GitHub开源的AI下五子棋（基于博弈树极大极小值alpha-beta剪枝搜索）

最近看到个两年前的AI案例，使用博弈树搜索算法实现AI下五子棋，什么是博弈树搜索呢？博弈就是相互采取最优策略斗争的意思。比如说下五子棋，你下一步，我下一步，这就是相互博弈。假设棋盘的大小是10*10,那就是100个点可以下， 那么第一步可选择的可能就是100， 假设是下在了A点， 那么第二步就有除了A点的剩下的99个点的可能。 假设下在了B点， 那么第二步就有除了B点的剩下的99个点的可能，假设下在了C点......

项目运行效果如下：

在GitHub中这位大神进行了详细的介绍说明，参见： https://github.com/colingogogo/gobang_AI#gobang_ai

用深度神经网络搭建马赛克神器，高清无码效果感人

=目录

1、项目背景

2、适用范围

3、使用方法

---

1、项目背景

相信一提起马赛克这个东西，不少小伙伴都痛心疾首，虽然最近几年也频繁传出有在研发去除马赛克的软件，一直没有成品问世。不过最近一位程序员及经过不断努力终于完成了这款软件。

据悉这位程序员“deeppomf”用深度神经网络开发出了一个能抹去马赛克让原图重现的神奇程序：DeepCreamPy 。为了使这款软件达到更好的效果，作者在短短几个月内收集了超过10万张未打码的原图，但其中95%的图片他都没有仔细看过，只因为太过于浪费时间了。软件被上传分享后，在一周内被下载了500多次。不过目前该软件的局限性还很大，只能完成一些简单的修复。

该项目使用深度完全卷积神经网络（deep fully convolutional neural network），参照了英伟达在今年4月前发布的一篇论文。当然，英伟达原文的目的可不是用来做羞羞的事情，而是为了复原画面被单色条带遮挡的问题。

从实际效果来看，复原后的图片涂抹痕迹仍然比较明显，不过处理线条比较简单的漫画可以说是绰绰有余。 

2、适用范围

DeepCreamPy仅适用于薄码，如果马赛克太大太厚，去码可能会失效。另外，它对真人图片无效。如果你非要尝试，可以看一下强行使用的效果：

而且DeepCreamPy目前的版本还不能完全自动处理图片，需要用Photoshop首先对马赛克部分进行手动预处理。 

3、使用方法

第一步：安装程序

1、如果你是64位Windows用户，恭喜你可以直接下载exe程序

下载地址：https://github.com/deeppomf/DeepCreamPy/releases/latest

2、否则需要自己编译，编译代码需要一下组件：

• Python 3.6
• TensorFlow 1.10
• Keras 2.2.4
• Pillow
• h5py

请注意软件版本，Windows上的TensorFlow不兼容Python 2，也不兼容Python 3.7。

代码如下：

1. import numpy as np
2. from PIL import Image
3. import os
5. from copy import deepcopy
7. import config
8. from libs.pconv_hybrid_model import PConvUnet
9. from libs.utils import *
11. class Decensor:
13. def __init__(self):
14. self.args = config.get_args()
15. self.is_mosaic = self.args.is_mosaic
17. self.mask_color = [self.args.mask_color_red/255.0, self.args.
18. mask_color_green/255.0, self.args.mask_color_blue/255.0]
20. if not os.path.exists(self.args.decensor_output_path):
21. os.makedirs(self.args.decensor_output_path)
23. self.load_model()
25. def get_mask(self, colored):
26. mask = np.ones(colored.shape, np.uint8)
27. i, j = np.where(np.all(colored[0] == self.mask_color, axis=-1))
28. mask[0, i, j] = 0
29. return mask
31. def load_model(self):
32. self.model = PConvUnet()
33. self.model.load(
34. r"./models/model.h5",
35. train_bn=False,
36. lr=0.00005
37. )
39. def decensor_all_images_in_folder(self):
40. #load model once at beginning and reuse same model
41. #self.load_model()
42. color_dir = self.args.decensor_input_path
43. file_names = os.listdir(color_dir)
45. #convert all images into np arrays and put them in a list
46. for file_name in file_names:
47. color_file_path = os.path.join(color_dir, file_name)
48. color_bn, color_ext = os.path.splitext(file_name)
49. if os.path.isfile(color_file_path) and color_ext.casefold() == ".png":
50. print("-----------------------------------------------------------
51. ---------------")
52. print("Decensoring the image {}".format(color_file_path))
53. colored_img = Image.open(color_file_path)
54. #if we are doing a mosaic decensor
55. if self.is_mosaic:
56. #get the original file that hasn't been colored
57. ori_dir = self.args.decensor_input_original_path
58. #since the original image might not be a png, test multiple file formats
59. valid_formats = {".png", ".jpg", ".jpeg"}
60. for test_file_name in os.listdir(ori_dir):
61. test_bn, test_ext = os.path.splitext(test_file_name)
62. if (test_bn == color_bn) and (test_ext.casefold() in valid_formats):
63. ori_file_path = os.path.join(ori_dir, test_file_name)
64. ori_img = Image.open(ori_file_path)
65. # colored_img.show()
66. self.decensor_image(ori_img, colored_img, file_name)
67. break
68. else: #for...else, i.e if the loop finished without encountering break
69. print("Corresponding original, uncolored image not found in {}.
70. ".format(ori_file_path))
71. print("Check if it exists and is in the PNG or JPG format.")
72. else:
73. self.decensor_image(colored_img, colored_img, file_name)
74. print("--------------------------------------------------------------------------")
76. #decensors one image at a time
77. #TODO: decensor all cropped parts of the same image in a batch (then i need
78. input for colored an array of those images and make additional changes)
79. def decensor_image(self, ori, colored, file_name):
80. width, height = ori.size
81. #save the alpha channel if the image has an alpha channel
82. has_alpha = False
83. if (ori.mode == "RGBA"):
84. has_alpha = True
85. alpha_channel = np.asarray(ori)[:,:,3]
86. alpha_channel = np.expand_dims(alpha_channel, axis =-1)
87. ori = ori.convert('RGB')
89. ori_array = image_to_array(ori)
90. ori_array = np.expand_dims(ori_array, axis = 0)
92. if self.is_mosaic:
93. #if mosaic decensor, mask is empty
94. # mask = np.ones(ori_array.shape, np.uint8)
95. # print(mask.shape)
96. colored = colored.convert('RGB')
97. color_array = image_to_array(colored)
98. color_array = np.expand_dims(color_array, axis = 0)
99. mask = self.get_mask(color_array)
100. # mask_reshaped = mask[0,:,:,:] * 255.0
101. # mask_img = Image.fromarray(mask_reshaped.astype('uint8'))
102. # mask_img.show()
104. else:
105. mask = self.get_mask(ori_array)
107. #colored image is only used for finding the regions
108. regions = find_regions(colored.convert('RGB'))
109. print("Found {region_count} censored regions in this image!".format
110. (region_count = len(regions)))
112. if len(regions) == 0 and not self.is_mosaic:
113. print("No green regions detected!")
114. return
116. output_img_array = ori_array[0].copy()
118. for region_counter, region in enumerate(regions, 1):
119. bounding_box = expand_bounding(ori, region)
120. crop_img = ori.crop(bounding_box)
121. # crop_img.show()
122. #convert mask back to image
123. mask_reshaped = mask[0,:,:,:] * 255.0
124. mask_img = Image.fromarray(mask_reshaped.astype('uint8'))
125. #resize the cropped images
126. crop_img = crop_img.resize((512, 512))
127. crop_img_array = image_to_array(crop_img)
128. crop_img_array = np.expand_dims(crop_img_array, axis = 0)
129. #resize the mask images
130. mask_img = mask_img.crop(bounding_box)
131. mask_img = mask_img.resize((512, 512))
132. # mask_img.show()
133. #convert mask_img back to array
134. mask_array = image_to_array(mask_img)
135. #the mask has been upscaled so there will be values not equal to 0 or 1
137. mask_array[mask_array > 0] = 1
139. if self.is_mosaic:
140. a, b = np.where(np.all(mask_array == 0, axis = -1))
141. print(a, b)
142. coords = [coord for coord in zip(a,b) if ((coord[0] + coord[1]) % 2
143. == 0)]
144. a,b = zip(*coords)
146. mask_array[a,b] = 1
147. # mask_array = mask_array * 255.0
148. # img = Image.fromarray(mask_array.astype('uint8'))
149. # img.show()
150. # return
152. mask_array = np.expand_dims(mask_array, axis = 0)
154. # Run predictions for this batch of images
155. pred_img_array = self.model.predict([crop_img_array, mask_array, mask_array])
157. pred_img_array = pred_img_array * 255.0
158. pred_img_array = np.squeeze(pred_img_array, axis = 0)
160. #scale prediction image back to original size
161. bounding_width = bounding_box[2]-bounding_box[0]
162. bounding_height = bounding_box[3]-bounding_box[1]
163. #convert np array to image
165. # print(bounding_width,bounding_height)
166. # print(pred_img_array.shape)
168. pred_img = Image.fromarray(pred_img_array.astype('uint8'))
169. # pred_img.show()
170. pred_img = pred_img.resize((bounding_width, bounding_height),
171. resample = Image.BICUBIC)
173. pred_img_array = image_to_array(pred_img)
175. # print(pred_img_array.shape)
176. pred_img_array = np.expand_dims(pred_img_array, axis = 0)
178. # copy the decensored regions into the output image
179. for i in range(len(ori_array)):
180. for col in range(bounding_width):
181. for row in range(bounding_height):
182. bounding_width_index = col + bounding_box[0]
183. bounding_height_index = row + bounding_box[1]
184. if (bounding_width_index, bounding_height_index) in region:
185. output_img_array[bounding_height_index][bounding_
186. width_index] = pred_img_array[i,:,:,:][row][col]
187. print("{region_counter} out of {region_count} regions decensored."
188. .format(region_counter=region_counter, region_count=len(regions)))
190. output_img_array = output_img_array * 255.0
192. #restore the alpha channel if the image had one
193. if has_alpha:
194. output_img_array = np.concatenate((output_img_array, alpha_channel),
195. axis = 2)
197. output_img = Image.fromarray(output_img_array.astype('uint8'))
199. #save the decensored image
200. #file_name, _ = os.path.splitext(file_name)
201. save_path = os.path.join(self.args.decensor_output_path, file_name)
202. output_img.save(save_path)
204. print("Decensored image saved to {save_path}!".format(save_path=save_path))
205. return
207. if __name__ == '__main__':
208. decensor = Decensor()
209. decensor.decensor_all_images_in_folder()

注意：运行Demo需要下载模型，这里为了方便小伙伴，我已经下载完毕：用深度神经网络搭建马赛克神器，高清无码效果感人-深度学习工具类资源-CSDN下载

第二步：手动处理黑条遮挡和马赛克遮挡

。。。。。。。。。。。。。。。。。

版权原因，完整文章，请参考如下：

用深度神经网络搭建马赛克神器，高清无码效果感人