├── AI引论 ├── HW 01.pdf ├── HW 02.pdf ├── HW 03.pdf ├── HW 04.pdf ├── HW 05.pdf ├── HW 06.pdf ├── HW 07.pdf └── HW 08.pdf ├── README.md ├── 代数结构与组合数学 ├── ASCM 23_02_28.pdf ├── ASCM 23_03_07.pdf ├── ASCM 23_03_14.pdf ├── ASCM 23_03_21.pdf ├── ASCM 23_03_28.pdf ├── ASCM 23_04_04.pdf ├── ASCM 23_04_11.pdf ├── ASCM 23_04_18.pdf ├── ASCM 23_04_25.pdf ├── ASCM 23_05_09.pdf ├── ASCM 23_05_16.pdf ├── ASCM 23_05_23.pdf ├── ASCM 23_05_30.pdf └── ASCM 23_06_06.pdf ├── 信息论 ├── HW1.pdf ├── HW2.pdf └── HW3.pdf ├── 数据库概论 ├── HW1.pdf ├── HW2.pdf ├── HW3.pdf ├── HW4.pdf ├── HW5.pdf ├── HW6.pdf └── HW7.pdf ├── 数据结构与算法（A） ├── Ch1.pdf ├── Ch10.pdf ├── Ch2.pdf ├── Ch3.pdf ├── Ch4.pdf ├── Ch5.pdf ├── Ch6.pdf ├── Ch7.pdf ├── Ch8.pdf └── Ch9.pdf ├── 数理逻辑 ├── HW 6 22.10.21.pdf ├── HW1 22.9.16.pdf ├── HW10.pdf ├── HW11.pdf ├── HW12.pdf ├── HW2 22.9.23.pdf ├── HW3 22.9.30.pdf ├── HW4 22.10.7.pdf ├── HW5 22.10.14.pdf ├── HW7 22.10.28.pdf ├── HW8 22.11.11.pdf └── HW9 22.11.18.pdf ├── 概率统计（A） └── PTS HW.pdf ├── 算法设计与分析 ├── HW01.pdf ├── HW02.pdf ├── HW03.pdf ├── HW04.pdf ├── HW05.pdf ├── HW06.pdf ├── HW07.pdf ├── HW08.pdf ├── HW09.pdf └── HW10.pdf ├── 编译原理 ├── HW01.pdf ├── HW02.pdf ├── HW03.pdf ├── HW04.pdf ├── HW05.pdf ├── HW06.pdf ├── HW07.pdf ├── HW08.pdf ├── HW09.pdf ├── HW10.pdf ├── HW11.pdf ├── HW12.pdf ├── HW13.pdf ├── HW14.pdf ├── HW15.pdf └── HW16.pdf ├── 计算机网络（实验班） ├── Ch2.pdf ├── Ch3.pdf ├── Ch4.pdf ├── Ch5.pdf └── Ch6.pdf ├── 计算机视觉导论 ├── HW1 │ ├── HM1_Canny.py │ ├── HM1_Convolve.py │ ├── HM1_HarrisCorner.py │ ├── HM1_RANSAC.py │ ├── HM1_ransac_points.txt │ ├── Lenna.png │ ├── hand_writting.png │ ├── pack.py │ ├── result │ │ ├── HM1_Canny_result.png │ │ ├── HM1_Convolve_img_blur.png │ │ ├── HM1_Convolve_img_gadient_x.png │ │ ├── HM1_Convolve_img_gadient_y.png │ │ ├── HM1_Convolve_replicate_pad.txt │ │ ├── HM1_Convolve_result_1.txt │ │ ├── HM1_Convolve_result_2.txt │ │ ├── HM1_Convolve_zero_pad.txt │ │ ├── HM1_HarrisCorner.png │ │ ├── HM1_RANSAC_fig.png │ │ ├── HM1_RANSAC_plane.txt │ │ └── HM1_RANSAC_sample_time.txt │ └── utils.py ├── HW2 │ ├── BP.py │ ├── README.md │ ├── batch_normalization │ │ └── bn.py │ ├── cifar-10 │ │ ├── Lenna.png │ │ ├── dataset.py │ │ ├── network.py │ │ ├── train.py │ │ └── util.py │ ├── mnist_subset │ │ ├── 0.png │ │ ├── 1.png │ │ ├── 2.png │ │ ├── 3.png │ │ ├── 4.png │ │ ├── 5.png │ │ ├── 6.png │ │ ├── 7.png │ │ ├── 8.png │ │ └── 9.png │ ├── pack.py │ └── results │ │ ├── BP.txt │ │ ├── Lenna.png │ │ ├── Lenna_aug1.png │ │ ├── Lenna_aug2.png │ │ ├── bn_loss.txt │ │ ├── learning_rate.png │ │ ├── my_best.png │ │ └── network_structure.png └── HW3 │ ├── README.md │ ├── camera_calibr │ ├── back_image.npy │ ├── calibr.ipynb │ ├── front.png │ └── front_image.npy │ ├── depth_pc │ ├── aligned_full_pc.txt │ ├── depth.png │ ├── depth_pc.ipynb │ ├── intrinsic.npy │ ├── raw_full_pc.txt │ ├── render.py │ ├── seg.png │ └── spot.obj │ ├── marching_cube │ ├── data │ │ ├── bob_cell.npy │ │ └── spot_cell.npy │ ├── lookup_table.py │ └── marching_cube.ipynb │ ├── mesh_pc │ ├── earthmover.py │ ├── fps_vis.txt │ ├── mesh_pc.ipynb │ ├── spot.obj │ └── uniform_sampling_vis.txt │ ├── pack.py │ └── results │ ├── bob.obj │ ├── calibr.npy │ ├── fps_results.npy │ ├── metrics.npy │ ├── one_way_CD.txt │ ├── pc_from_depth.txt │ ├── spot.obj │ └── uniform_sampling_results.npy ├── 计算理论导论 ├── HW1.pdf ├── HW2.pdf ├── HW3.pdf ├── HW4.pdf ├── HW5.pdf └── HW6.pdf ├── 随机过程引论 ├── HW1.pdf ├── HW2.pdf ├── HW3.pdf └── HW4.pdf └── 集合论与图论 ├── SG HW1.pdf ├── SG HW2.pdf ├── SG HW3.pdf ├── SG HW4.pdf └── SG HW5.pdf /AI引论/HW 01.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CS-icez/pku-cs-homework/1040ca9fae63f9aea17dbf807ba1de8a1f668b92/AI引论/HW 01.pdf -------------------------------------------------------------------------------- /AI引论/HW 02.pdf: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | # PKU CS 作业合集 2 | 3 | ## 关于本仓库 4 | 5 | CS 专业课的教学质量想必大家有目共睹，无需多言。本仓库存放了我所上部分课程的作业解答，希望能给大家一些帮助。 6 | 7 | 由于能力有限，我写的答案可能有不少疏漏，或者做法不够简洁，请大家谨慎参考。如发现错误，欢迎开 issue 指正。 8 | 9 | 为了防止可能的版权纠纷，不提供作业题面。对于那些有“祖传作业”的课程，这应该不成问题。 10 | 11 | 我现在大三，这个仓库会在每学期结束后更新，直到我毕业。 12 | 13 | 如果我的作业帮到了你，不妨给我点个 star，让我知道有人在看~ 14 | 15 | ## 友情链接 16 | 17 | [计算机系统导论的 lab](https://github.com/CS-icez/introduction-to-computer-systems) 18 | 19 | [程序设计实习的 OJ 作业和补充练习](https://github.com/CS-icez/practice-of-programming-in-c-and-cpp) 20 | 21 | [数据结构与算法（A）的 OJ 作业](https://github.com/CS-icez/data-structure-and-algorithm) 22 | 23 | [数学分析（Ⅲ）的全书习题](https://www.zhihu.com/column/c_1593381605103681536) 24 | 25 | [编译原理的 lab](https://github.com/CS-icez/SysY-Compiler) 26 | 27 | 操作系统（实验班）的 lab（待开源） 28 | 29 | [毕业论文的 LaTeX 模板](https://github.com/CS-icez/pkuthss-undergraduate-eecs-2025) 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Sobel_filter_y 3 | from utils import read_img, write_img 4 | 5 | def compute_gradient_magnitude_direction(x_grad, y_grad): 6 | """ 7 | The function you need to implement for Q2 a). 8 | Inputs: 9 | x_grad: array(float) 10 | y_grad: array(float) 11 | Outputs: 12 | magnitude_grad: array(float) 13 | direction_grad: array(float) you may keep the angle of the gradient at each pixel 14 | """ 15 | magnitude_grad = np.sqrt(x_grad**2 + y_grad**2) 16 | direction_grad = np.arctan2(y_grad, x_grad) 17 | 18 | return magnitude_grad, direction_grad 19 | 20 | 21 | 22 | def non_maximal_suppressor(grad_mag, grad_dir): 23 | """ 24 | The function you need to implement for Q2 b). 25 | Inputs: 26 | grad_mag: array(float) 27 | grad_dir: array(float) 28 | Outputs: 29 | output: array(float) 30 | """ 31 | 32 | grad_dir = (np.rad2deg(grad_dir) % 180 // 22.5 + 1) // 2 % 4 33 | 34 | # Get all neighbors in a tensor style. 35 | pad = np.pad(grad_mag, 1, mode='constant') 36 | t = pad[:-2, 1:-1] 37 | b = pad[2:, 1:-1] 38 | l = pad[1:-1, :-2] 39 | r = pad[1:-1, 2:] 40 | tl = pad[:-2, :-2] 41 | tr = pad[:-2, 2:] 42 | bl = pad[2:, :-2] 43 | br = pad[2:, 2:] 44 | 45 | NMS_output = np.select( 46 | # Note that x-axis points rightwards and y-axis points downwards. 47 | # This can be inferred from the Sobel filter. 48 | condlist=[ 49 | (grad_dir == 0) & (grad_mag > l) & (grad_mag > r), 50 | (grad_dir == 1) & (grad_mag > tl) & (grad_mag > br), 51 | (grad_dir == 2) & (grad_mag > t) & (grad_mag > b), 52 | (grad_dir == 3) & (grad_mag > tr) & (grad_mag > bl) 53 | ], 54 | choicelist=[grad_mag, grad_mag, grad_mag, grad_mag] 55 | ) 56 | 57 | return NMS_output 58 | 59 | 60 | 61 | def hysteresis_thresholding(img) : 62 | """ 63 | The function you need to implement for Q2 c). 64 | Inputs: 65 | img: array(float) 66 | Outputs: 67 | output: array(float) 68 | """ 69 | 70 | 71 | #you can adjust the parameters to fit your own implementation 72 | low_ratio = 0.06 73 | high_ratio = 0.18 74 | 75 | low = low_ratio * np.max(img) 76 | high = high_ratio * np.max(img) 77 | 78 | output = np.where(img > high, 1, 0) 79 | output = np.pad(output, 1, mode='constant') 80 | 81 | curr_num = np.count_nonzero(output) 82 | prev_num = 0 83 | 84 | while curr_num > prev_num: 85 | t = output[:-2, 1:-1] 86 | b = output[2:, 1:-1] 87 | l = output[1:-1, :-2] 88 | r = output[1:-1, 2:] 89 | tl = output[:-2, :-2] 90 | tr = output[:-2, 2:] 91 | bl = output[2:, :-2] 92 | br = output[2:, 2:] 93 | 94 | cond = (img > low) & (t | b | l | r | tl | tr | bl | br) 95 | output[1:-1, 1:-1] = np.where(cond, 1, output[1:-1, 1:-1]) 96 | 97 | prev_num = curr_num 98 | curr_num = np.count_nonzero(output) 99 | 100 | output = output[1:-1, 1:-1] 101 | 102 | return output 103 | 104 | 105 | 106 | if __name__=="__main__": 107 | 108 | #Load the input images 109 | input_img = read_img("Lenna.png")/255 110 | 111 | #Apply gaussian blurring 112 | blur_img = Gaussian_filter(input_img) 113 | 114 | x_grad = Sobel_filter_x(blur_img) 115 | y_grad = Sobel_filter_y(blur_img) 116 | 117 | #Compute the magnitude and the direction of gradient 118 | magnitude_grad, direction_grad = compute_gradient_magnitude_direction(x_grad, y_grad) 119 | 120 | #NMS 121 | NMS_output = non_maximal_suppressor(magnitude_grad, direction_grad) 122 | 123 | #Edge linking with hysteresis 124 | output_img = hysteresis_thresholding(NMS_output) 125 | 126 | write_img("result/HM1_Canny_result.png", output_img*255) 127 | -------------------------------------------------------------------------------- /计算机视觉导论/HW1/HM1_Convolve.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from utils import read_img, write_img 3 | 4 | def padding(img, padding_size, type): 5 | """ 6 | The function you need to implement for Q1 a). 7 | Inputs: 8 | img: array(float) 9 | padding_size: int 10 | type: str, zeroPadding/replicatePadding 11 | Outputs: 12 | padding_img: array(float) 13 | """ 14 | 15 | h, w = img.shape 16 | p = padding_size 17 | 18 | if type=="zeroPadding": 19 | padding_img = np.zeros((h+2*p, w+2*p)) 20 | padding_img[p:p+h, p:p+w] = img # Center. 21 | return padding_img 22 | elif type=="replicatePadding": 23 | padding_img = np.zeros((h+2*p, w+2*p)) 24 | padding_img[p:p+h, p:p+w] = img # Center. 25 | padding_img[:p, p:p+w] = img[:1, :] # Top. 26 | padding_img[p+h:, p:p+w] = img[-1:, :] # Bottom. 27 | padding_img[p:p+h, :p] = img[:, :1] # Left. 28 | padding_img[p:p+h, p+w:] = img[:, -1:] # Right. 29 | padding_img[:p, :p] = img[0, 0] # Top left. 30 | padding_img[:p, p+w:] = img[0, -1] # Top right. 31 | padding_img[p+h:, :p] = img[-1, 0] # Bottom left. 32 | padding_img[p+h:, p+w:] = img[-1, -1] # Bottom right. 33 | return padding_img 34 | 35 | 36 | def convol_with_Toeplitz_matrix(img, kernel): 37 | """ 38 | The function you need to implement for Q1 b). 39 | Inputs: 40 | img: array(float) 6*6 41 | kernel: array(float) 3*3 42 | Outputs: 43 | output: array(float) 44 | """ 45 | 46 | #zero padding 47 | K = kernel.shape[0] 48 | H, W = img.shape 49 | img_pad = padding(img, (K - 1) // 2, "zeroPadding") 50 | HP, WP = img_pad.shape 51 | 52 | #build the Toeplitz matrix and compute convolution 53 | k_pad = np.zeros((K, WP)) # (K, WP) 54 | k_pad[:K, :K] = kernel 55 | 56 | # Construct circulant matrix. (n,) -> (n_rows, n). 57 | to_toeplitz = lambda v, n_row: v[np.arange(len(v)) - np.arange(n_row)[:, None]] 58 | t = np.apply_along_axis(lambda v: to_toeplitz(v, W), 1, k_pad) # (K, W, WP) 59 | t = np.concatenate((t, np.zeros((HP - K, W, WP))), axis=0) # (HP, W, WP) 60 | t = to_toeplitz(t, H) # (H, HP, W, WP) 61 | t = t.transpose(0, 2, 1, 3) # (H, W, HP, WP) 62 | t = t.reshape(img.size, img_pad.size) # (H*W, HP*WP) 63 | 64 | output = t @ img_pad.flatten() # (H*W, HP*WP) @ (HP*WP, 1) = (H*W, 1) 65 | output = output.reshape(img.shape) # (H, W) 66 | 67 | return output 68 | 69 | 70 | def convolve(img, kernel): 71 | """ 72 | The function you need to implement for Q1 c). 73 | Inputs: 74 | img: array(float) 75 | kernel: array(float) 76 | Outputs: 77 | output: array(float) 78 | """ 79 | 80 | #build the sliding-window convolution here 81 | H, W = img.shape 82 | K = kernel.shape[0] 83 | 84 | # Idea: convert each iteration dimension to a tensor dimension. 85 | x = np.arange(H-K+1).reshape(-1, 1, 1, 1) # (H-K+1, 1, 1, 1) 86 | y = np.arange(W-K+1).reshape(1, -1, 1, 1) # (1, W-K+1, 1, 1) 87 | dx = np.arange(K).reshape(1, 1, -1, 1) # (1, 1, K, 1) 88 | dy = np.arange(K).reshape(1, 1, 1, -1) # (1, 1, 1, K) 89 | kk = kernel.reshape(1, 1, K, K) # (1, 1, K, K) 90 | 91 | r = x + dx # (H-K+1, 1, K, 1) 92 | c = y + dy # (1, W-K+1, 1, K) 93 | 94 | windows = img[r, c] # (H-K+1, W-K+1, K, K) 95 | output = np.sum(windows * kk, axis=(2, 3)) # (H-K+1, W-K+1) 96 | 97 | return output 98 | 99 | 100 | def Gaussian_filter(img): 101 | padding_img = padding(img, 1, "replicatePadding") 102 | gaussian_kernel = np.array([[1/16,1/8,1/16],[1/8,1/4,1/8],[1/16,1/8,1/16]]) 103 | output = convolve(padding_img, gaussian_kernel) 104 | return output 105 | 106 | def Sobel_filter_x(img): 107 | padding_img = padding(img, 1, "replicatePadding") 108 | sobel_kernel_x = np.array([[-1,0,1],[-2,0,2],[-1,0,1]]) 109 | output = convolve(padding_img, sobel_kernel_x) 110 | return output 111 | 112 | def Sobel_filter_y(img): 113 | padding_img = padding(img, 1, "replicatePadding") 114 | sobel_kernel_y = np.array([[-1,-2,-1],[0,0,0],[1,2,1]]) 115 | output = convolve(padding_img, sobel_kernel_y) 116 | return output 117 | 118 | 119 | 120 | if __name__=="__main__": 121 | 122 | np.random.seed(111) 123 | input_array=np.random.rand(6,6) 124 | input_kernel=np.random.rand(3,3) 125 | 126 | 127 | # task1: padding 128 | zero_pad = padding(input_array,1,"zeroPadding") 129 | np.savetxt("result/HM1_Convolve_zero_pad.txt",zero_pad) 130 | 131 | replicate_pad = padding(input_array,1,"replicatePadding") 132 | np.savetxt("result/HM1_Convolve_replicate_pad.txt",replicate_pad) 133 | 134 | 135 | #task 2: convolution with Toeplitz matrix 136 | result_1 = convol_with_Toeplitz_matrix(input_array, input_kernel) 137 | np.savetxt("result/HM1_Convolve_result_1.txt", result_1) 138 | 139 | #task 3: convolution with sliding-window 140 | result_2 = convolve(input_array, input_kernel) 141 | np.savetxt("result/HM1_Convolve_result_2.txt", result_2) 142 | 143 | #task 4/5: Gaussian filter and Sobel filter 144 | input_img = read_img("Lenna.png")/255 145 | 146 | img_gadient_x = Sobel_filter_x(input_img) 147 | img_gadient_y = Sobel_filter_y(input_img) 148 | img_blur = Gaussian_filter(input_img) 149 | 150 | write_img("result/HM1_Convolve_img_gadient_x.png", img_gadient_x*255) 151 | write_img("result/HM1_Convolve_img_gadient_y.png", img_gadient_y*255) 152 | write_img("result/HM1_Convolve_img_blur.png", img_blur*255) 153 | 154 | 155 | 156 | 157 | -------------------------------------------------------------------------------- /计算机视觉导论/HW1/HM1_HarrisCorner.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from utils import read_img, draw_corner 3 | from HM1_Convolve import convolve, Sobel_filter_x,Sobel_filter_y,padding 4 | 5 | 6 | 7 | def corner_response_function(input_img, window_size, alpha, threshold): 8 | """ 9 | The function you need to implement for Q3. 10 | Inputs: 11 | input_img: array(float) 12 | window_size: int 13 | alpha: float 14 | threshold: float 15 | Outputs: 16 | corner_list: array 17 | """ 18 | 19 | # please solve the corner_response_function of each window, 20 | # and keep windows with theta > threshold. 21 | # you can use several functions from HM1_Convolve to get 22 | # I_xx, I_yy, I_xy as well as the convolution result. 23 | # for detials of corner_response_function, please refer to the slides. 24 | 25 | Ix = Sobel_filter_x(input_img) 26 | Iy = Sobel_filter_y(input_img) 27 | 28 | window = np.ones((window_size, window_size)) 29 | a11 = convolve(Ix**2, window) 30 | a22 = convolve(Iy**2, window) 31 | a12 = convolve(Ix*Iy, window) 32 | a21 = a12 33 | 34 | tr = a11 + a22 35 | det = a11 * a22 - a12 * a21 36 | theta = det - alpha * (tr**2) 37 | theta[theta < threshold] = 0 38 | 39 | rows, cols = np.nonzero(theta) 40 | vals = theta[rows, cols] 41 | corner_list = np.column_stack((rows, cols, vals)) 42 | 43 | return corner_list # array, each row contains information about one corner, namely (index of row, index of col, theta) 44 | 45 | 46 | 47 | if __name__=="__main__": 48 | 49 | #Load the input images 50 | input_img = read_img("hand_writting.png")/255. 51 | 52 | #you can adjust the parameters to fit your own implementation 53 | window_size = 5 54 | alpha = 0.04 55 | threshold = 10 56 | 57 | corner_list = corner_response_function(input_img,window_size,alpha,threshold) 58 | 59 | # NMS 60 | corner_list_sorted = sorted(corner_list, key = lambda x: x[2], reverse = True) 61 | NML_selected = [] 62 | NML_selected.append(corner_list_sorted[0][:-1]) 63 | dis = 10 64 | for i in corner_list_sorted : 65 | for j in NML_selected : 66 | if(abs(i[0] - j[0]) <= dis and abs(i[1] - j[1]) <= dis) : 67 | break 68 | else : 69 | NML_selected.append(i[:-1]) 70 | 71 | 72 | #save results 73 | draw_corner("hand_writting.png", "result/HM1_HarrisCorner.png", NML_selected) 74 | -------------------------------------------------------------------------------- /计算机视觉导论/HW1/HM1_RANSAC.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from utils import draw_save_plane_with_points, normalize 3 | 4 | 5 | if __name__ == "__main__": 6 | 7 | 8 | np.random.seed(0) 9 | # load data, total 130 points inlcuding 100 inliers and 30 outliers 10 | # to simplify this problem, we provide the number of inliers and outliers here 11 | 12 | noise_points = np.loadtxt("HM1_ransac_points.txt") 13 | 14 | 15 | #RANSAC 16 | # Please formulate the palnace function as: A*x+B*y+C*z+D=0 17 | 18 | INLIER = 100 19 | OUTLIER = 30 20 | TOTAL = INLIER + OUTLIER 21 | PROB = 0.001 22 | SAMPLE_SIZE = 3 23 | fail_p = 1 - ((INLIER - SAMPLE_SIZE) / TOTAL) ** SAMPLE_SIZE 24 | t = np.log(PROB) / np.log(fail_p) 25 | t = int(t) + 1 26 | 27 | sample_time = t # the minimal time that can guarantee the probability of at least one hypothesis does not contain any outliers is larger than 99.9% 28 | distance_threshold = 0.05 29 | 30 | # sample points group 31 | indices = np.random.choice(TOTAL, (sample_time, SAMPLE_SIZE), replace=False) # (N, 3) (denote N as `sample_time`) 32 | samples = noise_points[indices] # (N, 3, 3) 33 | 34 | # estimate the plane with sampled points group 35 | p0 = samples[:, 0] # (N, 3) 36 | p1 = samples[:, 1] # (N, 3) 37 | p2 = samples[:, 2] # (N, 3) 38 | 39 | v1 = p1 - p0 # (N, 3) 40 | v2 = p2 - p0 # (N, 3) 41 | normal = np.cross(v1, v2) # (N, 3) 42 | D = -np.sum(normal * p0, axis=1, keepdims=True) # (N, 1) 43 | plane = np.hstack((normal, D)) # (N, 4) 44 | 45 | norm = np.linalg.norm(normal, axis=1, keepdims=True) # (N, 1) 46 | plane = plane / norm # (N, 4) 47 | 48 | #evaluate inliers (with point-to-plance distance < distance_threshold) 49 | point = np.vstack((noise_points.T, np.ones((1, TOTAL)))) # (4, TOTAL) 50 | distance = np.abs(plane @ point) # (N, TOTAL) 51 | inlier_cnt = np.sum(distance < distance_threshold, axis=1) # (N,) 52 | 53 | best_sample = np.argmax(inlier_cnt) # int 54 | best_plane = plane[best_sample] # (4,) 55 | inlier_idx = distance[best_sample] < distance_threshold # (TOTAL,) 56 | inlier = noise_points[inlier_idx] # (M, 3) (denote M as inlier number) 57 | 58 | # minimize the sum of squared perpendicular distances of all inliers with least-squared method 59 | A = np.hstack((inlier, np.ones((inlier.shape[0], 1)))) # (M, 4) 60 | U, S, Vt = np.linalg.svd(A) 61 | pf = Vt[-1] 62 | 63 | # draw the estimated plane with points and save the results 64 | # check the utils.py for more details 65 | # pf: [A,B,C,D] contains the parameters of palnace function A*x+B*y+C*z+D=0 66 | pf = normalize(pf) 67 | draw_save_plane_with_points(pf, noise_points,"result/HM1_RANSAC_fig.png") 68 | np.savetxt("result/HM1_RANSAC_plane.txt", pf) 69 | np.savetxt('result/HM1_RANSAC_sample_time.txt', np.array([sample_time])) 70 | -------------------------------------------------------------------------------- /计算机视觉导论/HW1/HM1_ransac_points.txt: -------------------------------------------------------------------------------- 1 | -4.093493911942476249e-01 -7.016740857210204130e-01 7.124552243345759095e+00 2 | -1.595510154708845718e-01 -5.226357175071079109e-01 6.636144289736422230e+00 3 | 9.814249292617573950e-01 -5.245470921999544345e-01 8.647624290948501269e+00 4 | 3.392004764932596839e-01 2.424858388013939603e-01 3.551161541559510137e+00 5 | -6.755718021005385765e-02 -7.632644977534328579e-01 8.042915113873814192e+00 6 | 8.015483583213673935e-01 5.879251209592568639e-01 2.574406684379292098e+00 7 | 6.304149149058813073e-01 9.819097026608203382e-01 2.351012602605501989e-01 8 | 6.275338340729754716e-01 -1.573642452015624471e-01 6.126425296677871835e+00 9 | -9.172671368306017925e-02 -7.893478293698137360e-01 8.135510855553143017e+00 10 | 3.954554691912652853e-01 1.305708026794529797e-01 4.229071113484499378e+00 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in filenames: 11 | print(filename) 12 | if filename.endswith('.zip'): 13 | continue 14 | zip.write(os.path.join(path, filename), os.path.join(fpath, filename)) 15 | zip.close() 16 | 17 | 18 | if __name__ == "__main__": 19 | 20 | # --------------------------------------------------------- 21 | # 请用你的学号和姓名替换下面的内容，注意参照例子的格式，使用拼音而非中文 22 | id = 2000010000 23 | name = 'ZhangSan' 24 | # --------------------------------------------------------- 25 | 26 | zip_name = f'{id}_{name}.zip' 27 | current_file_directory_path = os.path.dirname(os.path.abspath(__file__)) 28 | input_path = current_file_directory_path 29 | output_path = os.path.join(current_file_directory_path, zip_name) 30 | 31 | zipDir(input_path, output_path) -------------------------------------------------------------------------------- /计算机视觉导论/HW1/result/HM1_Canny_result.png: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- https://raw.githubusercontent.com/CS-icez/pku-cs-homework/1040ca9fae63f9aea17dbf807ba1de8a1f668b92/计算机视觉导论/HW1/result/HM1_Convolve_img_gadient_y.png -------------------------------------------------------------------------------- /计算机视觉导论/HW1/result/HM1_Convolve_replicate_pad.txt: -------------------------------------------------------------------------------- 1 | 6.121701756176186615e-01 6.121701756176186615e-01 1.690697543456364249e-01 4.360590193711701978e-01 7.692624725231228533e-01 2.953253044028761876e-01 1.491629571394897935e-01 1.491629571394897935e-01 2 | 6.121701756176186615e-01 6.121701756176186615e-01 1.690697543456364249e-01 4.360590193711701978e-01 7.692624725231228533e-01 2.953253044028761876e-01 1.491629571394897935e-01 1.491629571394897935e-01 3 | 2.247832454416176517e-02 2.247832454416176517e-02 4.202244922645577141e-01 2.386821412464460446e-01 3.376561918887923675e-01 9.907124646308786975e-01 2.377264539000227828e-01 2.377264539000227828e-01 4 | 8.119265945121156847e-02 8.119265945121156847e-02 6.696002382466298419e-01 6.212429194006969801e-01 2.742535302065184366e-01 4.662214098949730712e-01 1.183677511232835711e-01 1.183677511232835711e-01 5 | 7.395756414320420191e-02 7.395756414320420191e-02 9.007741791606836967e-01 7.939625604796284319e-01 8.405696487242811932e-01 8.152074574529406537e-01 9.909548513304101691e-01 9.909548513304101691e-01 6 | 5.772738296076793674e-01 5.772738296076793674e-01 8.137669170364877358e-01 4.213178773992187764e-01 2.744795981315373279e-02 4.541366431584699104e-01 1.053260853150931320e-01 1.053260853150931320e-01 7 | 8.172200615549912728e-01 8.172200615549912728e-01 6.977277345956326426e-01 5.652854013397264898e-01 2.742272954485119874e-01 9.984737436896493623e-01 1.380420530559359760e-01 1.380420530559359760e-01 8 | 8.172200615549912728e-01 8.172200615549912728e-01 6.977277345956326426e-01 5.652854013397264898e-01 2.742272954485119874e-01 9.984737436896493623e-01 1.380420530559359760e-01 1.380420530559359760e-01 9 | -------------------------------------------------------------------------------- /计算机视觉导论/HW1/result/HM1_Convolve_result_1.txt: -------------------------------------------------------------------------------- 1 | 6.726929099524359135e-01 1.299302001764504944e+00 1.242277848380564098e+00 1.990147764064087443e+00 1.979515410581911272e+00 8.042878141780547274e-01 2 | 1.233052743124086525e+00 2.113876763734376940e+00 2.199275198995008118e+00 2.326898746813730767e+00 1.975711725876759361e+00 1.312757340422121555e+00 3 | 1.373123590780443104e+00 2.444661726762888065e+00 3.120520693998054096e+00 3.237767655645754861e+00 3.132839976693769124e+00 2.301233400731500289e+00 4 | 1.989684052026016836e+00 2.631015404889353260e+00 2.756966696140569884e+00 2.457542757960983870e+00 2.250434181206241391e+00 1.502196161584189449e+00 5 | 2.328109733508479273e+00 3.096520763029277923e+00 3.029142896424832365e+00 3.081291009456641028e+00 2.682901772749619607e+00 1.796120230915411842e+00 6 | 1.152878120400029349e+00 1.967086661250722068e+00 1.516439580734680881e+00 1.356437579617560063e+00 8.570340226429324915e-01 1.101869898510777102e+00 7 | -------------------------------------------------------------------------------- /计算机视觉导论/HW1/result/HM1_Convolve_result_2.txt: -------------------------------------------------------------------------------- 1 | 2.113876763734377384e+00 2.199275198995008562e+00 2.326898746813730767e+00 1.975711725876758917e+00 2 | 2.444661726762888510e+00 3.120520693998053652e+00 3.237767655645754861e+00 3.132839976693769124e+00 3 | 2.631015404889353260e+00 2.756966696140569439e+00 2.457542757960983870e+00 2.250434181206241391e+00 4 | 3.096520763029277923e+00 3.029142896424831921e+00 3.081291009456641028e+00 2.682901772749619607e+00 5 | -------------------------------------------------------------------------------- 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9.007741791606836967e-01 7.939625604796284319e-01 8.405696487242811932e-01 8.152074574529406537e-01 9.909548513304101691e-01 0.000000000000000000e+00 6 | 0.000000000000000000e+00 5.772738296076793674e-01 8.137669170364877358e-01 4.213178773992187764e-01 2.744795981315373279e-02 4.541366431584699104e-01 1.053260853150931320e-01 0.000000000000000000e+00 7 | 0.000000000000000000e+00 8.172200615549912728e-01 6.977277345956326426e-01 5.652854013397264898e-01 2.742272954485119874e-01 9.984737436896493623e-01 1.380420530559359760e-01 0.000000000000000000e+00 8 | 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 9 | -------------------------------------------------------------------------------- /计算机视觉导论/HW1/result/HM1_HarrisCorner.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CS-icez/pku-cs-homework/1040ca9fae63f9aea17dbf807ba1de8a1f668b92/计算机视觉导论/HW1/result/HM1_HarrisCorner.png -------------------------------------------------------------------------------- /计算机视觉导论/HW1/result/HM1_RANSAC_fig.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CS-icez/pku-cs-homework/1040ca9fae63f9aea17dbf807ba1de8a1f668b92/计算机视觉导论/HW1/result/HM1_RANSAC_fig.png -------------------------------------------------------------------------------- /计算机视觉导论/HW1/result/HM1_RANSAC_plane.txt: -------------------------------------------------------------------------------- 1 | 2.521511431554123162e-01 2 | -7.429045891115706457e-01 3 | -1.433801776492190649e-01 4 | 6.032865796117676815e-01 5 | -------------------------------------------------------------------------------- /计算机视觉导论/HW1/result/HM1_RANSAC_sample_time.txt: -------------------------------------------------------------------------------- 1 | 1.300000000000000000e+01 2 | -------------------------------------------------------------------------------- /计算机视觉导论/HW1/utils.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import matplotlib.pyplot as plt 3 | from mpl_toolkits.mplot3d import Axes3D 4 | import numpy as np 5 | 6 | def read_img(path): 7 | return cv2.imread(path, cv2.IMREAD_GRAYSCALE) 8 | 9 | def write_img(path, img): 10 | cv2.imwrite(path, img) 11 | 12 | def draw_corner(img_path, save_path, coner_list): 13 | vis_img = cv2.imread(img_path) 14 | for point in coner_list: 15 | cv2.circle(vis_img, (int(point[1]), int(point[0])), 2, (0,0,255), -1) 16 | 17 | write_img(save_path, vis_img) 18 | 19 | 20 | def plane_func(pf, p_xy): 21 | return (pf[0]*p_xy[:,0] + pf[1]*p_xy[:,1] + pf[3])/-pf[2] 22 | 23 | def draw_save_plane_with_points(estimated_pf, p, path): # plane function: pf[0]*x+pf[1]*y+pf[2]*z+pf[3]=0 24 | fig = plt.figure() 25 | ax = fig.add_subplot(111, projection='3d') 26 | ax.scatter(p[:,0], p[:,1], p[:,2], c="g", s=10) 27 | x = np.linspace(-1, 1, 100) 28 | y = np.linspace(-1, 1, 100) 29 | x, y = np.meshgrid(x, y) 30 | estimated_plane_z = plane_func(estimated_pf, np.concatenate((x.reshape(-1,1), y.reshape(-1,1)),axis=1)) 31 | ax.plot_surface(x, y, estimated_plane_z.reshape(100, 100), alpha=0.5) 32 | ax.view_init(elev=45, azim=315) 33 | plt.savefig(path) 34 | # plt.show() 35 | plt.clf() 36 | 37 | def normalize(pf): 38 | return pf / np.linalg.norm(pf) * np.sign(pf[0]) 39 | 40 | -------------------------------------------------------------------------------- /计算机视觉导论/HW2/BP.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import cv2 3 | import os 4 | 5 | 6 | def read_img(path): 7 | return cv2.imread(path, cv2.IMREAD_GRAYSCALE) 8 | 9 | 10 | if __name__ == "__main__": 11 | 12 | #input 13 | input_vector = np.zeros((10,784)) 14 | for i in range(10): 15 | input_vector[i,:] = read_img("mnist_subset/"+str(i)+".png").reshape(-1)/255. 16 | gt_y = np.zeros((10,1)) 17 | gt_y[0] =1 18 | 19 | np.random.seed(14) 20 | 21 | #Intialization MLP (784 -> 16 -> 1) 22 | MLP_layer_1 = np.random.randn(784,16) 23 | MLP_layer_2 = np.random.randn(16,1) 24 | lr=1e-1 25 | loss_list=[] 26 | 27 | for i in range(50): 28 | #Forward 29 | output_layer_1 = input_vector.dot(MLP_layer_1) 30 | output_layer_1_act = 1 / (1 + np.exp(-output_layer_1)) #sigmoid activation function 31 | output_layer_2 = output_layer_1_act.dot(MLP_layer_2) 32 | pred_y = 1 / (1 + np.exp(-output_layer_2)) #sigmoid activation function 33 | loss = -(gt_y * np.log(pred_y) + (1 - gt_y) * np.log(1 - pred_y)).sum() #cross-entroy loss 34 | print("iteration: %d, loss: %f" % (i + 1 ,loss)) 35 | loss_list.append(loss) 36 | 37 | 38 | # Backward : compute the gradient of paratmerters of layer1 (grad_layer_1) and layer2 (grad_layer_2) 39 | 40 | grad_output_layer_2 = pred_y - gt_y 41 | grad_layer_2 = output_layer_1_act.T.dot(grad_output_layer_2) 42 | grad_output_layer_1_act = grad_output_layer_2.dot(MLP_layer_2.T) 43 | grad_output_layer_1 = grad_output_layer_1_act * (output_layer_1_act * (1 - output_layer_1_act)) 44 | grad_layer_1 = input_vector.T.dot(grad_output_layer_1) 45 | 46 | MLP_layer_1 -= lr * grad_layer_1 47 | MLP_layer_2 -= lr * grad_layer_2 48 | 49 | os.makedirs("results", exist_ok=True) 50 | np.savetxt("results/BP.txt", loss_list) -------------------------------------------------------------------------------- /计算机视觉导论/HW2/README.md: -------------------------------------------------------------------------------- 1 | # Installation 2 | 3 | - We recommand using [Anaconda](https://www.anaconda.com/) to manage your python environments. Use the following command to create a new environment. Please change the 3.x bellow to your python version. Note that, we only test the code on python version higher than 3.7. 4 | ```bash 5 | conda create -n hw2 python=3.x 6 | conda activate hw2 7 | ``` 8 | 9 | - We recommand using [Tsinghua Mirror](https://mirrors.tuna.tsinghua.edu.cn/) to install dependent packages. 10 | 11 | ```bash 12 | # pip 13 | python -m pip install --upgrade pip 14 | pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple 15 | 16 | # conda 17 | conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch 18 | conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main 19 | conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free 20 | conda config --set show_channel_urls yes 21 | ``` 22 | 23 | - Now you can install [pytorch](https://pytorch.org/get-started/previous-versions/) and other dependencies as below. 24 | ```bash 25 | conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cpuonly # remember to remove "-c pytorh"! 26 | 27 | # tips: try "pip install xxx" first before "conda install xxx" 28 | pip install opencv-python 29 | pip install pillow 30 | pip install tensorboardx 31 | pip install tensorflow # for tensorboardx 32 | ``` 33 | The specific version of pytorch should make no difference for this assignment, since we only use some basic functions. You can also install the GPU version if you can access a GPU. 34 | 35 | # CIFAR-10 36 | 37 | ## Dataset 38 | - Download cifar-10 dataset from [here](https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz) and save to `datasets`. Then unzip it. 39 | 40 | ```bash 41 | cd datasets 42 | wget https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz 43 | tar -zxvf cifar-10-python.tar.gz 44 | ``` 45 | 46 | ## Visualization 47 | - Visualize the structure of your network 48 | ```bash 49 | cd cifar-10 50 | python network.py 51 | cd ../experiments 52 | tensorboard --logdir . 53 | ``` 54 | 55 | - Visualize and check the input data 56 | ```bash 57 | cd cifar-10 58 | python dataset.py 59 | ``` 60 | 61 | - Train network and visualize the curves 62 | ```bash 63 | cd cifar-10 64 | python train.py 65 | cd ../experiments 66 | tensorboard --logdir . 67 | ``` 68 | 69 | # Submission 70 | - Compress the entire folder **except** `datasets` and `experiments`. 71 | 72 | - Rename the compressed folder to **Name_ID.zip** and submit to [course.pku.edu.cn](https://course.pku.edu.cn/). 73 | 74 | - The folder named `results` should be structed as follows. 75 | ```bash 76 | results 77 | ├── bn_loss.txt 78 | ├── Lenna.png 79 | ├── Lenna_aug1.png 80 | ├── Lenna_aug2.png 81 | ├── Screenshot_LearningRate.png 82 | └── Screenshot_YourBest.png 83 | 84 | ``` 85 | 86 | # Appendix 87 | We list some libraries that may help you solve this assignment. 88 | 89 | - [TensorboardX](https://pytorch.org/docs/stable/tensorboard.html) 90 | - [OpenCV-Python](https://docs.opencv.org/4.x/d6/d00/tutorial_py_root.html) 91 | - [Pillow (PIL)](https://pillow.readthedocs.io/en/stable/) 92 | - [Torchvision.transforms](https://pytorch.org/vision/0.9/transforms.html) 93 | -------------------------------------------------------------------------------- /计算机视觉导论/HW2/batch_normalization/bn.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import cv2 3 | import os 4 | 5 | 6 | # eps may help you to deal with numerical problem 7 | eps = 1e-5 8 | def bn_forward_test(x, gamma, beta, mean, var): 9 | 10 | #----------------TODO------------------ 11 | # Implement forward 12 | x_hat = (x - mean) / np.sqrt(var + eps) 13 | out = gamma * x_hat + beta 14 | #----------------TODO------------------ 15 | 16 | return out 17 | 18 | def bn_forward_train(x, gamma, beta): 19 | 20 | #----------------TODO------------------ 21 | # Implement forward 22 | sample_mean = np.mean(x, axis=0) 23 | sample_var = np.var(x, axis=0) 24 | x_hat = (x - sample_mean) / np.sqrt(sample_var + eps) 25 | out = gamma * x_hat + beta 26 | #----------------TODO------------------ 27 | 28 | # save intermidiate variables for computing the gradient when backward 29 | cache = (gamma, x, sample_mean, sample_var, x_hat) 30 | return out, cache 31 | 32 | def bn_backward(dout, cache): 33 | 34 | #----------------TODO------------------ 35 | # Implement backward 36 | #----------------TODO------------------ 37 | gamma, x, mean, var, x_hat = cache 38 | B = x.shape[0] 39 | 40 | dgamma = np.sum(dout * x_hat, axis=0) 41 | dbeta = np.sum(dout, axis=0) 42 | 43 | dx_hat = dout * gamma 44 | t = np.einsum('kj,ij->kij', x_hat, x_hat) 45 | t = (1 + t) / B 46 | t = np.einsum('kj,kij->ij', dx_hat, t) 47 | dx = (dx_hat - t) / np.sqrt(var + eps) 48 | 49 | return dx, dgamma, dbeta 50 | 51 | # This function may help you to check your code 52 | def print_info(x): 53 | print('mean:', np.mean(x,axis=0)) 54 | print('var:',np.var(x,axis=0)) 55 | print('------------------') 56 | return 57 | 58 | if __name__ == "__main__": 59 | HW_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) 60 | 61 | # input data 62 | train_data = np.zeros((9,784)) 63 | for i in range(9): 64 | train_data[i,:] = cv2.imread(os.path.join(HW_dir, "mnist_subset", f"{i}.png"), cv2.IMREAD_GRAYSCALE).reshape(-1)/255. 65 | gt_y = np.zeros((9,1)) 66 | gt_y[0] =1 67 | 68 | val_data = np.zeros((1,784)) 69 | val_data[0,:] = cv2.imread(os.path.join(HW_dir, "mnist_subset", "9.png"), cv2.IMREAD_GRAYSCALE).reshape(-1)/255. 70 | val_gt = np.zeros((1,1)) 71 | 72 | np.random.seed(14) 73 | 74 | # Intialize MLP (784 -> 16 -> 1) 75 | MLP_layer_1 = np.random.randn(784,16) 76 | MLP_layer_2 = np.random.randn(16,1) 77 | 78 | # Initialize gamma and beta 79 | gamma = np.random.randn(16) 80 | beta = np.random.randn(16) 81 | 82 | lr=1e-1 83 | loss_list=[] 84 | 85 | # ---------------- TODO ------------------- 86 | # compute mean and var for testing 87 | # add codes anywhere as you need 88 | # ---------------- TODO ------------------- 89 | mean = np.zeros(16) 90 | var = np.ones(16) 91 | 92 | # training 93 | for i in range(50): 94 | # Forward 95 | output_layer_1 = train_data.dot(MLP_layer_1) 96 | output_layer_1_bn, cache = bn_forward_train(output_layer_1, gamma, beta) 97 | output_layer_1_act = 1 / (1+np.exp(-output_layer_1_bn)) #sigmoid activation function 98 | output_layer_2 = output_layer_1_act.dot(MLP_layer_2) 99 | pred_y = 1 / (1+np.exp(-output_layer_2)) #sigmoid activation function 100 | 101 | r = 0.9 102 | mean = r * cache[2] + (1-r) * mean 103 | var = r * cache[3] + (1-r) * var 104 | 105 | # compute loss 106 | loss = -( gt_y * np.log(pred_y) + (1-gt_y) * np.log(1-pred_y)).sum() 107 | print("iteration: %d, loss: %f" % (i+1 ,loss)) 108 | loss_list.append(loss) 109 | 110 | # Backward : compute the gradient of paratmerters of layer1 (grad_layer_1) and layer2 (grad_layer_2) 111 | grad_pred_y = -(gt_y/pred_y) + (1-gt_y)/(1-pred_y) 112 | grad_activation_func = grad_pred_y * pred_y * (1-pred_y) 113 | grad_layer_2 = output_layer_1_act.T.dot(grad_activation_func) 114 | grad_output_layer_1_act = grad_activation_func.dot(MLP_layer_2.T) 115 | grad_output_layer_1_bn = grad_output_layer_1_act * (1-output_layer_1_act) * output_layer_1_act 116 | grad_output_layer_1, grad_gamma, grad_beta = bn_backward(grad_output_layer_1_bn, cache) 117 | grad_layer_1 = train_data.T.dot(grad_output_layer_1) 118 | 119 | # update parameters 120 | gamma -= lr * grad_gamma 121 | beta -= lr * grad_beta 122 | MLP_layer_1 -= lr * grad_layer_1 123 | MLP_layer_2 -= lr * grad_layer_2 124 | 125 | # validate 126 | output_layer_1 = val_data.dot(MLP_layer_1) 127 | output_layer_1_bn = bn_forward_test(output_layer_1, gamma, beta, mean, var) 128 | output_layer_1_act = 1 / (1+np.exp(-output_layer_1_bn)) #sigmoid activation function 129 | output_layer_2 = output_layer_1_act.dot(MLP_layer_2) 130 | pred_y = 1 / (1+np.exp(-output_layer_2)) #sigmoid activation function 131 | loss = -( val_gt * np.log(pred_y) + (1-val_gt) * np.log(1-pred_y)).sum() 132 | print("validation loss: %f" % (loss)) 133 | loss_list.append(loss) 134 | 135 | os.makedirs(os.path.join(os.path.join(HW_dir), "results"), exist_ok=True) 136 | np.savetxt(os.path.join(os.path.join(HW_dir), "results", "bn_loss.txt"), loss_list) -------------------------------------------------------------------------------- /计算机视觉导论/HW2/cifar-10/Lenna.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CS-icez/pku-cs-homework/1040ca9fae63f9aea17dbf807ba1de8a1f668b92/计算机视觉导论/HW2/cifar-10/Lenna.png -------------------------------------------------------------------------------- /计算机视觉导论/HW2/cifar-10/dataset.py: -------------------------------------------------------------------------------- 1 | import os 2 | import os.path 3 | import numpy as np 4 | import pickle 5 | import torch 6 | import torchvision.transforms as tfs 7 | from PIL import Image 8 | 9 | class CIFAR10(torch.utils.data.Dataset): 10 | """ 11 | modified from `CIFAR10 `_ Dataset. 12 | """ 13 | def __init__(self, train=True): 14 | super(CIFAR10, self).__init__() 15 | 16 | self.base_folder = '../datasets/cifar-10-batches-py' 17 | self.train_list = ['data_batch_1', 'data_batch_2', 'data_batch_3', 'data_batch_4','data_batch_5'] 18 | self.test_list = ['test_batch'] 19 | 20 | self.meta = { 21 | 'filename': 'batches.meta', 22 | 'key': 'label_names' 23 | } 24 | 25 | self.train = train # training set or test set 26 | if self.train: 27 | file_list = self.train_list 28 | else: 29 | file_list = self.test_list 30 | 31 | self.data = [] 32 | self.targets = [] 33 | 34 | # now load the picked numpy arrays 35 | for file_name in file_list: 36 | file_path = os.path.join(self.base_folder, file_name) 37 | with open(file_path, 'rb') as f: 38 | entry = pickle.load(f, encoding='latin1') 39 | self.data.append(entry['data']) 40 | if 'labels' in entry: 41 | self.targets.extend(entry['labels']) 42 | else: 43 | self.targets.extend(entry['fine_labels']) 44 | 45 | self.data = np.vstack(self.data).reshape(-1, 3, 32, 32) 46 | self.data = self.data.transpose((0, 2, 3, 1)) # convert to HWC 47 | 48 | self._load_meta() 49 | 50 | def _load_meta(self): 51 | path = os.path.join(self.base_folder, self.meta['filename']) 52 | with open(path, 'rb') as infile: 53 | data = pickle.load(infile, encoding='latin1') 54 | self.classes = data[self.meta['key']] 55 | self.class_to_idx = {_class: i for i, _class in enumerate(self.classes)} 56 | 57 | def __getitem__(self, index): 58 | """ 59 | Args: 60 | index (int): Index 61 | 62 | Returns: 63 | tuple: (image, target) where target is index of the target class. 64 | """ 65 | img, target = self.data[index], self.targets[index] 66 | # img = img.astype(np.float32) 67 | # img = img.transpose(2, 0, 1) 68 | 69 | # ------------TODO-------------- 70 | # data augmentation 71 | transform = tfs.Compose([ 72 | tfs.ToPILImage(), 73 | tfs.RandomHorizontalFlip(), 74 | tfs.RandomRotation(10), 75 | tfs.ToTensor() 76 | ]) 77 | img = transform(img) 78 | # ------------TODO-------------- 79 | 80 | return img, target 81 | 82 | def __len__(self): 83 | return len(self.data) 84 | 85 | 86 | if __name__ == '__main__': 87 | # -------------------------------- 88 | # The resolution of CIFAR-10 is tooooo low 89 | # You can use Lenna.png as an example to visualize and check your code. 90 | # Submit the origin image "Lenna.png" as well as at least two augmented images of Lenna named "Lenna_aug1.png", "Lenna_aug2.png" ... 91 | # -------------------------------- 92 | 93 | # # Visualize CIFAR-10. For someone who are intersted. 94 | # train_dataset = CIFAR10() 95 | # i = 0 96 | # for imgs, labels in train_dataset: 97 | # imgs = imgs.transpose(1,2,0) 98 | # cv2.imwrite(f'aug1_{i}.png', imgs) 99 | # i += 1 100 | # if i == 10: 101 | # break 102 | 103 | # Visualize and save for submission 104 | img = Image.open('Lenna.png') 105 | img.save('../results/Lenna.png') 106 | 107 | # --------------TODO------------------ 108 | # Copy the first kind of your augmentation code here 109 | transform = tfs.RandomHorizontalFlip() 110 | # --------------TODO------------------ 111 | aug1 = transform(img) 112 | aug1.save(f'../results/Lenna_aug1.png') 113 | 114 | # --------------TODO------------------ 115 | # Copy the second kind of your augmentation code here 116 | transform = tfs.RandomRotation(10) 117 | # --------------TODO------------------ 118 | aug2 = transform(img) 119 | aug2.save(f'../results/Lenna_aug2.png') 120 | 121 | -------------------------------------------------------------------------------- /计算机视觉导论/HW2/cifar-10/network.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | 3 | 4 | class ConvNet(nn.Module): 5 | def __init__(self, num_class=10): 6 | super(ConvNet, self).__init__() 7 | pass 8 | # ----------TODO------------ 9 | # define a network 10 | wraped_conv_tuple = lambda in_channels, out_channels: ( 11 | nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), 12 | nn.BatchNorm2d(out_channels), 13 | nn.ReLU(inplace=True) 14 | ) 15 | # Simplified vgg11_bn. 16 | self.features = nn.Sequential( 17 | *wraped_conv_tuple(3, 64), 18 | nn.MaxPool2d(kernel_size=2), 19 | *wraped_conv_tuple(64, 128), 20 | nn.MaxPool2d(kernel_size=2), 21 | *wraped_conv_tuple(128, 256), 22 | nn.MaxPool2d(kernel_size=2), 23 | *wraped_conv_tuple(256, 512), 24 | nn.MaxPool2d(kernel_size=2), 25 | *wraped_conv_tuple(512, 512) 26 | ) 27 | self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) 28 | self.classifier = nn.Sequential( 29 | nn.Flatten(), 30 | nn.Linear(512, 128), 31 | nn.ReLU(inplace=True), 32 | nn.Dropout(), 33 | nn.Linear(128, 128), 34 | nn.ReLU(inplace=True), 35 | nn.Dropout(), 36 | nn.Linear(128, num_class), 37 | nn.Softmax() 38 | ) 39 | # ----------TODO------------ 40 | 41 | def forward(self, x): 42 | 43 | # ----------TODO------------ 44 | # network forwarding 45 | # ----------TODO------------ 46 | x = self.features(x) 47 | x = self.avgpool(x) 48 | x = self.classifier(x) 49 | return x 50 | 51 | 52 | if __name__ == '__main__': 53 | import torch 54 | from torch.utils.tensorboard import SummaryWriter 55 | from dataset import CIFAR10 56 | writer = SummaryWriter(log_dir='../experiments/network_structure') 57 | net = ConvNet() 58 | train_dataset = CIFAR10() 59 | train_loader = torch.utils.data.DataLoader( 60 | train_dataset, batch_size=2, shuffle=False, num_workers=2) 61 | # Write a CNN graph. 62 | # Please save a figure/screenshot to '../results' for submission. 63 | for imgs, labels in train_loader: 64 | writer.add_graph(net, imgs) 65 | writer.close() 66 | break 67 | -------------------------------------------------------------------------------- /计算机视觉导论/HW2/cifar-10/train.py: -------------------------------------------------------------------------------- 1 | import os 2 | import torch 3 | from dataset import CIFAR10 4 | from network import ConvNet 5 | import tqdm 6 | import torch.optim as optim 7 | from util import evaluate, AverageMeter 8 | import argparse 9 | from torch.utils.tensorboard import SummaryWriter 10 | 11 | 12 | def MyCELoss(pred, gt): 13 | # ----------TODO------------ 14 | # Implement CE loss here 15 | t = torch.zeros_like(pred) 16 | t[torch.arange(t.shape[0]), gt] = 1 17 | loss = -torch.sum(t * torch.log(pred + 1e-10), dim=1) 18 | loss = torch.mean(loss) 19 | # ----------TODO------------ 20 | return loss 21 | 22 | 23 | def validate(epoch, model, val_loader, writer): 24 | model.eval() 25 | top1 = AverageMeter() 26 | top5 = AverageMeter() 27 | for imgs, labels in tqdm.tqdm(val_loader): 28 | if torch.cuda.is_available(): 29 | imgs = imgs.cuda() 30 | bsz = labels.shape[0] 31 | output = model(imgs) 32 | if torch.cuda.is_available(): 33 | output = output.cpu() 34 | # update metric 35 | acc1, acc5 = evaluate(output, labels, topk=(1, 5)) 36 | top1.update(acc1.item(), bsz) 37 | top5.update(acc5.item(), bsz) 38 | 39 | # ----------TODO------------ 40 | # draw accuracy curve! 41 | writer.add_scalar('val/top1', top1.avg, epoch) 42 | writer.add_scalar('val/top5', top5.avg, epoch) 43 | # ----------TODO------------ 44 | 45 | print(' Val Acc@1 {top1.avg:.3f}'.format(top1=top1)) 46 | print(' Val Acc@5 {top5.avg:.3f}'.format(top5=top5)) 47 | return 48 | 49 | def train(epoch, model, optimizer, criterion, train_loader, writer): 50 | model.train() 51 | 52 | losses = AverageMeter() 53 | top1 = AverageMeter() 54 | top5 = AverageMeter() 55 | iteration = len(train_loader) * epoch 56 | for imgs, labels in tqdm.tqdm(train_loader): 57 | bsz = labels.shape[0] 58 | 59 | if torch.cuda.is_available(): 60 | imgs = imgs.cuda() 61 | labels = labels.cuda() 62 | 63 | optimizer.zero_grad() 64 | 65 | output = model(imgs) 66 | loss = criterion(output, labels) 67 | 68 | # update metric 69 | losses.update(loss.item(), bsz) 70 | acc1, acc5 = evaluate(output, labels, topk=(1, 5)) 71 | top1.update(acc1.item(), bsz) 72 | top5.update(acc5.item(), bsz) 73 | 74 | loss.backward() 75 | optimizer.step() 76 | 77 | iteration += 1 78 | if iteration % 50 == 0: 79 | pass 80 | # ----------TODO------------ 81 | # draw loss curve and accuracy curve! 82 | writer.add_scalar('train/loss', losses.val, iteration) 83 | writer.add_scalar('train/top1', top1.avg, iteration) 84 | writer.add_scalar('train/top5', top5.avg, iteration) 85 | # ----------TODO------------ 86 | 87 | print(' Epoch: %d'%(epoch)) 88 | print(' Train Acc@1 {top1.avg:.3f}'.format(top1=top1)) 89 | print(' Train Acc@5 {top5.avg:.3f}'.format(top5=top5)) 90 | return 91 | 92 | def run(args): 93 | save_folder = os.path.join('../experiments', args.exp_name) 94 | ckpt_folder = os.path.join(save_folder, 'ckpt') 95 | log_folder = os.path.join(save_folder, 'log') 96 | os.makedirs(ckpt_folder, exist_ok=True) 97 | os.makedirs(log_folder, exist_ok=True) 98 | writer = SummaryWriter(log_dir=log_folder) 99 | 100 | # define dataset and dataloader 101 | train_dataset = CIFAR10() 102 | val_dataset = CIFAR10(train=False) 103 | train_loader = torch.utils.data.DataLoader( 104 | train_dataset, batch_size=args.batchsize, shuffle=True, num_workers=2) 105 | val_loader = torch.utils.data.DataLoader( 106 | val_dataset, batch_size=args.batchsize, shuffle=False, num_workers=2) 107 | 108 | # define network 109 | model = ConvNet() 110 | if torch.cuda.is_available(): 111 | model = model.cuda() 112 | 113 | # define optimizer 114 | optimizer = optim.Adam(model.parameters(), lr=args.lr) 115 | 116 | # define loss 117 | criterion = MyCELoss 118 | 119 | if args.cont: 120 | # load latest checkpoint 121 | ckpt_lst = os.listdir(ckpt_folder) 122 | ckpt_lst.sort(key=lambda x: int(x.split('_')[-1])) 123 | read_path = os.path.join(ckpt_folder, ckpt_lst[-1]) 124 | print('load checkpoint from %s'%(read_path)) 125 | checkpoint = torch.load(read_path) 126 | model.load_state_dict(checkpoint['model']) 127 | optimizer.load_state_dict(checkpoint['optimizer']) 128 | start_epoch = checkpoint['epoch'] + 1 129 | else: 130 | start_epoch = 0 131 | 132 | for epoch in range(start_epoch, args.total_epoch): 133 | train(epoch, model, optimizer, criterion, train_loader, writer) 134 | 135 | if epoch % args.save_freq == 0: 136 | state = { 137 | 'model': model.state_dict(), 138 | 'optimizer': optimizer.state_dict(), 139 | 'epoch': epoch, 140 | } 141 | save_file = os.path.join(ckpt_folder, 'ckpt_epoch_%s'%(str(epoch))) 142 | torch.save(state, save_file) 143 | 144 | with torch.no_grad(): 145 | validate(epoch, model, val_loader, writer) 146 | return 147 | 148 | if __name__ == '__main__': 149 | arg_parser = argparse.ArgumentParser() 150 | arg_parser.add_argument('--exp_name', '-e', type=str, required=True, help="The checkpoints and logs will be save in ./checkpoint/$EXP_NAME") 151 | arg_parser.add_argument('--lr', '-l', type=float, default=1e-4, help="Learning rate") 152 | arg_parser.add_argument('--save_freq', '-s', type=int, default=1, help="frequency of saving model") 153 | arg_parser.add_argument('--total_epoch', '-t', type=int, default=10, help="total epoch number for training") 154 | arg_parser.add_argument('--cont', '-c', action='store_true', help="whether to load saved checkpoints from $EXP_NAME and continue training") 155 | arg_parser.add_argument('--batchsize', '-b', type=int, default=20, help="batch size") 156 | args = arg_parser.parse_args() 157 | 158 | run(args) -------------------------------------------------------------------------------- /计算机视觉导论/HW2/cifar-10/util.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | class AverageMeter(object): 4 | """Computes and stores the average and current value 5 | Imported from https://github.com/pytorch/examples/blob/master/imagenet/main.py#L247-L262 6 | """ 7 | 8 | def __init__(self): 9 | self.reset() 10 | 11 | def reset(self): 12 | self.val = 0 13 | self.avg = 0 14 | self.sum = 0 15 | self.count = 0 16 | 17 | def update(self, val, n=1): 18 | self.val = val 19 | self.sum += val * n 20 | self.count += n 21 | self.avg = self.sum / self.count 22 | 23 | def evaluate(output, target, topk=(1,)): 24 | """Computes the accuracy over the k top predictions for the specified values of k""" 25 | with torch.no_grad(): 26 | maxk = max(topk) 27 | batch_size = target.size(0) 28 | 29 | _, pred = output.topk(maxk, 1, True, True) 30 | pred = pred.t() 31 | correct = pred.eq(target.view(1, -1).expand_as(pred)) 32 | 33 | res = [] 34 | for k in topk: 35 | correct_k = correct[:k].reshape(-1).float().sum(0) 36 | res.append(correct_k.mul_(100.0 / batch_size)) 37 | return res 38 | -------------------------------------------------------------------------------- /计算机视觉导论/HW2/mnist_subset/0.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CS-icez/pku-cs-homework/1040ca9fae63f9aea17dbf807ba1de8a1f668b92/计算机视觉导论/HW2/mnist_subset/0.png -------------------------------------------------------------------------------- /计算机视觉导论/HW2/mnist_subset/1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CS-icez/pku-cs-homework/1040ca9fae63f9aea17dbf807ba1de8a1f668b92/计算机视觉导论/HW2/mnist_subset/1.png -------------------------------------------------------------------------------- /计算机视觉导论/HW2/mnist_subset/2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CS-icez/pku-cs-homework/1040ca9fae63f9aea17dbf807ba1de8a1f668b92/计算机视觉导论/HW2/mnist_subset/2.png 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filenames in os.walk(os.path.join(input_path, "cifar-10")): 13 | fpath = path.replace(input_path, f'HW2_{zip_name}') 14 | for filename in filenames: 15 | if filename in ["dataset.py", "network.py", "train.py"]: 16 | zip.write(os.path.join(path, filename), os.path.join(fpath, filename)) 17 | for path, dirnames, filenames in os.walk(os.path.join(input_path, "results")): 18 | fpath = path.replace(input_path, f'HW2_{zip_name}') 19 | for filename in filenames: 20 | zip.write(os.path.join(path, filename), os.path.join(fpath, filename)) 21 | zip.write(os.path.join(input_path, "BP.py"), os.path.join(f'HW2_{zip_name}', "BP.py")) 22 | zip.close() 23 | 24 | 25 | if __name__ == "__main__": 26 | 27 | # --------------------------------------------------------- 28 | # 请用你的学号和姓名替换下面的内容，注意参照例子的格式，使用拼音而非中文 29 | id = 1900012950 30 | name = 'ZhangSan' 31 | # --------------------------------------------------------- 32 | 33 | zip_name = f'{id}_{name}.zip' 34 | input_path = os.path.dirname(os.path.abspath(__file__)) 35 | output_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), zip_name) 36 | 37 | zipHW2(input_path, output_path, zip_name.split(".")[0]) 38 | -------------------------------------------------------------------------------- /计算机视觉导论/HW2/results/BP.txt: -------------------------------------------------------------------------------- 1 | 9.779284769618634243e+00 2 | 4.179958911953551848e+00 3 | 3.212628089564451006e+00 4 | 2.781939232574513809e+00 5 | 2.521983696361397964e+00 6 | 2.317483164400497397e+00 7 | 2.140868130341873510e+00 8 | 1.984310588985534807e+00 9 | 1.844338086451177361e+00 10 | 1.718717435254833115e+00 11 | 1.605697625090467540e+00 12 | 1.503788222428893429e+00 13 | 1.411693541447843314e+00 14 | 1.328286516684636265e+00 15 | 1.252585864633001700e+00 16 | 1.183731285334008287e+00 17 | 1.120961515703425393e+00 18 | 1.063599520962808187e+00 19 | 1.011044783556032556e+00 20 | 9.627693681274921422e-01 21 | 9.183140418425473905e-01 22 | 8.772823295082272654e-01 23 | 8.393323255945468464e-01 24 | 8.041673595907683492e-01 25 | 7.715269910867809111e-01 26 | 7.411794933070361413e-01 27 | 7.129163281032779853e-01 28 | 6.865484872314185916e-01 29 | 6.619042068756149089e-01 30 | 6.388274911054411254e-01 31 | 6.171770013305257763e-01 32 | 5.968250494395517247e-01 33 | 5.776565842351083502e-01 34 | 5.595681544049057043e-01 35 | 5.424668735082949222e-01 36 | 5.262694221302972597e-01 37 | 5.109011162768259950e-01 38 | 4.962950599135954044e-01 39 | 4.823913885516625810e-01 40 | 4.691366018603405075e-01 41 | 4.564829767179177655e-01 42 | 4.443880476090866782e-01 43 | 4.328141385444577161e-01 44 | 4.217279295930687066e-01 45 | 4.111000417449204303e-01 46 | 4.009046262801188587e-01 47 | 3.911189491179150046e-01 48 | 3.817229664558677293e-01 49 | 3.726988946794456137e-01 50 | 3.640307839412161850e-01 51 | -------------------------------------------------------------------------------- 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/计算机视觉导论/HW2/results/bn_loss.txt: -------------------------------------------------------------------------------- 1 | 1.390185479780854827e+01 2 | 3.033448550031816016e+00 3 | 2.752286567016787977e+00 4 | 2.656001582022865293e+00 5 | 2.593305208061170930e+00 6 | 2.539740282361730994e+00 7 | 2.489078261615607968e+00 8 | 2.439408137224944095e+00 9 | 2.390089215026077962e+00 10 | 2.340904016676573107e+00 11 | 2.291789582483165777e+00 12 | 2.242744965600917517e+00 13 | 2.193797333728853438e+00 14 | 2.144988604485112749e+00 15 | 2.096369477225719091e+00 16 | 2.047996165041164218e+00 17 | 1.999928135915148175e+00 18 | 1.952226276004763506e+00 19 | 1.904951297351569206e+00 20 | 1.858162358771123790e+00 21 | 1.811915912627082070e+00 22 | 1.766264793810403466e+00 23 | 1.721257555994122113e+00 24 | 1.676938045352085016e+00 25 | 1.633345188631723177e+00 26 | 1.590512963047900685e+00 27 | 1.548470510562641511e+00 28 | 1.507242358394743409e+00 29 | 1.466848710181750670e+00 30 | 1.427305777002585430e+00 31 | 1.388626123398267742e+00 32 | 1.350819009712002305e+00 33 | 1.313890717861643243e+00 34 | 1.277844852656450536e+00 35 | 1.242682614788496176e+00 36 | 1.208403044640072199e+00 37 | 1.175003238132246830e+00 38 | 1.142478537135842132e+00 39 | 1.110822697637114720e+00 40 | 1.080028039056951883e+00 41 | 1.050085578008018050e+00 42 | 1.020985149458546459e+00 43 | 9.927155178477751196e-01 44 | 9.652644802354032327e-01 45 | 9.386189631135049005e-01 46 | 9.127651140943587871e-01 47 | 8.876883893285624216e-01 48 | 8.633736372114767521e-01 49 | 8.398051787026913351e-01 50 | 8.169668844084373438e-01 51 | 2.756584251033496191e-02 52 | -------------------------------------------------------------------------------- /计算机视觉导论/HW2/results/learning_rate.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CS-icez/pku-cs-homework/1040ca9fae63f9aea17dbf807ba1de8a1f668b92/计算机视觉导论/HW2/results/learning_rate.png 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Depth_pc 8 | 9 | - The `depth.png` and `seg.png` are generated by `render.py`, which uses `pyrender` to render `spot.obj` to 2D image plane. 10 | - If you implement the backprojection correctly, the result should be overlapped with `aligned_full_pc.txt`. 11 | - For interest readers, `aligned_full_pc.txt` is different from `raw_full_pc.txt`, which is sampled from `spot.obj`, because the default camera in `pyrender` looks at -z axis. 12 | 13 | 14 | 15 | # Mesh_pc 16 | 17 | - Note that we use the mean distance in this assignment, which is different from the slides. 18 | - We recommend using [this repo](https://github.com/j2kun/earthmover/) to compute earth move distance between two point clouds. 19 | ```bash 20 | cd mesh_pc 21 | git clone git@github.com:j2kun/earthmover.git 22 | ``` 23 | 24 | 25 | 26 | 27 | # Submission 28 | - Compress the entire folder using our provided `pack.py` and submit to [course.pku.edu.cn](https://course.pku.edu.cn/). 29 | - The folder named `results` in the main directoy should be structed as follows. 30 | ```bash 31 | results 32 | ├── bob.obj 33 | ├── spot.obj 34 | ├── calibr.npy 35 | ├── uniform_sampling_results.npy 36 | ├── fps_results.npy 37 | ├── metrics.npy 38 | ├── pc_from_depth.txt 39 | └── one_way_CD.txt 40 | ``` 41 | 42 | 43 | 44 | # Appendix 45 | 46 | - We recommand to use some softwares to visualize the results to help debug. 47 | - CloudCompare: https://www.danielgm.net/cc/ 48 | - Meshlab: https://www.meshlab.net/ 49 | - ... 50 | -------------------------------------------------------------------------------- /计算机视觉导论/HW3/camera_calibr/back_image.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CS-icez/pku-cs-homework/1040ca9fae63f9aea17dbf807ba1de8a1f668b92/计算机视觉导论/HW3/camera_calibr/back_image.npy -------------------------------------------------------------------------------- /计算机视觉导论/HW3/camera_calibr/calibr.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import numpy as np\n", 10 | "import cv2\n", 11 | "from matplotlib import pyplot as plt" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": null, 17 | "metadata": {}, 18 | "outputs": [], 19 | "source": [ 20 | "front_2Dcoord = np.load('front_image.npy', allow_pickle=True)\n", 21 | "back_2Dcoord = np.load('back_image.npy', allow_pickle=True)\n", 22 | "front_img = cv2.imread('front.png')" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": null, 28 | "metadata": {}, 29 | "outputs": [], 30 | "source": [ 31 | "#visualization\n", 32 | "print(front_2Dcoord.shape, back_2Dcoord.shape)\n", 33 | "from copy import deepcopy\n", 34 | "vis_img = deepcopy(front_img)\n", 35 | "for i in range(front_2Dcoord.shape[0]):\n", 36 | " cv2.circle(vis_img, (int(front_2Dcoord[i,0]), int(front_2Dcoord[i,1])), 2, (255,0,0), 2)\n", 37 | "plt.imshow(vis_img[:,:,::-1])\n", 38 | "plt.show()" 39 | ] 40 | }, 41 | { 42 | "cell_type": "code", 43 | "execution_count": null, 44 | "metadata": {}, 45 | "outputs": [], 46 | "source": [ 47 | "# ------------TODO----------------\n", 48 | "# Compute corresponding 3D coordinate\n", 49 | "t = np.array([[0, 0, 0], [50, 0, 0], [50, 50, 0], [0, 50, 0]])\n", 50 | "u = t + np.array([160, 240, 0])\n", 51 | "v = t + np.array([400, 400, 0])\n", 52 | "front_3Dcoord = np.vstack((t, u, v)).astype(np.float64)\n", 53 | "back_3Dcoord = front_3Dcoord + np.array([0, 0, 150])\n", 54 | "# ------------TODO----------------\n", 55 | "\n", 56 | "all_2Dcoord = np.concatenate((front_2Dcoord, back_2Dcoord), axis=0)\n", 57 | "all_3Dcoord = np.concatenate((front_3Dcoord, back_3Dcoord), axis=0)\n", 58 | "print(all_2Dcoord.shape, all_3Dcoord.shape)" 59 | ] 60 | }, 61 | { 62 | "cell_type": "code", 63 | "execution_count": null, 64 | "metadata": {}, 65 | "outputs": [], 66 | "source": [ 67 | "# ------------TODO----------------\n", 68 | "# Construct the Equation Pm=0 and solve m\n", 69 | "def f(p, u, v):\n", 70 | " p = np.hstack((p, 1))\n", 71 | " z = np.zeros((4,))\n", 72 | " l0 = np.hstack((p.T, z, -u * p.T))\n", 73 | " l1 = np.hstack((z, p.T, -v * p.T))\n", 74 | " return np.vstack((l0, l1))\n", 75 | "g = lambda x: f(x[:3], x[3], x[4])\n", 76 | "n = all_2Dcoord.shape[0]\n", 77 | "P = np.hstack((all_3Dcoord, all_2Dcoord))\n", 78 | "P = np.apply_along_axis(g, 1, P).reshape(2 * n, -1)\n", 79 | "U, S, Vt = np.linalg.svd(P)\n", 80 | "m = Vt[-1]\n", 81 | "# ------------TODO----------------" 82 | ] 83 | }, 84 | { 85 | "cell_type": "code", 86 | "execution_count": null, 87 | "metadata": {}, 88 | "outputs": [], 89 | "source": [ 90 | "# ------------TODO----------------\n", 91 | "# Solve K, and [R T] from m\n", 92 | "M = m.reshape(3, 4)\n", 93 | "A = M[:, :3]\n", 94 | "b = M[:, 3]\n", 95 | "a1, a2, a3 = map(lambda x: x.reshape(-1), np.vsplit(A, 3))\n", 96 | "\n", 97 | "rho_sq = 1 / np.dot(a3, a3) # How to decide the sign?\n", 98 | "cx = rho_sq * np.dot(a1, a3)\n", 99 | "cy = rho_sq * np.dot(a2, a3)\n", 100 | "t1 = np.cross(a2, a3)\n", 101 | "t2 = np.cross(a3, a1)\n", 102 | "t1_norm = np.linalg.norm(t1)\n", 103 | "t2_norm = np.linalg.norm(t2)\n", 104 | "cos_theta = np.dot(t1, t2) / (t1_norm * t2_norm)\n", 105 | "sin_theta = np.sqrt(1 - cos_theta ** 2)\n", 106 | "cot_theta = cos_theta / sin_theta\n", 107 | "\n", 108 | "alpha = rho_sq * t2_norm * sin_theta\n", 109 | "beta = rho_sq * t1_norm * sin_theta\n", 110 | "\n", 111 | "r1 = t1 / t1_norm\n", 112 | "r3 = a3 / np.linalg.norm(a3)\n", 113 | "r2 = np.cross(r3, r1)\n", 114 | "\n", 115 | "K = np.array([\n", 116 | " [alpha, -alpha * cot_theta, cx],\n", 117 | " [0, beta / sin_theta, cy],\n", 118 | " [0, 0, 1]\n", 119 | "])\n", 120 | "R = np.vstack((r1, r2, r3))\n", 121 | "T = np.sqrt(rho_sq) * np.linalg.inv(K) @ b\n", 122 | "# ------------TODO----------------" 123 | ] 124 | }, 125 | { 126 | "cell_type": "code", 127 | "execution_count": null, 128 | "metadata": {}, 129 | "outputs": [], 130 | "source": [ 131 | "# save results\n", 132 | "save_dict = {'3Dcoord': all_3Dcoord, 'P':P, 'm':m, 'K':K, 'R':R, 'T':T}\n", 133 | "np.save('../results/calibr', save_dict)\n", 134 | "\n", 135 | "# The reference answer of T should be [383001.8386981 -67357.81794783 67071.11338303]\n", 136 | "# The results are very strange because the data is fake." 137 | ] 138 | } 139 | ], 140 | "metadata": { 141 | "kernelspec": { 142 | "display_name": "Python 3", 143 | "language": "python", 144 | "name": "python3" 145 | }, 146 | "language_info": { 147 | "codemirror_mode": { 148 | "name": "ipython", 149 | "version": 3 150 | }, 151 | "file_extension": ".py", 152 | "mimetype": "text/x-python", 153 | "name": "python", 154 | "nbconvert_exporter": "python", 155 | "pygments_lexer": "ipython3", 156 | "version": "3.10.12" 157 | }, 158 | "orig_nbformat": 4 159 | }, 160 | "nbformat": 4, 161 | "nbformat_minor": 2 162 | } 163 | -------------------------------------------------------------------------------- /计算机视觉导论/HW3/camera_calibr/front.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CS-icez/pku-cs-homework/1040ca9fae63f9aea17dbf807ba1de8a1f668b92/计算机视觉导论/HW3/camera_calibr/front.png -------------------------------------------------------------------------------- /计算机视觉导论/HW3/camera_calibr/front_image.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CS-icez/pku-cs-homework/1040ca9fae63f9aea17dbf807ba1de8a1f668b92/计算机视觉导论/HW3/camera_calibr/front_image.npy -------------------------------------------------------------------------------- /计算机视觉导论/HW3/depth_pc/depth.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CS-icez/pku-cs-homework/1040ca9fae63f9aea17dbf807ba1de8a1f668b92/计算机视觉导论/HW3/depth_pc/depth.png -------------------------------------------------------------------------------- /计算机视觉导论/HW3/depth_pc/depth_pc.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import numpy as np\n", 10 | "import cv2" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": null, 16 | "metadata": {}, 17 | "outputs": [], 18 | "source": [ 19 | "# read depth image\n", 20 | "depth_scale = 0.00012498664727900177\n", 21 | "depth_img = cv2.imread('depth.png')\n", 22 | "dpt = depth_img[:, :, 2] + depth_img[:, :, 1] * 256\n", 23 | "dpt = dpt * depth_scale\n", 24 | "\n", 25 | "# read seg image\n", 26 | "seg = cv2.imread('seg.png')[...,0] # 255: fore ground, 0: background\n", 27 | "\n", 28 | "# read intrinsics and extrinsics\n", 29 | "K = np.load('intrinsic.npy')\n", 30 | "print(K)" 31 | ] 32 | }, 33 | { 34 | "cell_type": "code", 35 | "execution_count": null, 36 | "metadata": {}, 37 | "outputs": [], 38 | "source": [ 39 | "# task1: convert depth image to point cloud\n", 40 | "def depth2pc(depth, seg, K):\n", 41 | " # ------------TODO---------------\n", 42 | " # compute point cloud from depth image\n", 43 | " # for-loop is not allowed!!\n", 44 | " cx = K[0, 2]\n", 45 | " cy = K[1, 2]\n", 46 | " alpha = K[0, 0]\n", 47 | " beta = K[1, 1]\n", 48 | "\n", 49 | " u, v = np.meshgrid(np.arange(depth.shape[1]), np.arange(depth.shape[0]))\n", 50 | " pc = np.zeros((depth.shape[0], depth.shape[1], 3))\n", 51 | " pc[:, :, 0] = depth * (u - cx) / alpha\n", 52 | " pc[:, :, 1] = depth * (v - cy) / beta\n", 53 | " pc[:, :, 2] = depth\n", 54 | " pc = pc[seg > 0]\n", 55 | " # ------------TODO --------------\n", 56 | " return pc\n", 57 | "\n", 58 | "partial_pc = depth2pc(dpt, seg, K)\n", 59 | "\n", 60 | "# For debug and submission\n", 61 | "np.savetxt('../results/pc_from_depth.txt', partial_pc)" 62 | ] 63 | }, 64 | { 65 | "cell_type": "code", 66 | "execution_count": null, 67 | "metadata": {}, 68 | "outputs": [], 69 | "source": [ 70 | "# task2: compute one-way chamfer distance to the complete shape\n", 71 | "full_pc = np.loadtxt('aligned_full_pc.txt')\n", 72 | "\n", 73 | "def random_sample(pc, num):\n", 74 | " permu = np.random.permutation(pc.shape[0])\n", 75 | " return pc[permu][:num]\n", 76 | "\n", 77 | "partial_pc_sampled = random_sample(partial_pc, 2048)\n", 78 | "full_pc_sampled = random_sample(full_pc, 2048)\n", 79 | "\n", 80 | "# -----------TODO---------------\n", 81 | "# implement one way chamfer distance\n", 82 | "t = partial_pc_sampled[:, None, :] - full_pc_sampled[None, :, :]\n", 83 | "t = np.linalg.norm(t, axis=2)\n", 84 | "t = np.min(t, axis=1)\n", 85 | "one_way_CD = np.mean(t)[None]\n", 86 | "# -----------TODO---------------\n", 87 | "print('one way chamfer distance: ', one_way_CD)\n", 88 | "\n", 89 | "# For submission\n", 90 | "np.savetxt('../results/one_way_CD.txt', one_way_CD)" 91 | ] 92 | } 93 | ], 94 | "metadata": { 95 | "kernelspec": { 96 | "display_name": "Python 3", 97 | "language": "python", 98 | "name": "python3" 99 | }, 100 | "language_info": { 101 | "codemirror_mode": { 102 | "name": "ipython", 103 | "version": 3 104 | }, 105 | "file_extension": ".py", 106 | "mimetype": "text/x-python", 107 | "name": "python", 108 | "nbconvert_exporter": "python", 109 | "pygments_lexer": "ipython3", 110 | "version": "3.10.12" 111 | }, 112 | "orig_nbformat": 4 113 | }, 114 | "nbformat": 4, 115 | "nbformat_minor": 2 116 | } 117 | -------------------------------------------------------------------------------- /计算机视觉导论/HW3/depth_pc/intrinsic.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CS-icez/pku-cs-homework/1040ca9fae63f9aea17dbf807ba1de8a1f668b92/计算机视觉导论/HW3/depth_pc/intrinsic.npy -------------------------------------------------------------------------------- /计算机视觉导论/HW3/depth_pc/render.py: -------------------------------------------------------------------------------- 1 | # This code is used to render a depth image from a mesh. 2 | # This code is only provided for interested readers and you don't have to run this. 3 | 4 | import pyrender 5 | import trimesh 6 | import numpy as np 7 | import cv2 8 | 9 | # # if you are using a headless server, you may need below lines 10 | # import os 11 | # os.environ['PYOPENGL_PLATFORM'] = 'egl' 12 | 13 | # load object and preprocess 14 | mesh = trimesh.load('spot.obj', force='mesh') 15 | base_scale = np.sqrt(((mesh.vertices.max(axis=0)-mesh.vertices.min(axis=0))**2).sum()) 16 | obj_center = np.array(mesh.vertices.max(axis=0) + mesh.vertices.min(axis=0)) 17 | mesh.vertices = (mesh.vertices - obj_center) / base_scale 18 | mesh.vertices = mesh.vertices + [0.5,0.5,-1] 19 | np.savetxt('raw_full_pc.txt', np.array(mesh.vertices)) 20 | 21 | # pyrender load object 22 | scene = pyrender.Scene() 23 | obj_mesh = pyrender.Mesh.from_trimesh(mesh) 24 | obj_node = pyrender.Node(mesh=obj_mesh, matrix=np.eye(4)) 25 | scene.add_node(obj_node) 26 | 27 | # initialize camera 28 | pw = 640 29 | ph = 480 30 | camera_pose = np.eye(4) 31 | camera = pyrender.PerspectiveCamera(yfov=np.deg2rad(60), aspectRatio=pw / ph, znear=0.1, zfar=10) 32 | scene.add(camera, camera_pose) 33 | 34 | # render 35 | r = pyrender.OffscreenRenderer(pw, ph) 36 | seg_img, depth = r.render(scene, flags=pyrender.constants.RenderFlags.SEG, seg_node_map={obj_node:[255,0,0]}) 37 | 38 | depth_scale = 0.00012498664727900177 39 | print(depth.mean(), depth.max(), depth.min()) 40 | 41 | depth = (depth / depth_scale).astype(np.int32) 42 | depth_img = np.zeros_like(seg_img) 43 | depth_img[..., 1] = depth // 256 44 | depth_img[..., 2] = depth % 256 45 | 46 | cv2.imwrite('seg.png', seg_img) 47 | cv2.imwrite('depth.png', depth_img) 48 | 49 | 50 | # intrinsic 51 | projection = camera.get_projection_matrix() 52 | K = np.eye(3) 53 | K[0,0] = projection[0,0] * pw / 2 54 | K[1,1] = projection[1,1] * ph / 2 55 | K[0,2] = pw / 2 56 | K[1,2] = ph / 2 57 | np.save('intrinsic', K) -------------------------------------------------------------------------------- /计算机视觉导论/HW3/depth_pc/seg.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CS-icez/pku-cs-homework/1040ca9fae63f9aea17dbf807ba1de8a1f668b92/计算机视觉导论/HW3/depth_pc/seg.png -------------------------------------------------------------------------------- /计算机视觉导论/HW3/marching_cube/data/bob_cell.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CS-icez/pku-cs-homework/1040ca9fae63f9aea17dbf807ba1de8a1f668b92/计算机视觉导论/HW3/marching_cube/data/bob_cell.npy -------------------------------------------------------------------------------- /计算机视觉导论/HW3/marching_cube/data/spot_cell.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CS-icez/pku-cs-homework/1040ca9fae63f9aea17dbf807ba1de8a1f668b92/计算机视觉导论/HW3/marching_cube/data/spot_cell.npy -------------------------------------------------------------------------------- /计算机视觉导论/HW3/marching_cube/lookup_table.py: -------------------------------------------------------------------------------- 1 | # -----------------------Hint--------------------- 2 | # Assuming that we have the signed distance field "cells" of a 3D shape and we want to extract the iso-surface of "thre", 3 | # how to compute the triangles of a cell "(x,y,z)" ~ "(x+1,y+1,z+1)"? 4 | # 1. Use "getCaseNum(x,y,z,thre,cells)" to get a list of case values for this cell. 5 | # 2. For each case value "case_num", use "CaseNum2EdgeOffset[case_num]" to get an edge of this cell which interacts with the iso-surface. 6 | # To be more specific, one corner of this edge is "(x+CaseNum2EdgeOffset[case_num][0],y+CaseNum2EdgeOffset[case_num][1],z+CaseNum2EdgeOffset[case_num][2])" 7 | # and the other is "(x+CaseNum2EdgeOffset[case_num][3],y+CaseNum2EdgeOffset[case_num][4],z+CaseNum2EdgeOffset[case_num][5])" 8 | # 3. Use linear interpolation to find the interaction point "(xp,yp,zp)", which should satisfy "cells[xp,yp,zp]=thre", on the edge found in Step 2. 9 | # 4. Back to Step 2 and find the next interaction point. Use every three consecutive interaction points to form a face. 10 | # -----------------------Hint--------------------- 11 | 12 | # Build Marching Cubes Lookup table 13 | # This table is straight from Paul Bourke's site (Source: http://paulbourke.net/geometry/polygonise/) 14 | triTable =[ 15 | [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 16 | [0, 8, 3, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 17 | [0, 1, 9, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 18 | [1, 8, 3, 9, 8, 1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 19 | [1, 2, 10, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 20 | [0, 8, 3, 1, 2, 10, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 21 | [9, 2, 10, 0, 2, 9, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 22 | [2, 8, 3, 2, 10, 8, 10, 9, 8, -1, -1, -1, -1, -1, -1, -1], 23 | [3, 11, 2, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 24 | [0, 11, 2, 8, 11, 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 25 | [1, 9, 0, 2, 3, 11, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 26 | [1, 11, 2, 1, 9, 11, 9, 8, 11, -1, -1, -1, -1, -1, -1, -1], 27 | [3, 10, 1, 11, 10, 3, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 28 | [0, 10, 1, 0, 8, 10, 8, 11, 10, -1, -1, -1, -1, -1, -1, -1], 29 | [3, 9, 0, 3, 11, 9, 11, 10, 9, -1, -1, -1, -1, -1, -1, -1], 30 | [9, 8, 10, 10, 8, 11, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 31 | [4, 7, 8, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 32 | [4, 3, 0, 7, 3, 4, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 33 | [0, 1, 9, 8, 4, 7, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 34 | [4, 1, 9, 4, 7, 1, 7, 3, 1, -1, -1, -1, -1, -1, -1, -1], 35 | [1, 2, 10, 8, 4, 7, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 36 | [3, 4, 7, 3, 0, 4, 1, 2, 10, -1, -1, -1, -1, -1, -1, -1], 37 | [9, 2, 10, 9, 0, 2, 8, 4, 7, -1, -1, -1, -1, -1, -1, -1], 38 | [2, 10, 9, 2, 9, 7, 2, 7, 3, 7, 9, 4, -1, -1, -1, -1], 39 | [8, 4, 7, 3, 11, 2, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 40 | [11, 4, 7, 11, 2, 4, 2, 0, 4, -1, -1, -1, -1, -1, -1, -1], 41 | [9, 0, 1, 8, 4, 7, 2, 3, 11, -1, -1, -1, -1, -1, -1, -1], 42 | [4, 7, 11, 9, 4, 11, 9, 11, 2, 9, 2, 1, -1, -1, -1, -1], 43 | [3, 10, 1, 3, 11, 10, 7, 8, 4, -1, -1, -1, -1, -1, -1, -1], 44 | [1, 11, 10, 1, 4, 11, 1, 0, 4, 7, 11, 4, -1, -1, -1, -1], 45 | [4, 7, 8, 9, 0, 11, 9, 11, 10, 11, 0, 3, -1, -1, -1, -1], 46 | [4, 7, 11, 4, 11, 9, 9, 11, 10, -1, -1, -1, -1, -1, -1, -1], 47 | [9, 5, 4, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 48 | [9, 5, 4, 0, 8, 3, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 49 | [0, 5, 4, 1, 5, 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 50 | [8, 5, 4, 8, 3, 5, 3, 1, 5, -1, -1, -1, -1, -1, -1, -1], 51 | [1, 2, 10, 9, 5, 4, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 52 | [3, 0, 8, 1, 2, 10, 4, 9, 5, -1, -1, -1, -1, -1, -1, -1], 53 | [5, 2, 10, 5, 4, 2, 4, 0, 2, -1, -1, -1, -1, -1, -1, -1], 54 | [2, 10, 5, 3, 2, 5, 3, 5, 4, 3, 4, 8, -1, -1, -1, -1], 55 | [9, 5, 4, 2, 3, 11, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 56 | [0, 11, 2, 0, 8, 11, 4, 9, 5, -1, -1, -1, -1, -1, -1, -1], 57 | [0, 5, 4, 0, 1, 5, 2, 3, 11, -1, -1, -1, -1, -1, -1, -1], 58 | [2, 1, 5, 2, 5, 8, 2, 8, 11, 4, 8, 5, -1, -1, -1, -1], 59 | [10, 3, 11, 10, 1, 3, 9, 5, 4, -1, -1, -1, -1, -1, -1, -1], 60 | [4, 9, 5, 0, 8, 1, 8, 10, 1, 8, 11, 10, -1, -1, -1, -1], 61 | [5, 4, 0, 5, 0, 11, 5, 11, 10, 11, 0, 3, -1, -1, -1, -1], 62 | [5, 4, 8, 5, 8, 10, 10, 8, 11, -1, -1, -1, -1, -1, -1, -1], 63 | [9, 7, 8, 5, 7, 9, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 64 | [9, 3, 0, 9, 5, 3, 5, 7, 3, -1, -1, -1, -1, -1, -1, -1], 65 | [0, 7, 8, 0, 1, 7, 1, 5, 7, -1, -1, -1, -1, -1, -1, -1], 66 | [1, 5, 3, 3, 5, 7, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 67 | [9, 7, 8, 9, 5, 7, 10, 1, 2, -1, -1, -1, -1, -1, -1, -1], 68 | [10, 1, 2, 9, 5, 0, 5, 3, 0, 5, 7, 3, -1, -1, -1, -1], 69 | [8, 0, 2, 8, 2, 5, 8, 5, 7, 10, 5, 2, -1, -1, -1, -1], 70 | [2, 10, 5, 2, 5, 3, 3, 5, 7, -1, -1, -1, -1, -1, -1, -1], 71 | [7, 9, 5, 7, 8, 9, 3, 11, 2, -1, -1, -1, -1, -1, -1, -1], 72 | [9, 5, 7, 9, 7, 2, 9, 2, 0, 2, 7, 11, -1, -1, -1, -1], 73 | [2, 3, 11, 0, 1, 8, 1, 7, 8, 1, 5, 7, -1, -1, -1, -1], 74 | [11, 2, 1, 11, 1, 7, 7, 1, 5, -1, -1, -1, -1, -1, -1, -1], 75 | [9, 5, 8, 8, 5, 7, 10, 1, 3, 10, 3, 11, -1, -1, -1, -1], 76 | [5, 7, 0, 5, 0, 9, 7, 11, 0, 1, 0, 10, 11, 10, 0, -1], 77 | [11, 10, 0, 11, 0, 3, 10, 5, 0, 8, 0, 7, 5, 7, 0, -1], 78 | [11, 10, 5, 7, 11, 5, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 79 | [10, 6, 5, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 80 | [0, 8, 3, 5, 10, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 81 | [9, 0, 1, 5, 10, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 82 | [1, 8, 3, 1, 9, 8, 5, 10, 6, -1, -1, -1, -1, -1, -1, -1], 83 | [1, 6, 5, 2, 6, 1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 84 | [1, 6, 5, 1, 2, 6, 3, 0, 8, -1, -1, -1, -1, -1, -1, -1], 85 | [9, 6, 5, 9, 0, 6, 0, 2, 6, -1, -1, -1, -1, -1, -1, -1], 86 | [5, 9, 8, 5, 8, 2, 5, 2, 6, 3, 2, 8, -1, -1, -1, -1], 87 | [2, 3, 11, 10, 6, 5, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 88 | [11, 0, 8, 11, 2, 0, 10, 6, 5, -1, -1, -1, -1, -1, -1, -1], 89 | [0, 1, 9, 2, 3, 11, 5, 10, 6, -1, -1, -1, -1, -1, -1, -1], 90 | [5, 10, 6, 1, 9, 2, 9, 11, 2, 9, 8, 11, -1, -1, -1, -1], 91 | [6, 3, 11, 6, 5, 3, 5, 1, 3, -1, -1, -1, -1, -1, -1, -1], 92 | [0, 8, 11, 0, 11, 5, 0, 5, 1, 5, 11, 6, -1, -1, -1, -1], 93 | [3, 11, 6, 0, 3, 6, 0, 6, 5, 0, 5, 9, -1, -1, -1, -1], 94 | [6, 5, 9, 6, 9, 11, 11, 9, 8, -1, -1, -1, -1, -1, -1, -1], 95 | [5, 10, 6, 4, 7, 8, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 96 | [4, 3, 0, 4, 7, 3, 6, 5, 10, -1, -1, -1, -1, -1, -1, -1], 97 | [1, 9, 0, 5, 10, 6, 8, 4, 7, -1, -1, -1, -1, -1, -1, -1], 98 | [10, 6, 5, 1, 9, 7, 1, 7, 3, 7, 9, 4, -1, -1, -1, -1], 99 | [6, 1, 2, 6, 5, 1, 4, 7, 8, -1, -1, -1, -1, -1, -1, -1], 100 | [1, 2, 5, 5, 2, 6, 3, 0, 4, 3, 4, 7, -1, -1, -1, -1], 101 | [8, 4, 7, 9, 0, 5, 0, 6, 5, 0, 2, 6, -1, -1, -1, -1], 102 | [7, 3, 9, 7, 9, 4, 3, 2, 9, 5, 9, 6, 2, 6, 9, -1], 103 | [3, 11, 2, 7, 8, 4, 10, 6, 5, -1, -1, -1, -1, -1, -1, -1], 104 | [5, 10, 6, 4, 7, 2, 4, 2, 0, 2, 7, 11, -1, -1, -1, -1], 105 | [0, 1, 9, 4, 7, 8, 2, 3, 11, 5, 10, 6, -1, -1, -1, -1], 106 | [9, 2, 1, 9, 11, 2, 9, 4, 11, 7, 11, 4, 5, 10, 6, -1], 107 | [8, 4, 7, 3, 11, 5, 3, 5, 1, 5, 11, 6, -1, -1, -1, -1], 108 | [5, 1, 11, 5, 11, 6, 1, 0, 11, 7, 11, 4, 0, 4, 11, -1], 109 | [0, 5, 9, 0, 6, 5, 0, 3, 6, 11, 6, 3, 8, 4, 7, -1], 110 | [6, 5, 9, 6, 9, 11, 4, 7, 9, 7, 11, 9, -1, -1, -1, -1], 111 | [10, 4, 9, 6, 4, 10, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 112 | [4, 10, 6, 4, 9, 10, 0, 8, 3, -1, -1, -1, -1, -1, -1, -1], 113 | [10, 0, 1, 10, 6, 0, 6, 4, 0, -1, -1, -1, -1, -1, -1, -1], 114 | [8, 3, 1, 8, 1, 6, 8, 6, 4, 6, 1, 10, -1, -1, -1, -1], 115 | [1, 4, 9, 1, 2, 4, 2, 6, 4, -1, -1, -1, -1, -1, -1, -1], 116 | [3, 0, 8, 1, 2, 9, 2, 4, 9, 2, 6, 4, -1, -1, -1, -1], 117 | [0, 2, 4, 4, 2, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 118 | [8, 3, 2, 8, 2, 4, 4, 2, 6, -1, -1, -1, -1, -1, -1, -1], 119 | [10, 4, 9, 10, 6, 4, 11, 2, 3, -1, -1, -1, -1, -1, -1, -1], 120 | [0, 8, 2, 2, 8, 11, 4, 9, 10, 4, 10, 6, -1, -1, -1, -1], 121 | [3, 11, 2, 0, 1, 6, 0, 6, 4, 6, 1, 10, -1, -1, -1, -1], 122 | [6, 4, 1, 6, 1, 10, 4, 8, 1, 2, 1, 11, 8, 11, 1, -1], 123 | [9, 6, 4, 9, 3, 6, 9, 1, 3, 11, 6, 3, -1, -1, -1, -1], 124 | [8, 11, 1, 8, 1, 0, 11, 6, 1, 9, 1, 4, 6, 4, 1, -1], 125 | [3, 11, 6, 3, 6, 0, 0, 6, 4, -1, -1, -1, -1, -1, -1, -1], 126 | [6, 4, 8, 11, 6, 8, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 127 | [7, 10, 6, 7, 8, 10, 8, 9, 10, -1, -1, -1, -1, -1, -1, -1], 128 | [0, 7, 3, 0, 10, 7, 0, 9, 10, 6, 7, 10, -1, -1, -1, -1], 129 | [10, 6, 7, 1, 10, 7, 1, 7, 8, 1, 8, 0, -1, -1, -1, -1], 130 | [10, 6, 7, 10, 7, 1, 1, 7, 3, -1, -1, -1, -1, -1, -1, -1], 131 | [1, 2, 6, 1, 6, 8, 1, 8, 9, 8, 6, 7, -1, -1, -1, -1], 132 | [2, 6, 9, 2, 9, 1, 6, 7, 9, 0, 9, 3, 7, 3, 9, -1], 133 | [7, 8, 0, 7, 0, 6, 6, 0, 2, -1, -1, -1, -1, -1, -1, -1], 134 | [7, 3, 2, 6, 7, 2, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 135 | [2, 3, 11, 10, 6, 8, 10, 8, 9, 8, 6, 7, -1, -1, -1, -1], 136 | [2, 0, 7, 2, 7, 11, 0, 9, 7, 6, 7, 10, 9, 10, 7, -1], 137 | [1, 8, 0, 1, 7, 8, 1, 10, 7, 6, 7, 10, 2, 3, 11, -1], 138 | [11, 2, 1, 11, 1, 7, 10, 6, 1, 6, 7, 1, -1, -1, -1, -1], 139 | [8, 9, 6, 8, 6, 7, 9, 1, 6, 11, 6, 3, 1, 3, 6, -1], 140 | [0, 9, 1, 11, 6, 7, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 141 | [7, 8, 0, 7, 0, 6, 3, 11, 0, 11, 6, 0, -1, -1, -1, -1], 142 | [7, 11, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 143 | [7, 6, 11, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 144 | [3, 0, 8, 11, 7, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 145 | [0, 1, 9, 11, 7, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 146 | [8, 1, 9, 8, 3, 1, 11, 7, 6, -1, -1, -1, -1, -1, -1, -1], 147 | [10, 1, 2, 6, 11, 7, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 148 | [1, 2, 10, 3, 0, 8, 6, 11, 7, -1, -1, -1, -1, -1, -1, -1], 149 | [2, 9, 0, 2, 10, 9, 6, 11, 7, -1, -1, -1, -1, -1, -1, -1], 150 | [6, 11, 7, 2, 10, 3, 10, 8, 3, 10, 9, 8, -1, -1, -1, -1], 151 | [7, 2, 3, 6, 2, 7, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 152 | [7, 0, 8, 7, 6, 0, 6, 2, 0, -1, -1, -1, -1, -1, -1, -1], 153 | [2, 7, 6, 2, 3, 7, 0, 1, 9, -1, -1, -1, -1, -1, -1, -1], 154 | [1, 6, 2, 1, 8, 6, 1, 9, 8, 8, 7, 6, -1, -1, -1, -1], 155 | [10, 7, 6, 10, 1, 7, 1, 3, 7, -1, -1, -1, -1, -1, -1, -1], 156 | [10, 7, 6, 1, 7, 10, 1, 8, 7, 1, 0, 8, -1, -1, -1, -1], 157 | [0, 3, 7, 0, 7, 10, 0, 10, 9, 6, 10, 7, -1, -1, -1, -1], 158 | [7, 6, 10, 7, 10, 8, 8, 10, 9, -1, -1, -1, -1, -1, -1, -1], 159 | [6, 8, 4, 11, 8, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 160 | [3, 6, 11, 3, 0, 6, 0, 4, 6, -1, -1, -1, -1, -1, -1, -1], 161 | [8, 6, 11, 8, 4, 6, 9, 0, 1, -1, -1, -1, -1, -1, -1, -1], 162 | [9, 4, 6, 9, 6, 3, 9, 3, 1, 11, 3, 6, -1, -1, -1, -1], 163 | [6, 8, 4, 6, 11, 8, 2, 10, 1, -1, -1, -1, -1, -1, -1, -1], 164 | [1, 2, 10, 3, 0, 11, 0, 6, 11, 0, 4, 6, -1, -1, -1, -1], 165 | [4, 11, 8, 4, 6, 11, 0, 2, 9, 2, 10, 9, -1, -1, -1, -1], 166 | [10, 9, 3, 10, 3, 2, 9, 4, 3, 11, 3, 6, 4, 6, 3, -1], 167 | [8, 2, 3, 8, 4, 2, 4, 6, 2, -1, -1, -1, -1, -1, -1, -1], 168 | [0, 4, 2, 4, 6, 2, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 169 | [1, 9, 0, 2, 3, 4, 2, 4, 6, 4, 3, 8, -1, -1, -1, -1], 170 | [1, 9, 4, 1, 4, 2, 2, 4, 6, -1, -1, -1, -1, -1, -1, -1], 171 | [8, 1, 3, 8, 6, 1, 8, 4, 6, 6, 10, 1, -1, -1, -1, -1], 172 | [10, 1, 0, 10, 0, 6, 6, 0, 4, -1, -1, -1, -1, -1, -1, -1], 173 | [4, 6, 3, 4, 3, 8, 6, 10, 3, 0, 3, 9, 10, 9, 3, -1], 174 | [10, 9, 4, 6, 10, 4, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 175 | [4, 9, 5, 7, 6, 11, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 176 | [0, 8, 3, 4, 9, 5, 11, 7, 6, -1, -1, -1, -1, -1, -1, -1], 177 | [5, 0, 1, 5, 4, 0, 7, 6, 11, -1, -1, -1, -1, -1, -1, -1], 178 | [11, 7, 6, 8, 3, 4, 3, 5, 4, 3, 1, 5, -1, -1, -1, -1], 179 | [9, 5, 4, 10, 1, 2, 7, 6, 11, -1, -1, -1, -1, -1, -1, -1], 180 | [6, 11, 7, 1, 2, 10, 0, 8, 3, 4, 9, 5, -1, -1, -1, -1], 181 | [7, 6, 11, 5, 4, 10, 4, 2, 10, 4, 0, 2, -1, -1, -1, -1], 182 | [3, 4, 8, 3, 5, 4, 3, 2, 5, 10, 5, 2, 11, 7, 6, -1], 183 | [7, 2, 3, 7, 6, 2, 5, 4, 9, -1, -1, -1, -1, -1, -1, -1], 184 | [9, 5, 4, 0, 8, 6, 0, 6, 2, 6, 8, 7, -1, -1, -1, -1], 185 | [3, 6, 2, 3, 7, 6, 1, 5, 0, 5, 4, 0, -1, -1, -1, -1], 186 | [6, 2, 8, 6, 8, 7, 2, 1, 8, 4, 8, 5, 1, 5, 8, -1], 187 | [9, 5, 4, 10, 1, 6, 1, 7, 6, 1, 3, 7, -1, -1, -1, -1], 188 | [1, 6, 10, 1, 7, 6, 1, 0, 7, 8, 7, 0, 9, 5, 4, -1], 189 | [4, 0, 10, 4, 10, 5, 0, 3, 10, 6, 10, 7, 3, 7, 10, -1], 190 | [7, 6, 10, 7, 10, 8, 5, 4, 10, 4, 8, 10, -1, -1, -1, -1], 191 | [6, 9, 5, 6, 11, 9, 11, 8, 9, -1, -1, -1, -1, -1, -1, -1], 192 | [3, 6, 11, 0, 6, 3, 0, 5, 6, 0, 9, 5, -1, -1, -1, -1], 193 | [0, 11, 8, 0, 5, 11, 0, 1, 5, 5, 6, 11, -1, -1, -1, -1], 194 | [6, 11, 3, 6, 3, 5, 5, 3, 1, -1, -1, -1, -1, -1, -1, -1], 195 | [1, 2, 10, 9, 5, 11, 9, 11, 8, 11, 5, 6, -1, -1, -1, -1], 196 | [0, 11, 3, 0, 6, 11, 0, 9, 6, 5, 6, 9, 1, 2, 10, -1], 197 | [11, 8, 5, 11, 5, 6, 8, 0, 5, 10, 5, 2, 0, 2, 5, -1], 198 | [6, 11, 3, 6, 3, 5, 2, 10, 3, 10, 5, 3, -1, -1, -1, -1], 199 | [5, 8, 9, 5, 2, 8, 5, 6, 2, 3, 8, 2, -1, -1, -1, -1], 200 | [9, 5, 6, 9, 6, 0, 0, 6, 2, -1, -1, -1, -1, -1, -1, -1], 201 | [1, 5, 8, 1, 8, 0, 5, 6, 8, 3, 8, 2, 6, 2, 8, -1], 202 | [1, 5, 6, 2, 1, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 203 | [1, 3, 6, 1, 6, 10, 3, 8, 6, 5, 6, 9, 8, 9, 6, -1], 204 | [10, 1, 0, 10, 0, 6, 9, 5, 0, 5, 6, 0, -1, -1, -1, -1], 205 | [0, 3, 8, 5, 6, 10, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 206 | [10, 5, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 207 | [11, 5, 10, 7, 5, 11, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 208 | [11, 5, 10, 11, 7, 5, 8, 3, 0, -1, -1, -1, -1, -1, -1, -1], 209 | [5, 11, 7, 5, 10, 11, 1, 9, 0, -1, -1, -1, -1, -1, -1, -1], 210 | [10, 7, 5, 10, 11, 7, 9, 8, 1, 8, 3, 1, -1, -1, -1, -1], 211 | [11, 1, 2, 11, 7, 1, 7, 5, 1, -1, -1, -1, -1, -1, -1, -1], 212 | [0, 8, 3, 1, 2, 7, 1, 7, 5, 7, 2, 11, -1, -1, -1, -1], 213 | [9, 7, 5, 9, 2, 7, 9, 0, 2, 2, 11, 7, -1, -1, -1, -1], 214 | [7, 5, 2, 7, 2, 11, 5, 9, 2, 3, 2, 8, 9, 8, 2, -1], 215 | [2, 5, 10, 2, 3, 5, 3, 7, 5, -1, -1, -1, -1, -1, -1, -1], 216 | [8, 2, 0, 8, 5, 2, 8, 7, 5, 10, 2, 5, -1, -1, -1, -1], 217 | [9, 0, 1, 5, 10, 3, 5, 3, 7, 3, 10, 2, -1, -1, -1, -1], 218 | [9, 8, 2, 9, 2, 1, 8, 7, 2, 10, 2, 5, 7, 5, 2, -1], 219 | [1, 3, 5, 3, 7, 5, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 220 | [0, 8, 7, 0, 7, 1, 1, 7, 5, -1, -1, -1, -1, -1, -1, -1], 221 | [9, 0, 3, 9, 3, 5, 5, 3, 7, -1, -1, -1, -1, -1, -1, -1], 222 | [9, 8, 7, 5, 9, 7, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 223 | [5, 8, 4, 5, 10, 8, 10, 11, 8, -1, -1, -1, -1, -1, -1, -1], 224 | [5, 0, 4, 5, 11, 0, 5, 10, 11, 11, 3, 0, -1, -1, -1, -1], 225 | [0, 1, 9, 8, 4, 10, 8, 10, 11, 10, 4, 5, -1, -1, -1, -1], 226 | [10, 11, 4, 10, 4, 5, 11, 3, 4, 9, 4, 1, 3, 1, 4, -1], 227 | [2, 5, 1, 2, 8, 5, 2, 11, 8, 4, 5, 8, -1, -1, -1, -1], 228 | [0, 4, 11, 0, 11, 3, 4, 5, 11, 2, 11, 1, 5, 1, 11, -1], 229 | [0, 2, 5, 0, 5, 9, 2, 11, 5, 4, 5, 8, 11, 8, 5, -1], 230 | [9, 4, 5, 2, 11, 3, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 231 | [2, 5, 10, 3, 5, 2, 3, 4, 5, 3, 8, 4, -1, -1, -1, -1], 232 | [5, 10, 2, 5, 2, 4, 4, 2, 0, -1, -1, -1, -1, -1, -1, -1], 233 | [3, 10, 2, 3, 5, 10, 3, 8, 5, 4, 5, 8, 0, 1, 9, -1], 234 | [5, 10, 2, 5, 2, 4, 1, 9, 2, 9, 4, 2, -1, -1, -1, -1], 235 | [8, 4, 5, 8, 5, 3, 3, 5, 1, -1, -1, -1, -1, -1, -1, -1], 236 | [0, 4, 5, 1, 0, 5, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 237 | [8, 4, 5, 8, 5, 3, 9, 0, 5, 0, 3, 5, -1, -1, -1, -1], 238 | [9, 4, 5, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 239 | [4, 11, 7, 4, 9, 11, 9, 10, 11, -1, -1, -1, -1, -1, -1, -1], 240 | [0, 8, 3, 4, 9, 7, 9, 11, 7, 9, 10, 11, -1, -1, -1, -1], 241 | [1, 10, 11, 1, 11, 4, 1, 4, 0, 7, 4, 11, -1, -1, -1, -1], 242 | [3, 1, 4, 3, 4, 8, 1, 10, 4, 7, 4, 11, 10, 11, 4, -1], 243 | [4, 11, 7, 9, 11, 4, 9, 2, 11, 9, 1, 2, -1, -1, -1, -1], 244 | [9, 7, 4, 9, 11, 7, 9, 1, 11, 2, 11, 1, 0, 8, 3, -1], 245 | [11, 7, 4, 11, 4, 2, 2, 4, 0, -1, -1, -1, -1, -1, -1, -1], 246 | [11, 7, 4, 11, 4, 2, 8, 3, 4, 3, 2, 4, -1, -1, -1, -1], 247 | [2, 9, 10, 2, 7, 9, 2, 3, 7, 7, 4, 9, -1, -1, -1, -1], 248 | [9, 10, 7, 9, 7, 4, 10, 2, 7, 8, 7, 0, 2, 0, 7, -1], 249 | [3, 7, 10, 3, 10, 2, 7, 4, 10, 1, 10, 0, 4, 0, 10, -1], 250 | [1, 10, 2, 8, 7, 4, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 251 | [4, 9, 1, 4, 1, 7, 7, 1, 3, -1, -1, -1, -1, -1, -1, -1], 252 | [4, 9, 1, 4, 1, 7, 0, 8, 1, 8, 7, 1, -1, -1, -1, -1], 253 | [4, 0, 3, 7, 4, 3, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 254 | [4, 8, 7, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 255 | [9, 10, 8, 10, 11, 8, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 256 | [3, 0, 9, 3, 9, 11, 11, 9, 10, -1, -1, -1, -1, -1, -1, -1], 257 | [0, 1, 10, 0, 10, 8, 8, 10, 11, -1, -1, -1, -1, -1, -1, -1], 258 | [3, 1, 10, 11, 3, 10, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 259 | [1, 2, 11, 1, 11, 9, 9, 11, 8, -1, -1, -1, -1, -1, -1, -1], 260 | [3, 0, 9, 3, 9, 11, 1, 2, 9, 2, 11, 9, -1, -1, -1, -1], 261 | [0, 2, 11, 8, 0, 11, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 262 | [3, 2, 11, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 263 | [2, 3, 8, 2, 8, 10, 10, 8, 9, -1, -1, -1, -1, -1, -1, -1], 264 | [9, 10, 2, 0, 9, 2, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 265 | [2, 3, 8, 2, 8, 10, 0, 1, 8, 1, 10, 8, -1, -1, -1, -1], 266 | [1, 10, 2, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 267 | [1, 3, 8, 9, 1, 8, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 268 | [0, 9, 1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 269 | [0, 3, 8, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 270 | [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]] 271 | 272 | 273 | # look up Lookup table 274 | def getCaseNum(x,y,z, thres,cells): 275 | case = 0 276 | if (thres < cells[x,y+1,z+1]): 277 | case = 128 278 | if (thres < cells[x+1,y+1,z+1]): 279 | case = case + 64 280 | if (thres < cells[x+1,y,z+1]): 281 | case = case + 32 282 | if (thres < cells[x,y,z+1]): 283 | case = case + 16 284 | if (thres < cells[x,y+1,z]): 285 | case = case + 8 286 | if (thres < cells[x+1,y+1,z]): 287 | case = case + 4 288 | if (thres < cells[x+1,y,z]): 289 | case = case + 2 290 | if (thres < cells[x,y,z]): 291 | case = case + 1 292 | case_nums = triTable[case] 293 | return case_nums 294 | 295 | 296 | # Change the value of Lookup table to an edge 297 | CaseNum2EdgeOffset = [ 298 | [0,0,0, 1,0,0], 299 | [1,0,0, 1,1,0], 300 | [0,1,0, 1,1,0], 301 | [0,0,0, 0,1,0], 302 | [0,0,1, 1,0,1], 303 | [1,0,1, 1,1,1], 304 | [0,1,1, 1,1,1], 305 | [0,0,1, 0,1,1], 306 | [0,0,0, 0,0,1], 307 | [1,0,0, 1,0,1], 308 | [1,1,0, 1,1,1], 309 | [0,1,0, 0,1,1] 310 | ] 311 | -------------------------------------------------------------------------------- /计算机视觉导论/HW3/marching_cube/marching_cube.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import numpy as np\n", 10 | "from lookup_table import CaseNum2EdgeOffset, getCaseNum\n", 11 | "import trimesh\n", 12 | "import os\n", 13 | "import time" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": null, 19 | "metadata": {}, 20 | "outputs": [], 21 | "source": [ 22 | "def marching_cube(thres,cells):\n", 23 | " # vertices use dictionary to avoid duplicate axes\n", 24 | " vertex_array = {}\n", 25 | " face_array = []\n", 26 | " t1 = time.time()\n", 27 | " # -------------------TODO------------------ \n", 28 | " # compute vertices and faces\n", 29 | " # vertices: [N, 3]\n", 30 | " # faces: [M, 3], e.g. np.array([[0,1,2]]) means a triangle composed of vertices[0], vertices[1] and vertices[2]\n", 31 | " # for-loop is allowed to reduce difficulty\n", 32 | " for i in range(cells.shape[0] - 1):\n", 33 | " for j in range(cells.shape[1] - 1):\n", 34 | " for k in range(cells.shape[2] - 1):\n", 35 | " case_nums = getCaseNum(i, j, k, thres, cells)\n", 36 | " for l in range(0, len(case_nums) - 1, 3):\n", 37 | " if case_nums[l] == -1:\n", 38 | " break\n", 39 | " triangle = case_nums[l:l+3]\n", 40 | " \n", 41 | " def f(edge):\n", 42 | " edge = CaseNum2EdgeOffset[edge]\n", 43 | " p0 = np.array(edge[:3])\n", 44 | " p1 = np.array(edge[3:])\n", 45 | " v0 = cells[i + p0[0], j + p0[1], k + p0[2]]\n", 46 | " v1 = cells[i + p1[0], j + p1[1], k + p1[2]]\n", 47 | " p = np.array([i, j, k]) + p0 + (thres - v0) / (v1 - v0) * (p1 - p0)\n", 48 | " return tuple(np.round(p, 6))\n", 49 | " \n", 50 | " vertices = list(map(f, triangle))\n", 51 | " for v in vertices:\n", 52 | " vertex_array.setdefault(v, len(vertex_array))\n", 53 | " face_array.append([vertex_array[v] for v in vertices])\n", 54 | " # -------------------TODO------------------ \n", 55 | " t2 = time.time()\n", 56 | " print(\"\\nTime taken by algorithm\\n\"+'-'*40+\"\\n{} s\".format(t2-t1))\n", 57 | " vertex_array = list(vertex_array.keys())\n", 58 | " return np.array(vertex_array), np.array(face_array)" 59 | ] 60 | }, 61 | { 62 | "cell_type": "code", 63 | "execution_count": null, 64 | "metadata": {}, 65 | "outputs": [], 66 | "source": [ 67 | "# reconstruct these two animals\n", 68 | "shape_name_lst = ['spot', 'bob']\n", 69 | "for shape_name in shape_name_lst:\n", 70 | " data = np.load(os.path.join('data', shape_name + '_cell.npy'))\n", 71 | " verts, faces = marching_cube(0, data)\n", 72 | " print(verts.shape, faces.shape)\n", 73 | " mesh = trimesh.Trimesh(vertices=verts, faces=faces)\n", 74 | " mesh_txt = trimesh.exchange.obj.export_obj(mesh)\n", 75 | " with open(os.path.join('../results', shape_name + '.obj'),\"w\") as fp:\n", 76 | " fp.write(mesh_txt)" 77 | ] 78 | } 79 | ], 80 | "metadata": { 81 | "kernelspec": { 82 | "display_name": "Python 3", 83 | "language": "python", 84 | "name": "python3" 85 | }, 86 | "language_info": { 87 | "codemirror_mode": { 88 | "name": "ipython", 89 | "version": 3 90 | }, 91 | "file_extension": ".py", 92 | "mimetype": "text/x-python", 93 | "name": "python", 94 | "nbconvert_exporter": "python", 95 | "pygments_lexer": "ipython3", 96 | "version": "3.10.12" 97 | }, 98 | "orig_nbformat": 4 99 | }, 100 | "nbformat": 4, 101 | "nbformat_minor": 2 102 | } 103 | -------------------------------------------------------------------------------- /计算机视觉导论/HW3/mesh_pc/earthmover.py: -------------------------------------------------------------------------------- 1 | # https://github.com/j2kun/earthmover/blob/main/earthmover.py 2 | 3 | ''' 4 | A python implementation of the Earthmover distance metric. 5 | ''' 6 | 7 | import math 8 | 9 | from collections import Counter 10 | from collections import defaultdict 11 | from ortools.linear_solver import pywraplp 12 | 13 | 14 | def euclidean_distance(x, y): 15 | return math.sqrt(sum((a - b)**2 for (a, b) in zip(x, y))) 16 | 17 | 18 | def earthmover_distance(p1, p2): 19 | ''' 20 | Output the Earthmover distance between the two given points. 21 | 22 | Arguments: 23 | 24 | - p1: an iterable of hashable iterables of numbers (i.e., list of tuples) 25 | - p2: an iterable of hashable iterables of numbers (i.e., list of tuples) 26 | ''' 27 | dist1 = {x: float(count) / len(p1) for (x, count) in Counter(p1).items()} 28 | dist2 = {x: float(count) / len(p2) for (x, count) in Counter(p2).items()} 29 | solver = pywraplp.Solver('earthmover_distance', pywraplp.Solver.GLOP_LINEAR_PROGRAMMING) 30 | 31 | variables = dict() 32 | 33 | # for each pile in dist1, the constraint that says all the dirt must leave this pile 34 | dirt_leaving_constraints = defaultdict(lambda: 0) 35 | 36 | # for each hole in dist2, the constraint that says this hole must be filled 37 | dirt_filling_constraints = defaultdict(lambda: 0) 38 | 39 | # the objective 40 | objective = solver.Objective() 41 | objective.SetMinimization() 42 | 43 | for (x, dirt_at_x) in dist1.items(): 44 | for (y, capacity_of_y) in dist2.items(): 45 | amount_to_move_x_y = solver.NumVar(0, solver.infinity(), 'z_{%s, %s}' % (x, y)) 46 | variables[(x, y)] = amount_to_move_x_y 47 | dirt_leaving_constraints[x] += amount_to_move_x_y 48 | dirt_filling_constraints[y] += amount_to_move_x_y 49 | objective.SetCoefficient(amount_to_move_x_y, euclidean_distance(x, y)) 50 | 51 | for x, linear_combination in dirt_leaving_constraints.items(): 52 | solver.Add(linear_combination == dist1[x]) 53 | 54 | for y, linear_combination in dirt_filling_constraints.items(): 55 | solver.Add(linear_combination == dist2[y]) 56 | 57 | status = solver.Solve() 58 | if status not in [solver.OPTIMAL, solver.FEASIBLE]: 59 | raise Exception('Unable to find feasible solution') 60 | 61 | for ((x, y), variable) in variables.items(): 62 | if variable.solution_value() != 0: 63 | cost = euclidean_distance(x, y) * variable.solution_value() 64 | print("move {} dirt from {} to {} for a cost of {}".format( 65 | variable.solution_value(), x, y, cost)) 66 | 67 | return objective.Value() 68 | 69 | 70 | if __name__ == "__main__": 71 | p1 = [ 72 | (0, 0), 73 | (0, 1), 74 | (0, -1), 75 | (1, 0), 76 | (-1, 0), 77 | ] 78 | 79 | p2 = [ 80 | (0, 0), 81 | (0, 2), 82 | (0, -2), 83 | (2, 0), 84 | (-2, 0), 85 | ] 86 | 87 | print(earthmover_distance(p1, p2)) -------------------------------------------------------------------------------- /计算机视觉导论/HW3/mesh_pc/fps_vis.txt: -------------------------------------------------------------------------------- 1 | 4.011833494028976332e+01 3.290412941850384954e+01 2.534337591013203905e+01 2 | 7.109170477117532805e+00 3.636440134198253560e+01 3.824841383912857395e+01 3 | 2.486235063378076049e+01 8.204212594109620227e+00 4.028084273003798899e+01 4 | 5.215221653972227500e+01 1.005386626135586603e+01 3.601361454539500784e+01 5 | 1.989741921651644319e+01 4.929038686532997104e+01 1.886370891940553207e+01 6 | 2.350852729303023381e+01 1.861257917553535179e+01 2.132502578154735318e+01 7 | 2.812937812538977767e+01 2.884225124309305599e+01 4.138327704224578696e+01 8 | 1.815929067114952744e+01 5.375231399385046416e+01 3.807642346993973348e+01 9 | 4.608712860309903192e+01 2.669111647633819473e+01 4.126355530246952696e+01 10 | 4.513589492597331798e+01 1.667957489884884481e+01 2.178497752312249958e+01 11 | 1.556381995908371252e+01 3.350661041070883073e+01 2.358818703451268561e+01 12 | 2.749375984929144323e+01 4.126444922669232795e+01 3.126663793365019828e+01 13 | 5.412852505049659868e+01 2.570554377310060801e+01 2.925510617314055750e+01 14 | 3.810030093269799778e+01 1.279967192253480590e+01 3.426525606819208036e+01 15 | 2.808077672792347812e+01 7.677586833631472096e+00 2.699058685663384693e+01 16 | 1.915869855596923799e+01 2.486179563516663293e+01 3.247603110264380888e+01 17 | 1.059195745026367064e+01 4.477054984547297067e+01 2.652012097622541376e+01 18 | 2.803980263719321897e+01 3.119056675531310319e+01 2.260150860097406067e+01 19 | 2.007368575178706394e+01 3.879227629250535614e+01 4.002622610006856263e+01 20 | 3.745668287136004437e+01 3.413497005930207706e+01 3.698557946118288697e+01 21 | 3.111413978204723918e+01 1.753287201313009547e+01 4.212077352389094642e+01 22 | 3.546983123668935178e+01 2.197825075635863357e+01 2.043311347659138377e+01 23 | 4.977243093380950256e+01 7.480712406636273570e+00 2.506496677611673718e+01 24 | 4.737674007307561652e+01 3.394967971694121900e+01 3.323336193162826646e+01 25 | 1.768951612437345844e+01 5.439548508210174305e+01 2.771740807110093030e+01 26 | 4.561111229106690956e+01 1.593806112676274722e+01 4.126708187179118426e+01 27 | 1.171766521685863083e+01 4.583447242629151219e+01 3.807350422866919359e+01 28 | 2.504900495274677752e+01 1.662873409172924610e+01 3.177261778816833271e+01 29 | 4.690729290686621766e+01 2.634901408122698285e+01 2.176609425135061215e+01 30 | 6.935013899601086607e+00 3.459695074540121595e+01 2.814236314589308563e+01 31 | 2.119948675168858543e+01 4.106589148591054794e+01 2.338126537751555745e+01 32 | 3.674227173507995303e+01 2.492995025111369500e+01 4.236278568037083403e+01 33 | 5.236933741873750847e+01 2.067551514480049235e+01 3.711268092586902156e+01 34 | 2.228241688764281037e+01 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3.096094060125465219e+01 432 | 1.236267763513679974e+01 4.570000231448063488e+01 2.428861358653551861e+01 433 | 1.878115612466769235e+01 3.272909742794468713e+01 3.716591075596443261e+01 434 | 2.471250983293530723e+01 2.140273885194969949e+01 4.201691800336050164e+01 435 | 3.297442813026548691e+01 1.474392401673529207e+01 3.560047589326698159e+01 436 | 4.551335607102162584e+01 1.444805009090985415e+01 2.995299394983599939e+01 437 | 3.166693175000037641e+01 1.383023901046968618e+01 3.732370702149305686e+01 438 | 2.627549491810977500e+01 1.453417187817683853e+01 4.280990802662607564e+01 439 | 1.165020240402665230e+01 3.278036899699826989e+01 2.339053423979378721e+01 440 | 2.479655806391646422e+01 2.055552820344411913e+01 4.219162625989056892e+01 441 | 4.971646746015075990e+01 1.777582063687187741e+01 2.185779300887194765e+01 442 | 8.913876519275568455e+00 3.194880546076088024e+01 3.192418930815460953e+01 443 | 4.726538846782708703e+01 7.152533900524229438e+00 2.603214306139453527e+01 444 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1.293556358916108451e+01 2.813648566648208060e+01 457 | 5.231781827768293880e+01 1.292316735217623602e+01 2.720943924563179905e+01 458 | 1.817841615278019418e+01 5.449238022761814193e+01 3.550947947283102479e+01 459 | 3.727045478938524781e+01 1.579566293983025993e+01 4.000082563624217613e+01 460 | 2.777872267848536580e+01 3.584785006047635392e+01 3.702816321693786961e+01 461 | 5.227178530723112004e+01 1.050918778317297608e+01 2.531982794286329863e+01 462 | 3.355800537145297824e+01 1.415561368041958801e+01 2.864319008535522926e+01 463 | 2.361969099489881785e+01 2.581907597525092868e+01 4.048231636213854046e+01 464 | 1.571278621272304399e+01 3.904214366078623044e+01 4.082877183635737595e+01 465 | 4.549736472041035995e+01 2.385725482394465757e+01 2.130143292020091295e+01 466 | 1.583000479489654744e+01 4.808247016695059273e+01 4.083537485935420364e+01 467 | 2.204199635740235408e+01 1.372942929229114384e+01 2.438139573691434236e+01 468 | 3.535273278709871647e+01 2.306385595556448109e+01 4.264136148849920005e+01 469 | 2.575642481126065775e+01 2.105073522576471845e+01 4.242102094293394998e+01 470 | 4.611692758109553836e+01 2.973728169515046460e+01 3.977618729311626566e+01 471 | 3.670359478683369048e+01 2.126285199823503547e+01 4.242879158048715027e+01 472 | 3.202544181975314075e+01 1.472561258752159219e+01 2.389505357486874360e+01 473 | 2.675263238534044419e+01 1.231416651051046252e+01 4.259415154060016562e+01 474 | 4.415274369619302774e+01 1.241538060168321422e+01 2.735068275403272153e+01 475 | 5.457748474683057083e+01 2.707867672706488094e+01 3.293145902801690283e+01 476 | 2.284494440285235939e+01 1.265065696115560812e+01 3.597277858467578682e+01 477 | 2.096278559161557453e+01 4.502257182641581323e+01 4.308601089651984495e+01 478 | 3.852918244695209182e+01 1.327021256620581369e+01 2.768260089893805187e+01 479 | 2.245711068493859841e+01 2.508379037747103979e+01 2.347418645239918078e+01 480 | 2.534751505174785535e+01 4.024137241690039701e+01 2.555356815605880172e+01 481 | 4.932173360634077142e+01 1.641755873556688172e+01 3.133671814582609372e+01 482 | 2.996037724916445200e+01 1.235405594932044870e+01 4.157902123592330668e+01 483 | 2.017425992413490476e+01 3.647205713142231076e+01 3.942974880121123249e+01 484 | 4.464801751611302194e+01 9.609822691630601099e+00 3.562939314134039392e+01 485 | 2.732866849571038514e+01 2.359389760940165104e+01 4.241707549405445121e+01 486 | 2.501292948224122625e+01 2.598836201443219807e+01 4.134005911215793816e+01 487 | 2.256133055053497571e+01 1.042075132013681227e+01 2.389722038776526603e+01 488 | 2.324095721980225804e+01 3.743454335121910503e+01 3.816170478862598969e+01 489 | 4.681976025265205266e+01 3.021647398286063435e+01 3.923097326703151566e+01 490 | 3.362266951430872552e+01 3.610554965101169955e+01 3.667832080248009419e+01 491 | 3.094855244886993972e+01 1.194281929502509065e+01 2.263925297555747207e+01 492 | 4.637793606441758243e+01 8.886652369245377159e+00 2.861082968288282657e+01 493 | 5.366484869161779159e+01 2.254345320357126425e+01 3.245456565795196724e+01 494 | 4.338977535379629558e+01 2.961000753737638647e+01 2.274593049244221277e+01 495 | 1.802243288544189070e+01 4.993949326670453814e+01 2.094399816124499836e+01 496 | 2.779839776696424636e+01 1.331409464150400801e+01 2.042753692731683302e+01 497 | 2.159221569681791664e+01 2.151078630771409905e+01 3.802110302109306872e+01 498 | 1.208160360634593644e+01 3.806481743511194082e+01 2.203478163976712523e+01 499 | 1.168316368607479738e+01 4.552966919100003196e+01 3.816075173958227396e+01 500 | 1.016881198188106872e+01 4.386273402485616657e+01 2.698445457175754569e+01 501 | 9.651375416535586282e+00 4.284222788918159353e+01 3.494458869685072244e+01 502 | 4.886420928322049662e+01 2.850867868296339935e+01 3.955605460308105137e+01 503 | 2.269081014047188205e+01 9.727095383401948681e+00 3.904491830712186839e+01 504 | 1.951601803356619769e+01 4.704742566497522915e+01 4.489647227004693519e+01 505 | 3.513964490872814395e+01 3.467564645947101809e+01 2.519484068386460862e+01 506 | 1.196643913257915770e+01 4.083316198143199216e+01 3.996938567863585234e+01 507 | 2.214526683254928230e+01 2.718508010732417191e+01 3.849032939164171552e+01 508 | 2.168024336583998135e+01 4.703775554850142271e+01 3.837109692879774059e+01 509 | 1.412898770728669007e+01 3.341600508363703170e+01 2.313749638137290532e+01 510 | 1.759827785489469321e+01 3.439183043926206551e+01 3.951127254488831397e+01 511 | 2.211003565822789341e+01 1.516091316933289157e+01 2.329826368953102289e+01 512 | 1.938283460858642826e+01 3.641258177145180497e+01 3.977674844959634015e+01 513 | -------------------------------------------------------------------------------- /计算机视觉导论/HW3/pack.py: -------------------------------------------------------------------------------- 1 | import os 2 | import zipfile 3 | 4 | 5 | def zipHW3(input_path: str, output_path: str, zip_name: str): 6 | zip = zipfile.ZipFile(output_path, "w", zipfile.ZIP_DEFLATED) 7 | for path, dirnames, filenames in os.walk(os.path.join(input_path, "camera_calibr")): 8 | fpath = path.replace(input_path, f'HW3_{zip_name}') 9 | for filename in filenames: 10 | if filename in ["calibr.ipynb"]: 11 | zip.write(os.path.join(path, filename), os.path.join(fpath, filename)) 12 | for path, dirnames, filenames in os.walk(os.path.join(input_path, "depth_pc")): 13 | fpath = path.replace(input_path, f'HW3_{zip_name}') 14 | for filename in filenames: 15 | if filename in ["depth_pc.ipynb"]: 16 | zip.write(os.path.join(path, filename), os.path.join(fpath, filename)) 17 | for path, dirnames, filenames in os.walk(os.path.join(input_path, "marching_cube")): 18 | fpath = path.replace(input_path, f'HW3_{zip_name}') 19 | for filename in filenames: 20 | if filename in ["marching_cube.ipynb"]: 21 | zip.write(os.path.join(path, filename), os.path.join(fpath, filename)) 22 | for path, dirnames, filenames in os.walk(os.path.join(input_path, "mesh_pc")): 23 | fpath = path.replace(input_path, f'HW3_{zip_name}') 24 | for filename in filenames: 25 | if filename in ["mesh_pc.ipynb"]: 26 | zip.write(os.path.join(path, filename), os.path.join(fpath, filename)) 27 | for path, dirnames, filenames in os.walk(os.path.join(input_path, "results")): 28 | fpath = path.replace(input_path, f'HW3_{zip_name}') 29 | for filename in filenames: 30 | zip.write(os.path.join(path, filename), os.path.join(fpath, filename)) 31 | zip.close() 32 | 33 | 34 | if __name__ == "__main__": 35 | 36 | # --------------------------------------------------------- 37 | # 请用你的学号和姓名替换下面的内容，注意参照例子的格式，使用拼音而非中文 38 | id = 1900012950 39 | name = 'ZhangSan' 40 | # --------------------------------------------------------- 41 | 42 | zip_name = f'{id}_{name}.zip' 43 | input_path = os.path.dirname(os.path.abspath(__file__)) 44 | output_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), zip_name) 45 | 46 | zipHW3(input_path, output_path, zip_name.split(".")[0]) 47 | 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