├── .idea ├── Bishe.iml ├── inspectionProfiles │ └── Project_Default.xml ├── misc.xml ├── modules.xml └── vcs.xml ├── BusterNetCore.py ├── README.md ├── __pycache__ ├── BusterNetCore.cpython-37.pyc ├── UIloadingpicture.cpython-37.pyc ├── UIlogin.cpython-37.pyc ├── cropimagecir.cpython-37.pyc ├── main.cpython-37.pyc ├── main_ui.cpython-37.pyc ├── model.cpython-37.pyc └── pasteimage.cpython-37.pyc ├── cropimagecir.py ├── fileaddress.txt ├── main.py ├── main_ui.py ├── main_ui.ui ├── masktest.png ├── model.py ├── modelUI.py ├── morepictures.py ├── pasteimage.py ├── rectangle_result.png └── test.png /.idea/Bishe.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 14 | -------------------------------------------------------------------------------- /.idea/inspectionProfiles/Project_Default.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 12 | -------------------------------------------------------------------------------- /.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 6 | 7 | -------------------------------------------------------------------------------- /.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /.idea/vcs.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | -------------------------------------------------------------------------------- /BusterNetCore.py: -------------------------------------------------------------------------------- 1 | """ 2 | This file defines all BusterNet related custom layers 3 | """ 4 | from __future__ import print_function 5 | from keras.layers import Conv2D, MaxPooling2D 6 | from keras.layers import Layer, Input, Lambda 7 | from keras.layers import BatchNormalization, Activation, Concatenate 8 | from keras.models import Model 9 | from keras.applications.vgg16 import preprocess_input 10 | from keras import backend as K 11 | import tensorflow as tf 12 | 13 | def std_norm_along_chs(x) : 14 | '''Data normalization along the channle axis 15 | Input: 16 | x = tensor4d, (n_samples, n_rows, n_cols, n_feats) 17 | Output: 18 | xn = tensor4d, same shape as x, normalized version of x 19 | ''' 20 | avg = K.mean(x, axis=-1, keepdims=True) 21 | std = K.maximum(1e-4, K.std(x, axis=-1, keepdims=True)) 22 | return (x - avg) / std 23 | 24 | def BnInception(x, nb_inc=16, inc_filt_list=[(1,1), (3,3), (5,5)], name='uinc') : 25 | '''Basic Google inception module with batch normalization 26 | Input: 27 | x = tensor4d, (n_samples, n_rows, n_cols, n_feats) 28 | nb_inc = int, number of filters in individual Conv2D 29 | inc_filt_list = list of kernel sizes, individual Conv2D kernel size 30 | name = str, name of module 31 | Output: 32 | xn = tensor4d, (n_samples, n_rows, n_cols, n_new_feats) 33 | ''' 34 | uc_list = [] 35 | for idx, ftuple in enumerate( inc_filt_list ) : 36 | uc = Conv2D( nb_inc, ftuple, activation='linear', padding='same', name=name+'_c%d' % idx)(x) 37 | uc_list.append(uc) 38 | if ( len( uc_list ) > 1 ) : 39 | uc_merge = Concatenate( axis=-1, name=name+'_merge')(uc_list) 40 | else : 41 | uc_merge = uc_list[0] 42 | uc_norm = BatchNormalization(name=name+'_bn')(uc_merge) 43 | xn = Activation('relu', name=name+'_re')(uc_norm) 44 | return xn 45 | 46 | class SelfCorrelationPercPooling( Layer ) : 47 | '''Custom Self-Correlation Percentile Pooling Layer 48 | Arugment: 49 | nb_pools = int, number of percentile poolings 50 | Input: 51 | x = tensor4d, (n_samples, n_rows, n_cols, n_feats) 52 | Output: 53 | x_pool = tensor4d, (n_samples, n_rows, n_cols, nb_pools) 54 | ''' 55 | def __init__( self, nb_pools=256, **kwargs ) : 56 | self.nb_pools = nb_pools 57 | super( SelfCorrelationPercPooling, self ).__init__( **kwargs ) 58 | def build( self, input_shape ) : 59 | self.built = True 60 | def call( self, x, mask=None ) : 61 | # parse input feature shape 62 | bsize, nb_rows, nb_cols, nb_feats = K.int_shape( x ) 63 | nb_maps = nb_rows * nb_cols 64 | # self correlation 65 | x_3d = K.reshape( x, tf.stack( [ -1, nb_maps, nb_feats ] ) ) 66 | x_corr_3d = tf.matmul( x_3d, x_3d, transpose_a = False, transpose_b = True ) / nb_feats 67 | x_corr = K.reshape( x_corr_3d, tf.stack( [ -1, nb_rows, nb_cols, nb_maps ] ) ) 68 | # argsort response maps along the translaton dimension 69 | if ( self.nb_pools is not None ) : 70 | ranks = K.cast( K.round( tf.lin_space( 1., nb_maps - 1, self.nb_pools ) ), 'int32' ) 71 | else : 72 | ranks = tf.range( 1, nb_maps, dtype = 'int32' ) 73 | x_sort, _ = tf.nn.top_k( x_corr, k = nb_maps, sorted = True ) 74 | # pool out x features at interested ranks 75 | # NOTE: tf v1.1 only support indexing at the 1st dimension 76 | x_f1st_sort = K.permute_dimensions( x_sort, ( 3, 0, 1, 2 ) ) 77 | x_f1st_pool = tf.gather( x_f1st_sort, ranks ) 78 | x_pool = K.permute_dimensions( x_f1st_pool, ( 1, 2, 3, 0 ) ) 79 | return x_pool 80 | def compute_output_shape( self, input_shape ) : 81 | bsize, nb_rows, nb_cols, nb_feats = input_shape 82 | nb_pools = self.nb_pools if ( self.nb_pools is not None ) else ( nb_rows * nb_cols - 1 ) 83 | return tuple( [ bsize, nb_rows, nb_cols, nb_pools ] ) 84 | 85 | class BilinearUpSampling2D( Layer ) : 86 | '''Custom 2x bilinear upsampling layer 87 | Input: 88 | x = tensor4d, (n_samples, n_rows, n_cols, n_feats) 89 | Output: 90 | x2 = tensor4d, (n_samples, 2*n_rows, 2*n_cols, n_feats) 91 | ''' 92 | def call( self, x, mask=None ) : 93 | bsize, nb_rows, nb_cols, nb_filts = K.int_shape(x) 94 | new_size = tf.constant( [ nb_rows * 2, nb_cols * 2 ], dtype = tf.int32 ) 95 | return tf.image.resize_images( x, new_size, align_corners=True ) 96 | def compute_output_shape( self, input_shape ) : 97 | bsize, nb_rows, nb_cols, nb_filts = input_shape 98 | return tuple( [ bsize, nb_rows * 2, nb_cols * 2, nb_filts ] ) 99 | 100 | class ResizeBack( Layer ) : 101 | '''Custom bilinear resize layer 102 | Resize x's spatial dimension to that of r 103 | 104 | Input: 105 | x = tensor4d, (n_samples, n_rowsX, n_colsX, n_featsX ) 106 | r = tensor4d, (n_samples, n_rowsR, n_colsR, n_featsR ) 107 | Output: 108 | xn = tensor4d, (n_samples, n_rowsR, n_colsR, n_featsX ) 109 | ''' 110 | def call( self, x ) : 111 | t, r = x 112 | new_size = [ tf.shape(r)[1], tf.shape(r)[2] ] 113 | return tf.image.resize_images( t, new_size, align_corners=True ) 114 | def compute_output_shape( self, input_shapes ) : 115 | tshape, rshape = input_shapes 116 | return ( tshape[0], ) + rshape[1:3] + ( tshape[-1], ) 117 | 118 | class Preprocess( Layer ) : 119 | """Basic preprocess layer for BusterNet 120 | More precisely, it does the following two things 121 | 1) normalize input image size to (256,256) to speed up processing 122 | 2) substract channel-wise means if necessary 123 | """ 124 | def call( self, x, mask=None ) : 125 | # parse input image shape 126 | bsize, nb_rows, nb_cols, nb_colors = K.int_shape(x) 127 | if (nb_rows != 256) or (nb_cols !=256) : 128 | # resize image if different from (256,256) 129 | x256 = tf.image.resize_bilinear( x, 130 | [256, 256], 131 | align_corners=True, 132 | name='resize' ) 133 | else : 134 | x256 = x 135 | # substract channel means if necessary 136 | if K.dtype(x) == 'float32' : 137 | # input is not a 'uint8' image 138 | # assume it has already been normalized 139 | xout = x256 140 | else : 141 | # input is a 'uint8' image 142 | # substract channel-wise means 143 | xout = preprocess_input( x256 ) 144 | return xout 145 | def compute_output_shape( self, input_shape ) : 146 | return (input_shape[0], 256, 256, 3) 147 | 148 | def create_cmfd_similarity_branch( img_shape=(256,256,3), 149 | nb_pools=100, 150 | name='simiDet' ) : 151 | '''Create the similarity branch for copy-move forgery detection 152 | ''' 153 | #--------------------------------------------------------- 154 | # Input 155 | #--------------------------------------------------------- 156 | img_input = Input( shape=img_shape, name=name+'_in' ) 157 | #--------------------------------------------------------- 158 | # VGG16 Conv Featex 159 | #--------------------------------------------------------- 160 | bname = name + '_cnn' 161 | ## Block 1 162 | x1 = Conv2D(64, (3, 3), activation='relu', padding='same', name=bname+'_b1c1')(img_input) 163 | x1 = Conv2D(64, (3, 3), activation='relu', padding='same', name=bname+'_b1c2')(x1) 164 | x1 = MaxPooling2D((2, 2), strides=(2, 2), name=bname+'_b1p')(x1) 165 | # Block 2 166 | x2 = Conv2D(128, (3, 3), activation='relu', padding='same', name=bname+'_b2c1')(x1) 167 | x2 = Conv2D(128, (3, 3), activation='relu', padding='same', name=bname+'_b2c2')(x2) 168 | x2 = MaxPooling2D((2, 2), strides=(2, 2), name=bname+'_b2p')(x2) 169 | # Block 3 170 | x3 = Conv2D(256, (3, 3), activation='relu', padding='same', name=bname+'_b3c1')(x2) 171 | x3 = Conv2D(256, (3, 3), activation='relu', padding='same', name=bname+'_b3c2')(x3) 172 | x3 = Conv2D(256, (3, 3), activation='relu', padding='same', name=bname+'_b3c3')(x3) 173 | x3 = MaxPooling2D((2, 2), strides=(2, 2), name=bname+'_b3p')(x3) 174 | # Block 4 175 | x4 = Conv2D(512, (3, 3), activation='relu', padding='same', name=bname+'_b4c1')(x3) 176 | x4 = Conv2D(512, (3, 3), activation='relu', padding='same', name=bname+'_b4c2')(x4) 177 | x4 = Conv2D(512, (3, 3), activation='relu', padding='same', name=bname+'_b4c3')(x4) 178 | x4 = MaxPooling2D((2, 2), strides=(2, 2), name=bname+'_b4p')(x4) 179 | # Local Std-Norm Normalization (within each sample) 180 | xx = Activation(std_norm_along_chs, name=bname+'_sn')(x4) 181 | #--------------------------------------------------------- 182 | # Self Correlation Pooling 183 | #--------------------------------------------------------- 184 | bname = name + '_corr' 185 | ## Self Correlation 186 | xcorr = SelfCorrelationPercPooling(name=bname+'_corr')(xx) 187 | ## Global Batch Normalization (across samples) 188 | xn = BatchNormalization(name=bname+'_bn')(xcorr) 189 | #--------------------------------------------------------- 190 | # Deconvolution Network 191 | #--------------------------------------------------------- 192 | patch_list = [(1,1),(3,3),(5,5)] 193 | # MultiPatch Featex 194 | bname = name + '_dconv' 195 | f16 = BnInception( xn, 8, patch_list, name =bname+'_mpf') 196 | # Deconv x2 197 | f32 = BilinearUpSampling2D( name=bname+'_bx2')( f16 ) 198 | dx32 = BnInception( f32, 6, patch_list, name=bname+'_dx2') 199 | # Deconv x4 200 | f64a = BilinearUpSampling2D( name=bname+'_bx4a')( f32 ) 201 | f64b = BilinearUpSampling2D( name=bname+'_bx4b')( dx32 ) 202 | f64 = Concatenate(axis=-1, name=name+'_dx4_m')([f64a, f64b]) 203 | dx64 = BnInception( f64, 4, patch_list, name=bname+'_dx4') 204 | # Deconv x8 205 | f128a = BilinearUpSampling2D( name=bname+'_bx8a')( f64a ) 206 | f128b = BilinearUpSampling2D( name=bname+'_bx8b')( dx64 ) 207 | f128 = Concatenate(axis=-1, name=name+'_dx8_m')([f128a, f128b]) 208 | dx128 = BnInception( f128, 2, patch_list, name=bname+'_dx8') 209 | # Deconv x16 210 | f256a = BilinearUpSampling2D( name=bname+'_bx16a')( f128a ) 211 | f256b = BilinearUpSampling2D( name=bname+'_bx16b')( dx128 ) 212 | f256 = Concatenate(axis=-1, name=name+'_dx16_m')([f256a,f256b]) 213 | dx256 = BnInception( f256, 2, patch_list, name=bname+'_dx16') 214 | # Summerize 215 | fm256 = Concatenate(axis=-1,name=name+'_mfeat')([f256a,dx256]) 216 | masks = BnInception( fm256, 2, [(5,5),(7,7),(11,11)], name=bname+'_dxF') 217 | #--------------------------------------------------------- 218 | # Output for Auxiliary Task 219 | #--------------------------------------------------------- 220 | pred_mask = Conv2D(1, (3,3), activation='sigmoid', name=name+'_pred_mask', padding='same')(masks) 221 | #--------------------------------------------------------- 222 | # End to End 223 | #--------------------------------------------------------- 224 | model = Model(inputs=img_input, outputs=pred_mask, name=name) 225 | return model 226 | 227 | 228 | def create_cmfd_manipulation_branch( img_shape=(256,256,3), 229 | name='maniDet' ) : 230 | '''Create the manipulation branch for copy-move forgery detection 231 | ''' 232 | #--------------------------------------------------------- 233 | # Input 234 | #--------------------------------------------------------- 235 | img_input = Input( shape = img_shape, name = name+'_in' ) 236 | #--------------------------------------------------------- 237 | # VGG16 Conv Featex 238 | #--------------------------------------------------------- 239 | bname = name + '_cnn' 240 | # Block 1 241 | x1 = Conv2D(64, (3, 3), activation='relu', padding='same', name=bname+'_b1c1')(img_input) 242 | x1 = Conv2D(64, (3, 3), activation='relu', padding='same', name=bname+'_b1c2')(x1) 243 | x1 = MaxPooling2D((2, 2), strides=(2, 2), name=bname+'_b1p')(x1) 244 | # Block 2 245 | x2 = Conv2D(128, (3, 3), activation='relu', padding='same', name=bname+'_b2c1')(x1) 246 | x2 = Conv2D(128, (3, 3), activation='relu', padding='same', name=bname+'_b2c2')(x2) 247 | x2 = MaxPooling2D((2, 2), strides=(2, 2), name=bname+'_b2p')(x2) 248 | # Block 3 249 | x3 = Conv2D(256, (3, 3), activation='relu', padding='same', name=bname+'_b3c1')(x2) 250 | x3 = Conv2D(256, (3, 3), activation='relu', padding='same', name=bname+'_b3c2')(x3) 251 | x3 = Conv2D(256, (3, 3), activation='relu', padding='same', name=bname+'_b3c3')(x3) 252 | x3 = MaxPooling2D((2, 2), strides=(2, 2), name=bname+'_b3p')(x3) 253 | # Block 4 254 | x4 = Conv2D(512, (3, 3), activation='relu', padding='same', name=bname+'_b4c1')(x3) 255 | x4 = Conv2D(512, (3, 3), activation='relu', padding='same', name=bname+'_b4c2')(x4) 256 | x4 = Conv2D(512, (3, 3), activation='relu', padding='same', name=bname+'_b4c3')(x4) 257 | x4 = MaxPooling2D((2, 2), strides=(2, 2), name=bname+'_b4p')(x4) 258 | #--------------------------------------------------------- 259 | # Deconvolution Network 260 | #--------------------------------------------------------- 261 | patch_list = [(1,1),(3,3),(5,5)] 262 | bname = name + '_dconv' 263 | # MultiPatch Featex 264 | f16 = BnInception( x4, 8, patch_list, name =bname+'_mpf') 265 | # Deconv x2 266 | f32 = BilinearUpSampling2D(name=bname+'_bx2')( f16 ) 267 | dx32 = BnInception( f32, 6, patch_list, name=bname+'_dx2') 268 | # Deconv x4 269 | f64 = BilinearUpSampling2D(name=bname+'_bx4')( dx32 ) 270 | dx64 = BnInception( f64, 4, patch_list, name=bname+'_dx4') 271 | # Deconv x8 272 | f128 = BilinearUpSampling2D(name=bname+'_bx8')( dx64 ) 273 | dx128 = BnInception( f128, 2, patch_list, name=bname+'_dx8') 274 | # Deconv x16 275 | f256 = BilinearUpSampling2D(name=bname+'_bx16')( dx128 ) 276 | dx256 = BnInception( f256, 2, [(5,5),(7,7),(11,11)], name=bname+'_dx16') 277 | #--------------------------------------------------------- 278 | # Output for Auxiliary Task 279 | #--------------------------------------------------------- 280 | pred_mask = Conv2D(1, (3,3), activation='sigmoid', name=bname+'_pred_mask', padding='same')(dx256) 281 | #--------------------------------------------------------- 282 | # End to End 283 | #--------------------------------------------------------- 284 | model = Model(inputs=img_input, outputs=pred_mask, name = bname) 285 | return model 286 | 287 | def create_BusterNet_testing_model( weight_file=None ) : 288 | '''create a busterNet testing model with pretrained weights 289 | ''' 290 | # 1. create branch model 291 | simi_branch = create_cmfd_similarity_branch() 292 | mani_branch = create_cmfd_manipulation_branch() 293 | # 2. crop off the last auxiliary task layer 294 | SimiDet = Model( inputs=simi_branch.inputs, 295 | outputs=simi_branch.layers[-2].output, 296 | name='simiFeatex' ) 297 | ManiDet = Model( inputs=mani_branch.inputs, 298 | outputs=mani_branch.layers[-2].output, 299 | name='maniFeatex' ) 300 | # 3. define the two-branch BusterNet model 301 | # 3.a define wrapper inputs 302 | img_raw = Input( shape=(None,None,3), name='image_in') 303 | img_in = Preprocess( name='preprocess')( img_raw ) 304 | # 3.b define BusterNet Core 305 | simi_feat = SimiDet( img_in ) 306 | mani_feat = ManiDet( img_in ) 307 | merged_feat = Concatenate(axis=-1, name='merge')([simi_feat, mani_feat]) 308 | f = BnInception( merged_feat, 3, name='fusion' ) 309 | mask_out = Conv2D( 3, (3,3), padding='same', activation='softmax', name='pred_mask')(f) 310 | # 3.c define wrapper output 311 | mask_out = ResizeBack(name='restore')([mask_out, img_raw] ) 312 | # 4. create BusterNet model end-to-end 313 | model = Model( inputs = img_raw, outputs = mask_out, name = 'busterNet') 314 | if weight_file is not None : 315 | try : 316 | model.load_weights( weight_file ) 317 | print("INFO: successfully load pretrained weights from {}".format( weight_file ) ) 318 | except Exception as e : 319 | print("INFO: fail to load pretrained weights from {} for reason: {}".format( weight_file, e )) 320 | return model -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # BachelorGraduationProject 2 | my Bachelor Graduation Project 3 | 4 | This is a python-based program which used a model designed in https://github.com/isi-vista/BusterNet. 5 | The software can achieve image cropping, pasting and saving. 6 | Moreover, simple tampered image localization and batch tampered images localization can also be completed by this software. 7 | 总之是一个python写的软件，用pyqt5做的UI，可以打开文件，检测文件（是否有复制粘帖的篡改）。可以批处理（直接处理一个文件夹的图片），也可以用户自己复制粘帖篡改然后再检测。 8 | 毕设项目。 9 | 10 | 11 | 12 | Here are some decriptions: 13 | 14 | The invention of image editing software leads to the proliferation of tampered images and makes images less credible. 15 | Copy-move tampering is a common type of forgery in digital image tampering. 16 | So, the digital image copy-move tampering and localization system will help people, especially those with occupational needs, distinguish and analyze the forged images. 17 | Therefore, using the latest copy-move tampering detection algorithm BusterNet and PyQt5 as the system framework, this paper designs a digital image copy-move tampering and localization system based on Python language, OpenCV and PIL digital image processing library. 18 | Through the model and software test, the results show that the digital image copy-move tampering and localization system has certain accuracy, so it has certain practicability. 19 | 20 | 部分成果展示： 21 | 图1: 导入图片并显示 22 | 23 | ![image](https://user-images.githubusercontent.com/31587409/133111039-73b6717c-69a1-49df-9ca4-27488e81eefc.png) 24 | 25 | 图2: 抠图 26 | 27 | ![image](https://user-images.githubusercontent.com/31587409/133111130-bbef11db-1f0a-454d-b024-ed0ac36ddc18.png) 28 | 29 | 图3: 贴图 30 | 31 | ![image](https://user-images.githubusercontent.com/31587409/133111224-d99da81f-1c40-4fda-9c0e-18185709daf5.png) 32 | 33 | 图4: 单幅定位及结果 34 | 35 | ![image](https://user-images.githubusercontent.com/31587409/133111290-932becda-e15a-42b6-b4e1-67b6b38f8d83.png) 36 | 37 | 图5: 导入文件夹处理结果 38 | 39 | ![image](https://user-images.githubusercontent.com/31587409/133111355-1610398f-2697-47cb-bbff-c1f7471b11b2.png) 40 | 41 | 42 | 43 | 44 | -------------------------------------------------------------------------------- /__pycache__/BusterNetCore.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/luckyredpanda/Image-Copy-move-Tampering-Localization-System/5b3e76047a6f1fdf021ae1715c2befa719d9bae4/__pycache__/BusterNetCore.cpython-37.pyc -------------------------------------------------------------------------------- /__pycache__/UIloadingpicture.cpython-37.pyc: 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/__pycache__/model.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/luckyredpanda/Image-Copy-move-Tampering-Localization-System/5b3e76047a6f1fdf021ae1715c2befa719d9bae4/__pycache__/model.cpython-37.pyc -------------------------------------------------------------------------------- /__pycache__/pasteimage.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/luckyredpanda/Image-Copy-move-Tampering-Localization-System/5b3e76047a6f1fdf021ae1715c2befa719d9bae4/__pycache__/pasteimage.cpython-37.pyc -------------------------------------------------------------------------------- /cropimagecir.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | from main import * 3 | 4 | global img 5 | global point1, point2 6 | 7 | lsPointsChoose = [] 8 | tpPointsChoose = [] 9 | lx = [] 10 | ly = [] 11 | 12 | pointsCount = 0 13 | count = 0 14 | pointsMax = 5 15 | 16 | #-----------------------鼠标操作相关------------------------------------------ 17 | #------------------------------------------------------------------------------ 18 | lsPointsChoose = [] 19 | tpPointsChoose = [] 20 | 21 | pointsCount = 0 22 | count = 0 23 | pointsMax = 20 24 | 25 | def on_mouse(event, x, y, flags, param): 26 | global img, point1, point2,count,pointsMax 27 | global lsPointsChoose, tpPointsChoose #存入选择的点 28 | global pointsCount #对鼠标按下的点计数 29 | global img2, ROI_bymouse_flag 30 | global a,b 31 | img2 = img.copy() #此行代码保证每次都重新再原图画 避免画多了 32 | 33 | 34 | 35 | 36 | if event == cv2.EVENT_LBUTTONDOWN: #左键点击 37 | 38 | pointsCount=pointsCount+1 39 | 40 | # 为了保存绘制的区域，画的点稍晚清零 41 | if(pointsCount==pointsMax+1): 42 | pointsCount = 0 43 | tpPointsChoose=[] 44 | print('pointsCount:', pointsCount) 45 | point1 = (x, y) 46 | print(x, y) 47 | lx.append(x) 48 | ly.append(y) 49 | print (lx) 50 | 51 | if (pointsCount == 1): 52 | a=x;b=y; 53 | # 画出点击的点 54 | cv2.circle(img2, point1, 10, (0, 255, 0), 5) 55 | 56 | # 将选取的点保存到list列表里 57 | lsPointsChoose.append([x, y]) #用于转化为darry 提取多边形ROI 58 | tpPointsChoose.append((x, y)) #用于画点 59 | #---------------------------------------------------------------------- 60 | #将鼠标选的点用直线链接起来 61 | print(len(tpPointsChoose)) 62 | for i in range(len(tpPointsChoose)-1): 63 | cv2.line(img2, tpPointsChoose[i], tpPointsChoose[i+1], (0, 0, 255), 2) 64 | #---------------------------------------------------------------------- 65 | #----------点击到pointMax时可以提取去绘图---------------- 66 | if (pointsCount !=pointsMax): 67 | if(pointsCount > 2): 68 | if(point1==(a,b) or point1==(a+1,b) or point1==(a-1,b)or point1==(a,b+1)or point1==(a,b-1)or point1==(a-1,b-1)or point1==(a+1,b+1)): 69 | #-----------绘制感兴趣区域----------- 70 | ROI_byMouse() 71 | ROI_bymouse_flag = 1 72 | lsPointsChoose = [] 73 | sys.exit(0) 74 | else: 75 | ROI_byMouse() 76 | ROI_bymouse_flag = 1 77 | lsPointsChoose = [] 78 | sys.exit(0) 79 | #-------------------------------------------------------- 80 | cv2.imshow('src', img2) 81 | #-------------------------右键按下清除轨迹（未完成）----------------------------- 82 | if event == cv2.EVENT_RBUTTONDOWN: #右键点击 83 | print("right-mouse") 84 | pointsCount = 0 85 | 86 | 87 | tpPointsChoose = [] 88 | lsPointsChoose = [] 89 | 90 | 91 | #-------------------------------------------------------------- 92 | def ROI_byMouse(): 93 | global src, ROI, ROI_flag, mask2 94 | mask = np.zeros(img.shape, np.uint8) 95 | pts = np.array([lsPointsChoose], np.int32) 96 | # pts.shape=(4，2) 97 | pts = pts.reshape((-1, 1, 2)) # -1代表剩下的维度自动计算 98 | # reshape 后的 pts.shape=(4。1，2)？？ 99 | #--------------画多边形--------------------- 100 | mask = cv2.polylines(mask, [pts], True, (0, 255, 255)) 101 | ##-------------填充多边形--------------------- 102 | mask2 = cv2.fillPoly(mask, [pts], (255,255,255)) 103 | #cv2.imshow('mask', mask2) 104 | dst = cv2.bitwise_and(img, mask) 105 | 106 | cv2.imwrite("image.png", dst) 107 | croppedmask = mask[ min(ly):max(ly),min(lx):max(lx)] 108 | cv2.imwrite("masktest.png",croppedmask) 109 | 110 | file_name = "image.png" 111 | src = cv2.imread(file_name, 1) 112 | tmp = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY) 113 | _,alpha = cv2.threshold(tmp,0,255,cv2.THRESH_BINARY) 114 | b, g, r = cv2.split(src) 115 | rgba = [b,g,r, alpha] 116 | dst = cv2.merge(rgba,4) 117 | cropped = dst[ min(ly):max(ly),min(lx):max(lx)] 118 | cv2.imshow("test.png", cropped) 119 | cv2.imwrite("test.png", cropped) 120 | os.remove("image.png") 121 | 122 | def main(): 123 | global img,img2,ROI 124 | with open("/Users/chenweihao/PycharmProjects/Bishe/fileaddress.txt", "r") as f: 125 | data = f.read() 126 | img = cv2.imread(data) 127 | 128 | #--------------------------------------------------------- 129 | #--图像预处理，设置其大小 130 | height, width = img.shape[:2] 131 | size = (int(width*1), int(height*1)) 132 | img = cv2.resize(img, size, interpolation=cv2.INTER_AREA) 133 | #------------------------------------------------------------ 134 | ROI = img.copy() 135 | cv2.namedWindow('src') 136 | cv2.setMouseCallback('src', on_mouse) 137 | cv2.imshow('src', img) 138 | cv2.waitKey(0) 139 | cv2.destroyAllWindows() 140 | 141 | if __name__ == '__main__': 142 | 143 | main() 144 | sys.exit(0) -------------------------------------------------------------------------------- /fileaddress.txt: -------------------------------------------------------------------------------- 1 | /Users/chenweihao/Downloads/test -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | import sys,os 2 | import cv2 3 | import matplotlib.pyplot as plt 4 | import numpy as np 5 | from PyQt5.QtWidgets import * 6 | from main_ui import * 7 | 8 | class MyMainWindow(QMainWindow, Ui_MainWindow): 9 | 10 | def __init__(self, parent=None): 11 | super(MyMainWindow, self).__init__(parent) 12 | self.setupUi(self) 13 | self.pushButton.clicked.connect(self.show_img) 14 | self.pushButton_2.clicked.connect(self.crop_image) 15 | self.pushButton_3.clicked.connect(self.paste_image) 16 | self.pushButton_8.clicked.connect(self.save_image) 17 | self.pushButton_9.clicked.connect(self.detect_image) 18 | self.pushButton_10.clicked.connect(self.detectmore_image) 19 | 20 | def text_create(self,msg): 21 | desktop_path = "/Users/chenweihao/PycharmProjects/Bishe/" # 新创建的txt文件的存放路径 22 | full_path = desktop_path +'fileaddress.txt' # 也可以创建一个.doc的word文档 23 | file = open(full_path, 'w') 24 | file.write(msg) 25 | 26 | #show image 27 | def show_img(self): 28 | global pic_path 29 | pic_path, _ = QFileDialog.getOpenFileName(self, '显示图片', '/Users/', 'Image files(*.jpg *.gif *.png *.tif)') 30 | self.text_create(pic_path) 31 | if pic_path: 32 | image2 = cv2.imread(pic_path) 33 | global show 34 | show = cv2.resize(image2, (800, 600)) 35 | show2 = cv2.cvtColor(show, cv2.COLOR_BGR2RGB) # 视频色彩转换回RGB，这样才是现实的颜色 36 | showImage = QtGui.QImage(show2.data, show2.shape[1], show2.shape[0], 37 | QtGui.QImage.Format_RGB888) # 把读取到的视频数据变成QImage形式 38 | self.label.setPixmap(QtGui.QPixmap.fromImage(showImage)) # 往显示视频的Label里 显示QImage 39 | 40 | #crop 41 | def crop_image(self): 42 | result = os.path.exists('/Users/chenweihao/PycharmProjects/Bishe/fileaddress.txt') 43 | if result == True: 44 | os.system('python cropimagecir.py') 45 | QMessageBox.information(self,"成功了！","您已经抠图成功！",QMessageBox.Yes | QMessageBox.No) 46 | else: 47 | QMessageBox.information(self,"错误","请选择一张图片！",QMessageBox.Yes | QMessageBox.No) 48 | 49 | #paste 50 | def paste_image(self): 51 | result = os.path.exists('/Users/chenweihao/PycharmProjects/Bishe/fileaddress.txt') 52 | if result == True: 53 | os.system('python pasteimage.py') 54 | QMessageBox.information(self,"成功了！","您已经贴图成功！如果不满意可以重新操作。",QMessageBox.Yes | QMessageBox.No) 55 | else: 56 | QMessageBox.information(self,"错误","请选择一张图片！",QMessageBox.Yes | QMessageBox.No) 57 | 58 | #save 59 | def save_image(self): 60 | result = os.path.exists('/Users/chenweihao/PycharmProjects/Bishe/fileaddress.txt') 61 | if result == True: 62 | pic_path = "/Users/chenweihao/PycharmProjects/Bishe/rectangle_result.png" 63 | self.text_create("/Users/chenweihao/PycharmProjects/Bishe/rectangle_result.png") 64 | QMessageBox.information(self,"成功了！","图像已保存！",QMessageBox.Yes | QMessageBox.No) 65 | image2 = cv2.imread(pic_path) 66 | show = cv2.resize(image2, (800, 600)) 67 | show2 = cv2.cvtColor(show, cv2.COLOR_BGR2RGB) # 视频色彩转换回RGB，这样才是现实的颜色 68 | showImage = QtGui.QImage(show2.data, show2.shape[1], show2.shape[0], 69 | QtGui.QImage.Format_RGB888) 70 | self.label.setPixmap(QtGui.QPixmap.fromImage(showImage)) 71 | else: 72 | QMessageBox.information(self,"错误","请选择一张图片！",QMessageBox.Yes | QMessageBox.No) 73 | 74 | #detect 75 | def detect_image(self): 76 | QMessageBox.information(self,"检测中","检测中，请稍等！大约需要20秒。",QMessageBox.Yes | QMessageBox.No) 77 | os.system('python model.py') 78 | #detect more than one pictures 79 | def detectmore_image(self): 80 | global directory 81 | directory = QFileDialog.getExistingDirectory(self, 82 | "选取文件夹", 83 | "./") 84 | self.text_create(directory) 85 | QMessageBox.information(self,"检测中","检测中，请稍等！先喝杯茶吧。",QMessageBox.Yes | QMessageBox.No) 86 | os.system('python morepictures.py') 87 | QMessageBox.information(self,"完成了！","恭喜你！完成啦！",QMessageBox.Yes | QMessageBox.No) 88 | 89 | 90 | if __name__ == '__main__': 91 | app = QApplication(sys.argv) 92 | myWin = MyMainWindow() 93 | myWin.show() 94 | sys.exit(app.exec_()) -------------------------------------------------------------------------------- /main_ui.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | # Form implementation generated from reading ui file 'main_ui.ui' 4 | # 5 | # Created by: PyQt5 UI code generator 5.9.2 6 | # 7 | # WARNING! All changes made in this file will be lost! 8 | 9 | from PyQt5 import QtCore, QtGui, QtWidgets 10 | 11 | class Ui_MainWindow(object): 12 | def setupUi(self, MainWindow): 13 | MainWindow.setObjectName("MainWindow") 14 | MainWindow.resize(1058, 739) 15 | self.centralwidget = QtWidgets.QWidget(MainWindow) 16 | self.centralwidget.setObjectName("centralwidget") 17 | self.pushButton_9 = QtWidgets.QPushButton(self.centralwidget) 18 | self.pushButton_9.setGeometry(QtCore.QRect(400, 640, 101, 41)) 19 | self.pushButton_9.setIconSize(QtCore.QSize(50, 50)) 20 | self.pushButton_9.setObjectName("pushButton_9") 21 | self.label = QtWidgets.QLabel(self.centralwidget) 22 | self.label.setGeometry(QtCore.QRect(160, 10, 841, 611)) 23 | self.label.setText("") 24 | self.label.setTextFormat(QtCore.Qt.AutoText) 25 | self.label.setObjectName("label") 26 | self.layoutWidget = QtWidgets.QWidget(self.centralwidget) 27 | self.layoutWidget.setGeometry(QtCore.QRect(20, 40, 116, 491)) 28 | self.layoutWidget.setObjectName("layoutWidget") 29 | self.verticalLayout = QtWidgets.QVBoxLayout(self.layoutWidget) 30 | self.verticalLayout.setContentsMargins(0, 0, 0, 0) 31 | self.verticalLayout.setObjectName("verticalLayout") 32 | self.pushButton = QtWidgets.QPushButton(self.layoutWidget) 33 | self.pushButton.setObjectName("pushButton") 34 | self.verticalLayout.addWidget(self.pushButton) 35 | self.pushButton_2 = QtWidgets.QPushButton(self.layoutWidget) 36 | self.pushButton_2.setObjectName("pushButton_2") 37 | self.verticalLayout.addWidget(self.pushButton_2) 38 | self.pushButton_3 = QtWidgets.QPushButton(self.layoutWidget) 39 | self.pushButton_3.setObjectName("pushButton_3") 40 | self.verticalLayout.addWidget(self.pushButton_3) 41 | self.pushButton_8 = QtWidgets.QPushButton(self.layoutWidget) 42 | self.pushButton_8.setObjectName("pushButton_8") 43 | self.verticalLayout.addWidget(self.pushButton_8) 44 | self.pushButton_10 = QtWidgets.QPushButton(self.centralwidget) 45 | self.pushButton_10.setGeometry(QtCore.QRect(600, 640, 101, 41)) 46 | self.pushButton_10.setIconSize(QtCore.QSize(50, 50)) 47 | self.pushButton_10.setObjectName("pushButton_10") 48 | MainWindow.setCentralWidget(self.centralwidget) 49 | self.menubar = QtWidgets.QMenuBar(MainWindow) 50 | self.menubar.setGeometry(QtCore.QRect(0, 0, 1058, 22)) 51 | self.menubar.setObjectName("menubar") 52 | MainWindow.setMenuBar(self.menubar) 53 | self.statusbar = QtWidgets.QStatusBar(MainWindow) 54 | self.statusbar.setObjectName("statusbar") 55 | MainWindow.setStatusBar(self.statusbar) 56 | 57 | self.retranslateUi(MainWindow) 58 | QtCore.QMetaObject.connectSlotsByName(MainWindow) 59 | 60 | def retranslateUi(self, MainWindow): 61 | _translate = QtCore.QCoreApplication.translate 62 | MainWindow.setWindowTitle(_translate("MainWindow", "数字图像复制粘贴篡改与定位系统")) 63 | self.pushButton_9.setText(_translate("MainWindow", "单幅定位")) 64 | self.pushButton.setText(_translate("MainWindow", "打开图片")) 65 | self.pushButton_2.setText(_translate("MainWindow", "自由抠图")) 66 | self.pushButton_3.setText(_translate("MainWindow", "图像粘贴")) 67 | self.pushButton_8.setText(_translate("MainWindow", "结果保存")) 68 | self.pushButton_10.setText(_translate("MainWindow", "测试集定位")) 69 | 70 | -------------------------------------------------------------------------------- /main_ui.ui: -------------------------------------------------------------------------------- 1 | 2 | 3 | MainWindow 4 | 5 | 6 | 7 | 0 8 | 0 9 | 1058 10 | 739 11 | 12 | 13 | 14 | MainWindow 15 | 16 | 17 | 18 | 19 | 20 | 400 21 | 640 22 | 101 23 | 41 24 | 25 | 26 | 27 | 单幅定位 28 | 29 | 30 | 31 | 50 32 | 50 33 | 34 | 35 | 36 | 37 | 38 | 39 | 160 40 | 10 41 | 841 42 | 611 43 | 44 | 45 | 46 | 47 | 48 | 49 | Qt::AutoText 50 | 51 | 52 | 53 | 54 | 55 | 20 56 | 40 57 | 116 58 | 491 59 | 60 | 61 | 62 | 63 | 64 | 65 | 打开图片 66 | 67 | 68 | 69 | 70 | 71 | 72 | 自由抠图 73 | 74 | 75 | 76 | 77 | 78 | 79 | 图像粘贴 80 | 81 | 82 | 83 | 84 | 85 | 86 | 结果保存 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 600 96 | 640 97 | 101 98 | 41 99 | 100 | 101 | 102 | 测试集定位 103 | 104 | 105 | 106 | 50 107 | 50 108 | 109 | 110 | 111 | 112 | 113 | 114 | 115 | 0 116 | 0 117 | 1058 118 | 22 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | -------------------------------------------------------------------------------- /masktest.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/luckyredpanda/Image-Copy-move-Tampering-Localization-System/5b3e76047a6f1fdf021ae1715c2befa719d9bae4/masktest.png -------------------------------------------------------------------------------- /model.py: -------------------------------------------------------------------------------- 1 | 2 | 3 | #from __future__ import print_function 4 | import os 5 | import sys 6 | import cv2 7 | import pandas 8 | import tensorflow as tf 9 | import numpy as np 10 | import warnings 11 | from matplotlib import pyplot 12 | warnings.filterwarnings("ignore") 13 | os.environ['TF_CPP_MIN_LOG_LEVEL']='2' 14 | import matplotlib 15 | 16 | 17 | 18 | def visualize_one_sample( X, Y, Z, figsize=(12,4)): 19 | x = np.array(X).astype('uint8') 20 | y = np.array(Y).astype('uint8') 21 | z = np.array(Z) 22 | pyplot.figure(figsize=figsize) 23 | pyplot.subplot(131) 24 | pyplot.imshow( x ) 25 | pyplot.title('test image') 26 | pyplot.subplot(132) 27 | pyplot.imshow( y ) 28 | pyplot.title('ground truth') 29 | pyplot.subplot(133) 30 | pyplot.imshow( z ) 31 | pyplot.title('BusterNet predicted') 32 | return 33 | 34 | def text_createX(msg): 35 | desktop_path = "/Users/chenweihao/PycharmProjects/Bishe/" # 新创建的txt文件的存放路径 36 | full_path = desktop_path +'X.txt' # 也可以创建一个.doc的word文档 37 | file = open(full_path, 'w') 38 | file.write(msg) 39 | 40 | from BusterNetCore import create_BusterNet_testing_model 41 | 42 | with open("/Users/chenweihao/PycharmProjects/Bishe/fileaddress.txt", "r") as f: 43 | data = f.read() 44 | 45 | model_dir = '/Users/chenweihao/Downloads/CM/logs' 46 | sys.path.insert( 0, model_dir ) 47 | busterNetModel = create_BusterNet_testing_model( os.path.join( model_dir, 'pretrained_busterNet.hd5' ) ) 48 | 49 | filename_test = data # 测试其它图像，修改文件名 50 | X = cv2.imread(data) 51 | X = cv2.resize(X, (300, 300)) 52 | cv2.imwrite('114.png', np.uint8(X)) 53 | text_createX('/Users/chenweihao/PycharmProjects/Bishe/114.png') 54 | pyplot.subplot(1,2,1) 55 | pyplot.imshow(X) 56 | X = cv2.resize(X, (300, 300)) 57 | Z = busterNetModel.predict(np.uint8(np.expand_dims(X, axis=0)), verbose = 0) 58 | Z = np.uint8(Z[0] * 255.0) 59 | Z1 = cv2.cvtColor(Z, cv2.COLOR_BGR2GRAY) 60 | ret, result_img = cv2.threshold(Z1, 90, 255, cv2.THRESH_BINARY) 61 | pyplot.subplot(1,2,2) 62 | pyplot.imshow(result_img) 63 | cv2.imwrite('114514.png', np.uint8(result_img)) 64 | os.system("python modelUI.py") 65 | 66 | -------------------------------------------------------------------------------- /modelUI.py: -------------------------------------------------------------------------------- 1 | import sys,os,cv2 2 | from PyQt5 import QtWidgets, QtCore, QtGui 3 | from PyQt5.QtGui import * 4 | from PyQt5.QtWidgets import * 5 | from PyQt5.QtCore import * 6 | 7 | global a,b 8 | 9 | with open("/Users/chenweihao/PycharmProjects/Bishe/X.txt", "r") as f: 10 | datax = f.read() 11 | 12 | class modelUI (QMainWindow): 13 | 14 | def __init__(self): 15 | super ().__init__() 16 | self.initUI () 17 | 18 | def initUI(self): 19 | 20 | 21 | pix1 = QPixmap(datax) 22 | lb1 = QLabel(self) 23 | lb1.setGeometry(0,0,300,300) 24 | lb1.setStyleSheet("border: 1px solid black") 25 | lb1.setPixmap(pix1) 26 | 27 | pix2 = QPixmap('114514.png') 28 | 29 | lb1 = QLabel(self) 30 | lb1.setGeometry(0,300,300,300) 31 | lb1.setStyleSheet("border: 1px solid black") 32 | lb1.setPixmap(pix2) 33 | 34 | 35 | #设置窗口的位置和大小 36 | self.setGeometry(300, 600, 300, 600) 37 | #设置窗口的标题 38 | self.setWindowTitle('最终结果') 39 | 40 | self.show() 41 | 42 | 43 | 44 | if __name__ == '__main__': 45 | app = QApplication(sys.argv) 46 | md = modelUI () 47 | os.remove('/Users/chenweihao/PycharmProjects/Bishe/X.txt') 48 | os.remove('/Users/chenweihao/PycharmProjects/Bishe/fileaddress.txt') 49 | os.remove('/Users/chenweihao/PycharmProjects/Bishe/114514.png') 50 | os.remove('/Users/chenweihao/PycharmProjects/Bishe/114.png') 51 | sys.exit (app.exec_()) 52 | 53 | 54 | -------------------------------------------------------------------------------- /morepictures.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | import cv2 4 | import pandas 5 | import tensorflow as tf 6 | import numpy as np 7 | import warnings 8 | from matplotlib import pyplot 9 | warnings.filterwarnings("ignore") 10 | os.environ['TF_CPP_MIN_LOG_LEVEL']='2' 11 | import matplotlib 12 | 13 | def visualize_one_sample( X, Y, Z, figsize=(12,4)): 14 | x = np.array(X).astype('uint8') 15 | y = np.array(Y).astype('uint8') 16 | z = np.array(Z) 17 | pyplot.figure(figsize=figsize) 18 | pyplot.subplot(131) 19 | pyplot.imshow( x ) 20 | pyplot.title('test image') 21 | pyplot.subplot(132) 22 | pyplot.imshow( y ) 23 | pyplot.title('ground truth') 24 | pyplot.subplot(133) 25 | pyplot.imshow( z ) 26 | pyplot.title('BusterNet predicted') 27 | return 28 | 29 | from BusterNetCore import create_BusterNet_testing_model 30 | 31 | model_dir = '/Users/chenweihao/Downloads/CM/logs' 32 | sys.path.insert( 0, model_dir ) 33 | busterNetModel = create_BusterNet_testing_model( os.path.join( model_dir, 'pretrained_busterNet.hd5' ) ) 34 | 35 | with open("/Users/chenweihao/PycharmProjects/Bishe/fileaddress.txt", "r") as f: 36 | data = f.read() 37 | 38 | for filename in os.listdir(data): 39 | if filename.endswith('jpg') or filename.endswith('png') or filename.endswith('tif'): 40 | print (data+'/'+filename) # 测试其它图像，修改文件名 41 | X = cv2.imread(data+'/'+filename) 42 | pyplot.subplot(1,2,1) 43 | pyplot.imshow(X) 44 | Z = busterNetModel.predict( np.uint8(np.expand_dims(X, axis=0)), verbose = 0) 45 | Z = np.uint8(Z[0] * 255.0) 46 | Z1 = cv2.cvtColor(Z, cv2.COLOR_BGR2GRAY) 47 | ret, result_img = cv2.threshold(Z1, 90, 255, cv2.THRESH_BINARY) 48 | pyplot.subplot(1,2,2) 49 | pyplot.imshow(result_img) 50 | filename = filename [:-4] 51 | cv2.imwrite(data+'/'+filename + 'mask.png', np.uint8(result_img)) 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | -------------------------------------------------------------------------------- /pasteimage.py: -------------------------------------------------------------------------------- 1 | from PyQt5.QtWidgets import * 2 | from PyQt5.QtCore import QCoreApplication, QRect 3 | from PyQt5.QtWidgets import QFileDialog 4 | import PIL 5 | from PIL import Image, ImageDraw, ImageFilter 6 | from PyQt5 import QtCore, QtGui, QtWidgets 7 | from PyQt5.QtWidgets import QWidget, QApplication, QLabel 8 | from PyQt5.QtCore import QRect, Qt 9 | from PyQt5.QtGui import * 10 | import sys,os 11 | 12 | global a,b 13 | 14 | with open("/Users/chenweihao/PycharmProjects/Bishe/fileaddress.txt", "r") as f: 15 | data = f.read() 16 | im1 = Image.open(data) 17 | a = im1.size[0] 18 | b = im1.size[1] 19 | 20 | 21 | class myLabel2(QMainWindow): 22 | 23 | x0 = 0 24 | y0 = 0 25 | flag = False 26 | global x, y 27 | 28 | def __init__(self, parent=None): 29 | super(myLabel2, self).__init__(parent) 30 | 31 | self.setWindowTitle('贴图界面') 32 | window_pale = QtGui.QPalette() 33 | window_pale.setBrush(self.backgroundRole(), QtGui.QBrush(QtGui.QPixmap(data))) 34 | self.setPalette(window_pale) 35 | self.resize(a,b) 36 | 37 | 38 | def mousePressEvent(self, event): 39 | self.flag = True 40 | self.x0 = event.x() 41 | self.y0 = event.y() 42 | global x 43 | global y 44 | x = self.x0 45 | y = self.y0 46 | print(x) 47 | print(y) 48 | im1 = Image.open(data) 49 | im2 = Image.open('test.png') 50 | c = im2.size[0] 51 | d = im2.size[1] 52 | mask_im =Image.open('masktest.png').resize(im2.size).convert('L') 53 | back_im = im1.copy() 54 | x = int(x-(c/2)) 55 | y = int(y-(d/2)) 56 | print(x) 57 | print(y) 58 | back_im.paste(im2, (x, y), mask_im) 59 | back_im.save('rectangle_result.png', quality=95) 60 | back_im.show('rectangle_result.png') 61 | self.pic = QtGui.QPixmap("rectangle_result.png") 62 | 63 | def mouseReleaseEvent(self, event): 64 | self.flag = False 65 | 66 | 67 | qapp = QApplication(sys.argv) 68 | app = myLabel2() 69 | 70 | app.show() 71 | sys.exit(qapp.exec_()) -------------------------------------------------------------------------------- /rectangle_result.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/luckyredpanda/Image-Copy-move-Tampering-Localization-System/5b3e76047a6f1fdf021ae1715c2befa719d9bae4/rectangle_result.png -------------------------------------------------------------------------------- /test.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/luckyredpanda/Image-Copy-move-Tampering-Localization-System/5b3e76047a6f1fdf021ae1715c2befa719d9bae4/test.png --------------------------------------------------------------------------------