├── .gitignore ├── 3dmapped.py ├── LICENSE ├── MATPLOTLIB PYTHON IMAGE MANIPULATION.pptx ├── ModernCode_1_Canopy_Python2.jpg ├── ModernCode_2_Canopy_Python2.png ├── ModernCode_3_Anaconda_Python36.png ├── PrimaryCode_Canopy_Python2.png ├── README.md ├── jcskull.p └── mapto3d.py /.gitignore: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/andrewrgarcia/3Dmapping-algorithm/3ec13b8ebd5fcbd0c74bf193255520848776c62c/.gitignore -------------------------------------------------------------------------------- /3dmapped.py: -------------------------------------------------------------------------------- 1 | #patplot.py 2 | #an algorithm for importing and modifying images (primitive version) 3 | #offspring: mapto2dscat, mapto3d, mapto3d_2 4 | import numpy as np 5 | import matplotlib.pyplot as plt 6 | import pickle 7 | from mpl_toolkits.mplot3d import Axes3D 8 | 9 | 'converting image to code; in command line:' 10 | import matplotlib.image as mpimg 11 | img1 = mpimg.imread("images/catglasses.png") 12 | pickle.dump( img1, open( "cat.p", "wb" ) ) 13 | 14 | 'loading encrypted image' 15 | # img1 = pickle.load( open( "jcskull.p", "rb" ) ) 16 | 17 | lum_img1 = img1[:,:,0] 18 | x=[] 19 | y=[] 20 | 21 | 'pixel dimensions (px x py)' 22 | 23 | #px=175 24 | #py=167 25 | 26 | px=688 27 | py=688 28 | 29 | for i in range(py): 30 | for j in range(px): 31 | if lum_img1[i,j]<0.90: 32 | x.append(j) 33 | y.append(i) 34 | z=1*np.ones(np.size(x)) 35 | 36 | x2=[] 37 | y2=[] 38 | 39 | for i in range(py): 40 | for j in range(px): 41 | if lum_img1[i,j]<0.80: 42 | x2.append(j) 43 | y2.append(i) 44 | z2=2*np.ones(np.size(x2)) 45 | 46 | x3=[] 47 | y3=[] 48 | 49 | for i in range(py): 50 | for j in range(px): 51 | if lum_img1[i,j]<0.70: 52 | x3.append(j) 53 | y3.append(i) 54 | z3=3*np.ones(np.size(x3)) 55 | 56 | x4=[] 57 | y4=[] 58 | 59 | for i in range(py): 60 | for j in range(px): 61 | if lum_img1[i,j]<0.60: 62 | x4.append(j) 63 | y4.append(i) 64 | z4=4*np.ones(np.size(x4)) 65 | 66 | x5=[] 67 | y5=[] 68 | 69 | for i in range(py): 70 | for j in range(px): 71 | if lum_img1[i,j]<0.50: 72 | x5.append(j) 73 | y5.append(i) 74 | z5=5*np.ones(np.size(x5)) 75 | 76 | x6=[] 77 | y6=[] 78 | 79 | for i in range(py): 80 | for j in range(px): 81 | if lum_img1[i,j]<0.40: 82 | x6.append(j) 83 | y6.append(i) 84 | z6=6*np.ones(np.size(x6)) 85 | 86 | x7=[] 87 | y7=[] 88 | 89 | for i in range(py): 90 | for j in range(px): 91 | if lum_img1[i,j]<0.30: 92 | x7.append(j) 93 | y7.append(i) 94 | z7=7*np.ones(np.size(x7)) 95 | 96 | x8=[] 97 | y8=[] 98 | 99 | for i in range(py): 100 | for j in range(px): 101 | if lum_img1[i,j]<0.20: 102 | x8.append(j) 103 | y8.append(i) 104 | z8=8*np.ones(np.size(x8)) 105 | 106 | x9=[] 107 | y9=[] 108 | 109 | for i in range(py): 110 | for j in range(px): 111 | if lum_img1[i,j]<0.10: 112 | x9.append(j) 113 | y9.append(i) 114 | z9=9*np.ones(np.size(x9)) 115 | 116 | 117 | fig = plt.figure() 118 | ax = plt.axes(projection='3d') 119 | ax.plot(z,x,y,'o',color='magenta') 120 | ax.plot(z2, x2, y2,'o',color='yellow') 121 | ax.plot(z3, x3, y3,'o',color='black') 122 | ax.plot(z4, x4, y4,'o',color='cyan') 123 | ax.plot(z5,x5, y5,'o',color='pink') 124 | ax.plot(z6,x6, y6,'o',color='magenta') 125 | ax.plot(z7,x7, y7,'o',color='blue') 126 | ax.plot(z8,x8, y8,'o',color='navy') 127 | ax.plot(z9,x9, y9,'o',color='black') 128 | plt.xlabel('') 129 | plt.ylabel('') 130 | plt.suptitle('The Skull \nAndrew R Garcia, 2016') 131 | plt.show() -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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For a more functional application of this project, please re-direct to [**voxelmap**](https://github.com/andrewrgarcia/voxelmap) and use the **voxelmap.Image('yourfilepath.png')** object and its ->make() and ->map3d() functions to map images to 3-D models. 4 | 5 |
6 | 7 | # 3Dmapping-algorithm 8 | This code shows an algorithm for importing and mapping 2D images to 3D plots. Mapping algorithm: maps 2D binary image to 3D form by transforming relative pixel color to depth. 9 | 10 | *Primary Code* - generated from the attached code [from *mapto3d.py*] 11 | 12 | ![Screenshot](PrimaryCode_Canopy_Python2.png) 13 | 14 | *Modern Code* - generated for a more recent code that uses the same algorithm but cuts the main algorithm code to 5 lines 15 | and allows for the automation of generating N slices on the X-dimension without any additional code development. 16 | 17 | ![Screenshot](ModernCode_3_Anaconda_Python36.png) 18 | 19 | 3D rendering generated from Joe Clark's *Human Skull Photographic Print* 2D Image 20 | 21 | Watch video to see the creative process. 22 | https://www.youtube.com/watch?v=5E5mVVsrwZw 23 | -------------------------------------------------------------------------------- /mapto3d.py: -------------------------------------------------------------------------------- 1 | #mapto3d.py 2 | #an algorithm for importing and mapping 2D images to 3D plots 3 | #Andrew Garcia, 2016 4 | import numpy as np 5 | import matplotlib.pyplot as plt 6 | import pickle 7 | from mpl_toolkits.mplot3d import Axes3D 8 | import matplotlib.image as mpimg 9 | 10 | 'converting image to code; in command line:' 11 | #img1 = mpimg.imread("O36062_640.png") 12 | #pickle.dump( img1, open( "jcskull.p", "wb" ) ) 13 | 14 | 'loading encrypted image' 15 | img1 = pickle.load( open( "jcskull.p", "rb" ),encoding='latin1') 16 | 'converting image to binary' 17 | lum_img1 = img1[:,:,0] 18 | 19 | 'pixel dimensions (px x py)' 20 | 21 | px=244 22 | py=244 23 | 24 | 25 | '''mapping algorithm: maps 2D binary image to 3D form by transforming relative 26 | pixel color to depth''' 27 | 28 | x=[] 29 | y=[] 30 | 31 | for i in range(py): 32 | for j in range(px): 33 | if lum_img1[i,j]<0.90: 34 | x.append(j) 35 | y.append(i) 36 | z=1*np.ones(np.size(x)) 37 | 38 | x2=[] 39 | y2=[] 40 | 41 | for i in range(py): 42 | for j in range(px): 43 | if lum_img1[i,j]<0.80: 44 | x2.append(j) 45 | y2.append(i) 46 | z2=2*np.ones(np.size(x2)) 47 | 48 | x3=[] 49 | y3=[] 50 | 51 | for i in range(py): 52 | for j in range(px): 53 | if lum_img1[i,j]<0.70: 54 | x3.append(j) 55 | y3.append(i) 56 | z3=3*np.ones(np.size(x3)) 57 | 58 | x4=[] 59 | y4=[] 60 | 61 | for i in range(py): 62 | for j in range(px): 63 | if lum_img1[i,j]<0.60: 64 | x4.append(j) 65 | y4.append(i) 66 | z4=4*np.ones(np.size(x4)) 67 | 68 | x5=[] 69 | y5=[] 70 | 71 | for i in range(py): 72 | for j in range(px): 73 | if lum_img1[i,j]<0.50: 74 | x5.append(j) 75 | y5.append(i) 76 | z5=5*np.ones(np.size(x5)) 77 | 78 | x6=[] 79 | y6=[] 80 | 81 | for i in range(py): 82 | for j in range(px): 83 | if lum_img1[i,j]<0.40: 84 | x6.append(j) 85 | y6.append(i) 86 | z6=6*np.ones(np.size(x6)) 87 | 88 | x7=[] 89 | y7=[] 90 | 91 | for i in range(py): 92 | for j in range(px): 93 | if lum_img1[i,j]<0.30: 94 | x7.append(j) 95 | y7.append(i) 96 | z7=7*np.ones(np.size(x7)) 97 | 98 | x8=[] 99 | y8=[] 100 | 101 | for i in range(py): 102 | for j in range(px): 103 | if lum_img1[i,j]<0.20: 104 | x8.append(j) 105 | y8.append(i) 106 | z8=8*np.ones(np.size(x8)) 107 | 108 | x9=[] 109 | y9=[] 110 | 111 | for i in range(py): 112 | for j in range(px): 113 | if lum_img1[i,j]<0.10: 114 | x9.append(j) 115 | y9.append(i) 116 | z9=9*np.ones(np.size(x9)) 117 | 118 | 119 | fig = plt.figure() 120 | ax = plt.axes(projection='3d') 121 | ax.plot(z,x,y,'o',color='magenta') 122 | ax.plot(z2, x2, y2,'o',color='yellow') 123 | ax.plot(z3, x3, y3,'o',color='black') 124 | ax.plot(z4, x4, y4,'o',color='cyan') 125 | ax.plot(z5,x5, y5,'o',color='pink') 126 | ax.plot(z6,x6, y6,'o',color='magenta') 127 | ax.plot(z7,x7, y7,'o',color='blue') 128 | ax.plot(z8,x8, y8,'o',color='navy') 129 | ax.plot(z9,x9, y9,'o',color='black') 130 | plt.xlabel('') 131 | plt.ylabel('') 132 | plt.suptitle('Andrew R Garcia, 2016') 133 | plt.show() 134 | --------------------------------------------------------------------------------