)))) 32 | elif mode == '5_95': 33 | data_sort = np.sort(data_calculate,axis=None) 34 | th5 = data_sort[int(0.05*len(data_sort))] 35 | th95 = data_sort[int(0.95*len(data_sort))] 36 | baseline = (th5+th95)/2 37 | sigma = (th95-th5)/2 38 | if sigma == 0: 39 | sigma = 1e-06 40 | result = (data-baseline)/sigma 41 | 42 | if truncated > 1: 43 | result = np.clip(result, (-truncated), (truncated)) 44 | 45 | return result.astype(dtype) 46 | 47 | def diff1d(indata,stride=1,padding=1,bias=False): 48 | 49 | pad = np.zeros(padding) 50 | indata = np.append(indata, pad) 51 | if bias: 52 | if np.min(indata)<0: 53 | indata = indata - np.min(indata) 54 | 55 | outdata = np.zeros(int(len(indata)/stride)-1) 56 | for i in range(int(len(indata)/stride)-1): 57 | outdata[i]=indata[i*stride+stride]-indata[i*stride] 58 | return outdata 59 | 60 | 61 | def findpeak(indata, ismax=False, interval=10, threshold=0.1, reverse=False): 62 | ''' 63 | return:indexs 64 | ''' 65 | indexs = [] 66 | if reverse: 67 | indata = -1*indata 68 | indexs = [0,len(indata)-1] 69 | else: 70 | indata = np.clip(indata, np.max(indata)*threshold, np.max(indata)) 71 | diff = diff1d(indata) 72 | if ismax: 73 | return np.array([np.argmax(indata)]) 74 | 75 | rise = True 76 | if diff[0] <=0: 77 | rise = False 78 | for i in range(len(diff)): 79 | if rise==True and diff[i]<=0: 80 | index = i 81 | ok_flag = True 82 | for x in range(interval): 83 | if indata[np.clip(index-x,0,len(indata)-1)]>indata[index] or indata[np.clip(index+x,0,len(indata)-1)]>indata[index]: 84 | ok_flag = False 85 | if ok_flag: 86 | indexs.append(index) 87 | 88 | if diff[i] <=0: 89 | rise = False 90 | else: 91 | rise = True 92 | 93 | return np.sort(np.array(indexs)) 94 | 95 | def crop(data,indexs,size): 96 | if len(data) < size: 97 | data = pad(data, size-len(data)) 98 | 99 | crop_result = [] 100 | for index in indexs: 101 | if index-int(size/2)<0: 102 | crop_result.append(data[0:size]) 103 | elif index+int(size/2)<0: 104 | crop_result.append(data[len(data)-size:len(data)]) 105 | else: 106 | crop_result.append(data[index-int(size/2):index+int(size/2)]) 107 | return np.array(crop_result) 108 | 109 | def cropbyindex(data,indexs,reverse_indexs): 110 | crop_result = [] 111 | crop_index = [] 112 | for index in indexs: 113 | for i in range(len(reverse_indexs)-1): 114 | if reverse_indexs[i] < index: 115 | if reverse_indexs[i+1] > index: 116 | crop_result.append(data[reverse_indexs[i]:reverse_indexs[i+1]]) 117 | crop_index.append([reverse_indexs[i],reverse_indexs[i+1]]) 118 | return np.array(crop_result),np.array(crop_index) 119 | 120 | def get_crossing(line1,line2): 121 | cross_pos = [] 122 | dif = line1-line2 123 | flag = 1 124 | if dif[0]<0: 125 | dif = -dif 126 | for i in range(int(len(dif))): 127 | if flag == 1: 128 | if dif[i] <= 0: 129 | cross_pos.append(i) 130 | flag = 0 131 | else: 132 | if dif[i] >= 0: 133 | cross_pos.append(i) 134 | flag = 1 135 | return cross_pos 136 | 137 | def get_y(indexs,fun): 138 | y = [] 139 | for index in indexs: 140 | y.append(fun[index]) 141 | return np.array(y) 142 | 143 | def fillnone(arr_in,flag,num = 7): 144 | arr = arr_in.copy() 145 | index = np.linspace(0,len(arr)-1,len(arr),dtype='int') 146 | cnt = 0 147 | for i in range(2,len(arr)-2): 148 | if arr[i] != flag: 149 | arr[i] = arr[i] 150 | if cnt != 0: 151 | if cnt <= num*2: 152 | arr[i-cnt:round(i-cnt/2)] = arr[i-cnt-1-2] 153 | arr[round(i-cnt/2):i] = arr[i+2] 154 | index[i-cnt:round(i-cnt/2)] = i-cnt-1-2 155 | index[round(i-cnt/2):i] = i+2 156 | else: 157 | arr[i-cnt:i-cnt+num] = arr[i-cnt-1-2] 158 | arr[i-num:i] = arr[i+2] 159 | index[i-cnt:i-cnt+num] = i-cnt-1-2 160 | index[i-num:i] = i+2 161 | cnt = 0 162 | else: 163 | cnt += 1 164 | return arr,index 165 | 166 | 167 | def closest(a,array): 168 | ''' 169 | return:index 170 | ''' 171 | 172 | corrcoefs = [] 173 | for i in range(len(array)): 174 | #corrcoefs.append(np.linalg.norm(a-array[i], ord=1)) 175 | corrcoefs.append(np.corrcoef(a,array[i])[0][1]) 176 | return corrcoefs.index(np.max(corrcoefs)) 177 | 178 | def match(src,dst): 179 | ''' 180 | return:dstindexs 181 | ''' 182 | indexs = [] 183 | for i in range(len(src)): 184 | indexs.append(closest(src[i], dst)) 185 | return np.array(indexs) 186 | 187 | 188 | 189 | def main(): 190 | a = [0,2,4,6,8,10] 191 | print(interp(a, 6)) 192 | if __name__ == '__main__': 193 | main() -------------------------------------------------------------------------------- /Util/dsp.py: -------------------------------------------------------------------------------- 1 | import scipy 2 | import scipy.signal 3 | import scipy.fftpack 4 | import numpy as np 5 | from .array_operation import * 6 | import matplotlib.pylab as plt 7 | 8 | def sin(f,fs,time,theta=0): 9 | x = np.linspace(0, 2*np.pi*f*time, int(fs*time)) 10 | return np.sin(x+theta) 11 | 12 | def wave(f,fs,time,mode='sin'): 13 | f,fs,time = float(f),float(fs),float(time) 14 | if mode == 'sin': 15 | return sin(f,fs,time,theta=0) 16 | elif mode == 'square': 17 | half_T_num = int(time*f)*2 + 1 18 | half_T_point = int(fs/f/2) 19 | x = np.zeros(int(fs*time)+2*half_T_point) 20 | for i in range(half_T_num): 21 | if i%2 == 0: 22 | x[i*half_T_point:(i+1)*half_T_point] = -1 23 | else: 24 | x[i*half_T_point:(i+1)*half_T_point] = 1 25 | return x[:int(fs*time)] 26 | elif mode == 'triangle': 27 | half_T_num = int(time*f)*2 + 1 28 | half_T_point = int(fs/f/2) 29 | up = np.linspace(-1, 1, half_T_point) 30 | down = np.linspace(1, -1, half_T_point) 31 | x = np.zeros(int(fs*time)+2*half_T_point) 32 | for i in range(half_T_num): 33 | if i%2 == 0: 34 | x[i*half_T_point:(i+1)*half_T_point] = up.copy() 35 | else: 36 | x[i*half_T_point:(i+1)*half_T_point] = down.copy() 37 | return x[:int(fs*time)] 38 | 39 | def downsample(signal,fs1=0,fs2=0,alpha=0,mod = 'just_down'): 40 | if alpha ==0: 41 | alpha = int(fs1/fs2) 42 | if mod == 'just_down': 43 | return signal[::alpha] 44 | elif mod == 'avg': 45 | result = np.zeros(int(len(signal)/alpha)) 46 | for i in range(int(len(signal)/alpha)): 47 | result[i] = np.mean(signal[i*alpha:(i+1)*alpha]) 48 | return result 49 | 50 | def medfilt(signal,x): 51 | return scipy.signal.medfilt(signal,x) 52 | 53 | def cleanoffset(signal): 54 | return signal - np.mean(signal) 55 | 56 | def bpf(signal, fs, fc1, fc2, numtaps=3, mode='iir'): 57 | if mode == 'iir': 58 | b,a = scipy.signal.iirfilter(numtaps, [fc1,fc2], fs=fs) 59 | elif mode == 'fir': 60 | b = scipy.signal.firwin(numtaps, [fc1, fc2], pass_zero=False,fs=fs) 61 | a = 1 62 | return scipy.signal.lfilter(b, a, signal) 63 | 64 | def fft_filter(signal,fs,fc=[],type = 'bandpass'): 65 | ''' 66 | signal: Signal 67 | fs: Sampling frequency 68 | fc: [fc1,fc2...] Cut-off frequency 69 | type: bandpass | bandstop 70 | ''' 71 | k = [] 72 | N=len(signal)#get N 73 | 74 | for i in range(len(fc)): 75 | k.append(int(fc[i]*N/fs)) 76 | 77 | #FFT 78 | signal_fft=scipy.fftpack.fft(signal) 79 | #Frequency truncation 80 | 81 | if type == 'bandpass': 82 | a = np.zeros(N) 83 | for i in range(int(len(fc)/2)): 84 | a[k[2*i]:k[2*i+1]] = 1 85 | a[N-k[2*i+1]:N-k[2*i]] = 1 86 | elif type == 'bandstop': 87 | a = np.ones(N) 88 | for i in range(int(len(fc)/2)): 89 | a[k[2*i]:k[2*i+1]] = 0 90 | a[N-k[2*i+1]:N-k[2*i]] = 0 91 | signal_fft = a*signal_fft 92 | signal_ifft=scipy.fftpack.ifft(signal_fft) 93 | result = signal_ifft.real 94 | return result 95 | 96 | def basefreq(signal,fs,fc=0): 97 | if fc==0: 98 | kc = int(len(signal)/2) 99 | else: 100 | kc = int(len(signal)/fs*fc) 101 | length = len(signal) 102 | signal_fft = np.abs(scipy.fftpack.fft(signal))[:kc] 103 | _sum = np.sum(signal_fft)/2 104 | tmp_sum = 0 105 | for i in range(kc): 106 | tmp_sum += signal_fft[i] 107 | if tmp_sum > _sum: 108 | return i/(length/fs) 109 | 110 | def showfreq(signal,fs,fc=0): 111 | """ 112 | return f,fft 113 | """ 114 | if fc==0: 115 | kc = int(len(signal)/2) 116 | else: 117 | kc = int(len(signal)/fs*fc) 118 | signal_fft = np.abs(scipy.fftpack.fft(signal)) 119 | f = np.linspace(0,fs/2,num=int(len(signal_fft)/2)) 120 | return f[:kc],signal_fft[0:int(len(signal_fft)/2)][:kc] 121 | 122 | def rms(signal): 123 | signal = signal.astype('float64') 124 | return np.mean((signal*signal))**0.5 125 | 126 | def energy(signal,kernel_size,stride,padding = 0): 127 | _signal = np.zeros(len(signal)+padding) 128 | _signal[0:len(signal)] = signal 129 | signal = _signal 130 | out_len = int((len(signal)+1-kernel_size)/stride) 131 | energy = np.zeros(out_len) 132 | for i in range(out_len): 133 | energy[i] = rms(signal[i*stride:i*stride+kernel_size]) 134 | return energy 135 | 136 | def envelope_demodulation(signal,kernel_size,alpha = 0.9,mod='max'): 137 | out_len = int(len(signal)/kernel_size) 138 | envelope = np.zeros(out_len) 139 | for i in range(out_len): 140 | # envelope[i] = np.max(signal[i*kernel_size:(i+1)*kernel_size]) 141 | envelope[i] = np.sort(signal[i*kernel_size:(i+1)*kernel_size])[int(alpha*kernel_size)] 142 | return envelope 143 | 144 | def main(): 145 | print(downsample(piano,alpha=9)) 146 | if __name__ == '__main__': 147 | main() 148 | 149 | -------------------------------------------------------------------------------- /Util/ffmpeg.py: -------------------------------------------------------------------------------- 1 | import os,json 2 | import subprocess 3 | 4 | # ffmpeg 3.4.6 5 | 6 | def args2cmd(args): 7 | cmd = '' 8 | for arg in args: 9 | cmd += (arg+' ') 10 | return cmd 11 | 12 | def run(args,mode = 0): 13 | 14 | if mode == 0: 15 | cmd = args2cmd(args) 16 | os.system(cmd) 17 | 18 | elif mode == 1: 19 | ''' 20 | out_string = os.popen(cmd_str).read() 21 | For chinese path in Windows 22 | https://blog.csdn.net/weixin_43903378/article/details/91979025 23 | ''' 24 | cmd = args2cmd(args) 25 | stream = os.popen(cmd)._stream 26 | sout = stream.buffer.read().decode(encoding='utf-8') 27 | return sout 28 | 29 | elif mode == 2: 30 | cmd = args2cmd(args) 31 | p = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) 32 | sout = p.stdout.readlines() 33 | return sout 34 | 35 | 36 | def video2image(videopath, imagepath, fps=0, start_time='00:00:00', last_time='00:00:00'): 37 | args = ['ffmpeg', '-i', '"'+videopath+'"'] 38 | if last_time != '00:00:00': 39 | args += ['-ss', start_time] 40 | args += ['-t', last_time] 41 | if fps != 0: 42 | args += ['-r', str(fps)] 43 | args += ['-f', 'image2','-q:v','-0',imagepath] 44 | run(args) 45 | 46 | def video2voice(videopath, voicepath, start_time='00:00:00', last_time='00:00:00'): 47 | args = ['ffmpeg', '-i', '"'+videopath+'"','-f mp3','-b:a 320k'] 48 | if last_time != '00:00:00': 49 | args += ['-ss', start_time] 50 | args += ['-t', last_time] 51 | args += [voicepath] 52 | run(args) 53 | 54 | def image2video(fps,imagepath,voicepath,videopath): 55 | if voicepath != None: 56 | os.system('ffmpeg -y -r '+str(fps)+' -i '+imagepath+' -vcodec libx264 '+'./tmp/video_tmp.mp4') 57 | os.system('ffmpeg -i ./tmp/video_tmp.mp4 -i "'+voicepath+'" -vcodec copy -acodec aac '+videopath) 58 | else: 59 | os.system('ffmpeg -y -r '+str(fps)+' -i '+imagepath+' -vcodec libx264 '+videopath) 60 | 61 | def get_video_infos(videopath): 62 | args = ['ffprobe -v quiet -print_format json -show_format -show_streams', '-i', '"'+videopath+'"'] 63 | 64 | out_string = run(args,mode=1) 65 | 66 | infos = json.loads(out_string) 67 | try: 68 | fps = eval(infos['streams'][0]['avg_frame_rate']) 69 | endtime = float(infos['format']['duration']) 70 | width = int(infos['streams'][0]['width']) 71 | height = int(infos['streams'][0]['height']) 72 | except Exception as e: 73 | fps = eval(infos['streams'][1]['r_frame_rate']) 74 | endtime = float(infos['format']['duration']) 75 | width = int(infos['streams'][1]['width']) 76 | height = int(infos['streams'][1]['height']) 77 | 78 | return fps,endtime,height,width 79 | 80 | def cut_video(in_path,start_time,last_time,out_path,vcodec='h265'): 81 | if vcodec == 'copy': 82 | os.system('ffmpeg -ss '+start_time+' -t '+last_time+' -i "'+in_path+'" -vcodec copy -acodec copy '+out_path) 83 | elif vcodec == 'h264': 84 | os.system('ffmpeg -ss '+start_time+' -t '+last_time+' -i "'+in_path+'" -vcodec libx264 -b 12M '+out_path) 85 | elif vcodec == 'h265': 86 | os.system('ffmpeg -ss '+start_time+' -t '+last_time+' -i "'+in_path+'" -vcodec libx265 -b 12M '+out_path) 87 | 88 | def continuous_screenshot(videopath,savedir,fps): 89 | ''' 90 | videopath: input video path 91 | savedir: images will save here 92 | fps: save how many images per second 93 | ''' 94 | videoname = os.path.splitext(os.path.basename(videopath))[0] 95 | os.system('ffmpeg -i "'+videopath+'" -vf fps='+str(fps)+' -q:v -0 '+savedir+'/'+videoname+'_%06d.jpg') 96 | -------------------------------------------------------------------------------- /Util/image_processing.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import numpy as np 3 | import random 4 | 5 | import platform 6 | system_type = 'Linux' 7 | if 'Windows' in platform.platform(): 8 | system_type = 'Windows' 9 | 10 | def imread(file_path,mod = 'normal'): 11 | ''' 12 | mod = 'normal' | 'gray' | 'all' 13 | ''' 14 | if system_type == 'Linux': 15 | if mod == 'normal': 16 | img = cv2.imread(file_path) 17 | elif mod == 'gray': 18 | img = cv2.imread(file_path,0) 19 | elif mod == 'all': 20 | img = cv2.imread(file_path,-1) 21 | 22 | #For chinese path, use cv2.imdecode in windows. 23 | #It will loss EXIF, I can't fix it 24 | else: 25 | if mod == 'gray': 26 | img = cv2.imdecode(np.fromfile(file_path,dtype=np.uint8),0) 27 | else: 28 | img = cv2.imdecode(np.fromfile(file_path,dtype=np.uint8),-1) 29 | 30 | return img 31 | 32 | def imwrite(file_path,img): 33 | ''' 34 | in other to save chinese path images in windows, 35 | this fun just for save final output images 36 | ''' 37 | if system_type == 'Linux': 38 | cv2.imwrite(file_path, img) 39 | else: 40 | cv2.imencode('.jpg', img)[1].tofile(file_path) 41 | 42 | def resize(img,size,interpolation=cv2.INTER_LINEAR): 43 | h, w = img.shape[:2] 44 | if np.min((w,h)) ==size: 45 | return img 46 | if w >= h: 47 | res = cv2.resize(img,(int(size*w/h), size),interpolation=interpolation) 48 | else: 49 | res = cv2.resize(img,(size, int(size*h/w)),interpolation=interpolation) 50 | return res 51 | 52 | def resize_like(img,img_like): 53 | h, w = img_like.shape[:2] 54 | img = cv2.resize(img, (w,h)) 55 | return img 56 | 57 | def ch_one2three(img): 58 | #zeros = np.zeros(img.shape[:2], dtype = "uint8") 59 | # ret,thresh = cv2.threshold(img,127,255,cv2.THRESH_BINARY) 60 | res = cv2.merge([img, img, img]) 61 | return res 62 | 63 | def color_adjust(img,alpha=1,beta=0,b=0,g=0,r=0,ran = False): 64 | ''' 65 | g(x) = (1+α)g(x)+255*β, 66 | g(x) = g(x[:+b*255,:+g*255,:+r*255]) 67 | 68 | Args: 69 | img : input image 70 | alpha : contrast 71 | beta : brightness 72 | b : blue hue 73 | g : green hue 74 | r : red hue 75 | ran : if True, randomly generated color correction parameters 76 | Retuens: 77 | img : output image 78 | ''' 79 | img = img.astype('float') 80 | if ran: 81 | alpha = random.uniform(-0.2,0.2) 82 | beta = random.uniform(-0.2,0.2) 83 | b = random.uniform(-0.1,0.1) 84 | g = random.uniform(-0.1,0.1) 85 | r = random.uniform(-0.1,0.1) 86 | img = (1+alpha)*img+255.0*beta 87 | bgr = [b*255.0,g*255.0,r*255.0] 88 | for i in range(3): img[:,:,i]=img[:,:,i]+bgr[i] 89 | 90 | return (np.clip(img,0,255)).astype('uint8') 91 | 92 | def makedataset(target_image,orgin_image): 93 | target_image = resize(target_image,256) 94 | orgin_image = resize(orgin_image,256) 95 | img = np.zeros((256,512,3), dtype = "uint8") 96 | w = orgin_image.shape[1] 97 | img[0:256,0:256] = target_image[0:256,int(w/2-256/2):int(w/2+256/2)] 98 | img[0:256,256:512] = orgin_image[0:256,int(w/2-256/2):int(w/2+256/2)] 99 | return img 100 | 101 | def image2folat(img,ch): 102 | size=img.shape[0] 103 | if ch == 1: 104 | img = (img[:,:,0].reshape(1,size,size)/255.0).astype(np.float32) 105 | else: 106 | img = (img.transpose((2, 0, 1))/255.0).astype(np.float32) 107 | return img 108 | 109 | def spiltimage(img,size = 128): 110 | h, w = img.shape[:2] 111 | # size = min(h,w) 112 | if w >= h: 113 | img1 = img[:,0:size] 114 | img2 = img[:,w-size:w] 115 | else: 116 | img1 = img[0:size,:] 117 | img2 = img[h-size:h,:] 118 | 119 | return img1,img2 120 | 121 | def mergeimage(img1,img2,orgin_image,size = 128): 122 | h, w = orgin_image.shape[:2] 123 | new_img1 = np.zeros((h,w), dtype = "uint8") 124 | new_img2 = np.zeros((h,w), dtype = "uint8") 125 | 126 | # size = min(h,w) 127 | if w >= h: 128 | new_img1[:,0:size]=img1 129 | new_img2[:,w-size:w]=img2 130 | else: 131 | new_img1[0:size,:]=img1 132 | new_img2[h-size:h,:]=img2 133 | result_img = cv2.add(new_img1,new_img2) 134 | return result_img 135 | 136 | def find_best_ROI(mask): 137 | contours,hierarchy=cv2.findContours(mask, cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE) 138 | if len(contours)>0: 139 | areas = [] 140 | for contour in contours: 141 | areas.append(cv2.contourArea(contour)) 142 | index = areas.index(max(areas)) 143 | mask = np.zeros_like(mask) 144 | mask = cv2.fillPoly(mask,[contours[index]],(255)) 145 | return mask 146 | 147 | def boundingSquare(mask,Ex_mul): 148 | # thresh = mask_threshold(mask,10,threshold) 149 | area = mask_area(mask) 150 | if area == 0 : 151 | return 0,0,0,0 152 | 153 | x,y,w,h = cv2.boundingRect(mask) 154 | 155 | center = np.array([int(x+w/2),int(y+h/2)]) 156 | size = max(w,h) 157 | point0=np.array([x,y]) 158 | point1=np.array([x+size,y+size]) 159 | 160 | h, w = mask.shape[:2] 161 | if size*Ex_mul > min(h, w): 162 | size = min(h, w) 163 | halfsize = int(min(h, w)/2) 164 | else: 165 | size = Ex_mul*size 166 | halfsize = int(size/2) 167 | size = halfsize*2 168 | point0 = center - halfsize 169 | point1 = center + halfsize 170 | if point0[0]<0: 171 | point0[0]=0 172 | point1[0]=size 173 | if point0[1]<0: 174 | point0[1]=0 175 | point1[1]=size 176 | if point1[0]>w: 177 | point1[0]=w 178 | point0[0]=w-size 179 | if point1[1]>h: 180 | point1[1]=h 181 | point0[1]=h-size 182 | center = ((point0+point1)/2).astype('int') 183 | return center[0],center[1],halfsize,area 184 | 185 | def mask_threshold(mask,blur,threshold): 186 | mask = cv2.threshold(mask,threshold,255,cv2.THRESH_BINARY)[1] 187 | mask = cv2.blur(mask, (blur, blur)) 188 | mask = cv2.threshold(mask,threshold/5,255,cv2.THRESH_BINARY)[1] 189 | return mask 190 | 191 | def mask_area(mask): 192 | mask = cv2.threshold(mask,127,255,0)[1] 193 | # contours= cv2.findContours(mask,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)[1] #for opencv 3.4 194 | contours= cv2.findContours(mask,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)[0]#updata to opencv 4.0 195 | try: 196 | area = cv2.contourArea(contours[0]) 197 | except: 198 | area = 0 199 | return area 200 | 201 | 202 | def replace_mosaic(img_origin,img_fake,x,y,size,no_father): 203 | img_fake = resize(img_fake,size*2) 204 | if no_father: 205 | img_origin[y-size:y+size,x-size:x+size]=img_fake 206 | img_result = img_origin 207 | else: 208 | #color correction 209 | RGB_origin = img_origin[y-size:y+size,x-size:x+size].mean(0).mean(0) 210 | RGB_fake = img_fake.mean(0).mean(0) 211 | for i in range(3):img_fake[:,:,i] = np.clip(img_fake[:,:,i]+RGB_origin[i]-RGB_fake[i],0,255) 212 | #eclosion 213 | eclosion_num = int(size/5) 214 | entad = int(eclosion_num/2+2) 215 | mask = np.zeros(img_origin.shape, dtype='uint8') 216 | mask = cv2.rectangle(mask,(x-size+entad,y-size+entad),(x+size-entad,y+size-entad),(255,255,255),-1) 217 | mask = (cv2.blur(mask, (eclosion_num, eclosion_num))) 218 | mask = mask/255.0 219 | 220 | img_tmp = np.zeros(img_origin.shape) 221 | img_tmp[y-size:y+size,x-size:x+size]=img_fake 222 | img_result = img_origin.copy() 223 | img_result = (img_origin*(1-mask)+img_tmp*mask).astype('uint8') 224 | return img_result -------------------------------------------------------------------------------- /Util/sound.py: -------------------------------------------------------------------------------- 1 | import time 2 | import numpy as np 3 | import scipy.fftpack 4 | from .array_operation import * 5 | from .dsp import * 6 | from .util import * 7 | import librosa 8 | from scipy.io import wavfile 9 | import os 10 | import matplotlib.pylab as plt 11 | 12 | piano = np.array([0,27.5,29.1,30.9,32.7,34.6,36.7,38.9,41.2, 13 | 43.7,46.2,49.0,51.9,55.0,58.3,61.7,65.4,69.3, 14 | 73.4,77.8,82.4,87.3,92.5,98.0,103.8,110.0,116.5, 15 | 123.5,130.8,138.6,146.8,155.6,164.8,174.6,185.0, 16 | 196.0,207.7,220.0,233.1,246.9,261.6,277.2,293.7, 17 | 311.1,329.6,349.2,370.0,392.0,415.3,440.0,466.2, 18 | 493.9,523.3,554.4,587.3,622.3,659.3,698.5,740.0, 19 | 784.0,830.6,880.0,932.3,987.8,1047,1109,1175,1245, 20 | 1319,1397,1480,1568,1661,1760,1865,1976,2093,2217, 21 | 2349,2489,2637,2794,2960,3136,3322,3520,3729,3951,4186,4400]) 22 | 23 | piano_10 = np.array([0,73.4,207.7,349.2,587.3,987.8,1245,1661,2093,2794,4400]) 24 | 25 | #------------------------------IO------------------------------ 26 | def numpy2voice(npdata): 27 | voice = np.zeros((len(npdata),2)) 28 | voice[:,0] = npdata 29 | voice[:,1] = npdata 30 | return voice 31 | 32 | def load(path,ch = 0): 33 | freq,audio = wavfile.read(path) 34 | if ch == 0: 35 | audio = audio[:,0] 36 | elif ch == 1: 37 | audio = audio[:,1] 38 | return freq,audio.astype(np.float64) 39 | 40 | def write(npdata,path='./tmp/test_output.wav',freq = 44100): 41 | voice = numpy2voice(npdata) 42 | wavfile.write(path, freq, voice.astype(np.int16)) 43 | 44 | def play(path): 45 | os.system("paplay "+path) 46 | 47 | def playtest(npdata,freq = 44100): 48 | time.sleep(0.5) 49 | makedirs('./tmp/') 50 | voice = numpy2voice(npdata) 51 | wavfile.write('./tmp/test_output.wav', freq, voice.astype(np.int16)) 52 | play('./tmp/test_output.wav') 53 | 54 | 55 | #------------------------------DSP------------------------------ 56 | 57 | def filter(audio,fc=[],fs=44100,win_length = 4096): 58 | for i in range(len(audio)//win_length): 59 | audio[i*win_length:(i+1)*win_length] = fft_filter(audio[i*win_length:(i+1)*win_length], fs=fs, fc=fc) 60 | 61 | return audio 62 | 63 | # def freq_correct(src,dst,fs=44100,alpha = 0.05,fc=3000): 64 | # src_freq = basefreq(src, 44100,3000) 65 | # dst_freq = basefreq(dst, 44100,3000) 66 | # offset = int((src_freq-dst_freq)/(src_freq*0.05)) 67 | # out = librosa.effects.pitch_shift(dst.astype(np.float64), 44100, n_steps=offset) 68 | # #print('freqloss:',round((basefreq(out, 44100,3000)-basefreq(src, 44100,3000))/basefreq(src, 44100,3000),3)) 69 | # return out.astype(np.int16) 70 | 71 | def freq_correct(src,dst,fs=44100,fc=3000,mode = 'normal',win_length = 1024, alpha = 1): 72 | 73 | out = np.zeros_like(src) 74 | try: 75 | if mode == 'normal': 76 | src_oct = librosa.hz_to_octs(basefreq(src, fs, fc)) 77 | dst_oct = librosa.hz_to_octs(basefreq(dst, fs, fc)) 78 | offset = (dst_oct-src_oct)*12*alpha 79 | out = librosa.effects.pitch_shift(src, 44100, n_steps=offset) 80 | elif mode == 'track': 81 | length = min([len(src),len(dst)]) 82 | for i in range(length//win_length): 83 | src_oct = librosa.hz_to_octs(basefreq(src[i*win_length:(i+1)*win_length], fs, fc)) 84 | dst_oct = librosa.hz_to_octs(basefreq(dst[i*win_length:(i+1)*win_length], fs, fc)) 85 | 86 | offset = (dst_oct-src_oct)*12*alpha 87 | out[i*win_length:(i+1)*win_length] = librosa.effects.pitch_shift(src[i*win_length:(i+1)*win_length], 44100, n_steps=offset) 88 | return out 89 | except Exception as e: 90 | return src 91 | 92 | #print('freqloss:',round((basefreq(out, 44100,3000)-basefreq(src, 44100,3000))/basefreq(src, 44100,3000),3)) 93 | 94 | 95 | def energy_correct(src,dst,mode = 'normal',win_length = 512,alpha=1): 96 | """ 97 | mode: normal | track 98 | """ 99 | out = np.zeros_like(src) 100 | if mode == 'normal': 101 | src_rms = rms(src) 102 | dst_rms = rms(dst) 103 | out = src*(dst_rms/src_rms)*alpha 104 | elif mode == 'track': 105 | length = min([len(src),len(dst)]) 106 | tracks = [] 107 | for i in range(length//win_length): 108 | src_rms = np.clip(rms(src[i*win_length:(i+1)*win_length]),1e-6,np.inf) 109 | dst_rms = rms(dst[i*win_length:(i+1)*win_length]) 110 | tracks.append((dst_rms/src_rms)*alpha) 111 | tracks = np.clip(np.array(tracks),0.1,10) 112 | tracks = interp(tracks, length) 113 | out = src*tracks 114 | 115 | return np.clip(out,-32760,32760) 116 | 117 | def time_correct(src,dst,_min=0.25): 118 | src_time = len(src) 119 | dst_time = len(dst) 120 | rate = np.clip(src_time/dst_time,_min,100) 121 | out = librosa.effects.time_stretch(src,rate) 122 | return out 123 | 124 | def freqfeatures(signal,fs): 125 | signal = normliaze(signal,mode = '5_95',truncated=100) 126 | signal_fft = np.abs(scipy.fftpack.fft(signal)) 127 | length = len(signal) 128 | features = [] 129 | for i in range(len(piano_10)-1): 130 | k1 = int(length/fs*piano_10[i]) 131 | k2 = int(length/fs*piano_10[i+1]) 132 | features.append(np.mean(signal_fft[k1:k2])) 133 | return np.array(features) 134 | 135 | 136 | 137 | def main(): 138 | xp = [1, 2, 3] 139 | fp = [3, 2, 0] 140 | x = [0, 1, 1.5, 2.72, 3.14] 141 | print(np.interp(x, xp, fp)) 142 | 143 | if __name__ == '__main__': 144 | main() -------------------------------------------------------------------------------- /Util/util.py: -------------------------------------------------------------------------------- 1 | import os 2 | import shutil 3 | def Traversal(filedir): 4 | file_list=[] 5 | for root,dirs,files in os.walk(filedir): 6 | for file in files: 7 | file_list.append(os.path.join(root,file)) 8 | for dir in dirs: 9 | Traversal(dir) 10 | return file_list 11 | 12 | def is_img(path): 13 | ext = os.path.splitext(path)[1] 14 | ext = ext.lower() 15 | if ext in ['.jpg','.png','.jpeg','.bmp']: 16 | return True 17 | else: 18 | return False 19 | 20 | def is_video(path): 21 | ext = os.path.splitext(path)[1] 22 | ext = ext.lower() 23 | if ext in ['.mp4','.flv','.avi','.mov','.mkv','.wmv','.rmvb','.mts']: 24 | return True 25 | else: 26 | return False 27 | 28 | def is_imgs(paths): 29 | tmp = [] 30 | for path in paths: 31 | if is_img(path): 32 | tmp.append(path) 33 | return tmp 34 | 35 | def is_videos(paths): 36 | tmp = [] 37 | for path in paths: 38 | if is_video(path): 39 | tmp.append(path) 40 | return tmp 41 | 42 | def writelog(path,log): 43 | f = open(path,'a+') 44 | f.write(log+'\n') 45 | f.close() 46 | 47 | 48 | def makedirs(path): 49 | if os.path.isdir(path): 50 | print(path,'existed') 51 | else: 52 | os.makedirs(path) 53 | print('makedir:',path) 54 | 55 | def clean_tempfiles(tmp_init=True): 56 | if os.path.isdir('./tmp'): 57 | shutil.rmtree('./tmp') 58 | if tmp_init: 59 | os.makedirs('./tmp') 60 | os.makedirs('./tmp/video_voice') 61 | os.makedirs('./tmp/music/') 62 | os.makedirs('./tmp/video_imgs') 63 | os.makedirs('./tmp/output_imgs') 64 | 65 | def file_init(opt): 66 | if not os.path.isdir(opt.result_dir): 67 | os.makedirs(opt.result_dir) 68 | print('makedir:',opt.result_dir) 69 | clean_tempfiles() 70 | 71 | def get_bar(percent,num = 25): 72 | bar = '[' 73 | for i in range(num): 74 | if i < round(percent/(100/num)): 75 | bar += '#' 76 | else: 77 | bar += '-' 78 | bar += ']' 79 | return bar+' '+str(round(percent,2))+'%' 80 | 81 | def copyfile(src,dst): 82 | try: 83 | shutil.copyfile(src, dst) 84 | except Exception as e: 85 | print(e) 86 | 87 | def second2stamp(s): 88 | floats = s 89 | h = int(s/3600) 90 | s = int(s%3600) 91 | m = int(s/60) 92 | s = int(s%60) 93 | floats = floats - int(floats) + s 94 | 95 | return "%02d:%02d:%.3f" % (h, m, floats) 96 | -------------------------------------------------------------------------------- /imgs/DeepMosaics.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/HypoX64/bilibili/992029667ad37d7d03131aa2c4c9923da6cca6f2/imgs/DeepMosaics.jpg -------------------------------------------------------------------------------- /imgs/GuiChu_余生一个浪.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/HypoX64/bilibili/992029667ad37d7d03131aa2c4c9923da6cca6f2/imgs/GuiChu_余生一个浪.jpg -------------------------------------------------------------------------------- /imgs/OneImage2Video_badapple.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/HypoX64/bilibili/992029667ad37d7d03131aa2c4c9923da6cca6f2/imgs/OneImage2Video_badapple.jpg -------------------------------------------------------------------------------- /imgs/PlayMusic_小星星.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/HypoX64/bilibili/992029667ad37d7d03131aa2c4c9923da6cca6f2/imgs/PlayMusic_小星星.jpg -------------------------------------------------------------------------------- /imgs/Plogo.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/HypoX64/bilibili/992029667ad37d7d03131aa2c4c9923da6cca6f2/imgs/Plogo.jpg -------------------------------------------------------------------------------- /imgs/ShellPlayer_大威天龙.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/HypoX64/bilibili/992029667ad37d7d03131aa2c4c9923da6cca6f2/imgs/ShellPlayer_大威天龙.jpg -------------------------------------------------------------------------------- /imgs/ShellPlayer_教程.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/HypoX64/bilibili/992029667ad37d7d03131aa2c4c9923da6cca6f2/imgs/ShellPlayer_教程.jpg --------------------------------------------------------------------------------