我们从Python开源项目中，提取了以下5个代码示例，用于说明如何使用cv2.COLOR_BGR2YCR_CB。
def compute(self, img): averages = np.zeros((self.rows,self.cols,3)) imgH, imgW, _ = img.shape for row in range(self.rows): for col in range(self.cols): slice = img[imgH/self.rows * row: imgH/self.rows * (row+1), imgW/self.cols*col : imgW/self.cols*(col+1)] average_color_per_row = np.mean(slice, axis=0) average_color = np.mean(average_color_per_row, axis=0) average_color = np.uint8(average_color) averages[row][col][0] = average_color[0] averages[row][col][1] = average_color[1] averages[row][col][2] = average_color[2] icon = cv2.cvtColor(np.array(averages, dtype=np.uint8), cv2.COLOR_BGR2YCR_CB) y, cr, cb = cv2.split(icon) dct_y = cv2.dct(np.float32(y)) dct_cb = cv2.dct(np.float32(cb)) dct_cr = cv2.dct(np.float32(cr)) dct_y_zigzag = [] dct_cb_zigzag = [] dct_cr_zigzag = [] flip = True flipped_dct_y = np.fliplr(dct_y) flipped_dct_cb = np.fliplr(dct_cb) flipped_dct_cr = np.fliplr(dct_cr) for i in range(self.rows + self.cols -1): k_diag = self.rows - 1 - i diag_y = np.diag(flipped_dct_y, k=k_diag) diag_cb = np.diag(flipped_dct_cb, k=k_diag) diag_cr = np.diag(flipped_dct_cr, k=k_diag) if flip: diag_y = diag_y[::-1] diag_cb = diag_cb[::-1] diag_cr = diag_cr[::-1] dct_y_zigzag.append(diag_y) dct_cb_zigzag.append(diag_cb) dct_cr_zigzag.append(diag_cr) flip = not flip return np.concatenate([np.concatenate(dct_y_zigzag), np.concatenate(dct_cb_zigzag), np.concatenate(dct_cr_zigzag)])
def test_lapsrn(): img=cv2.imread(("E:\\DevProj\\Datasets\\SuperResolution\\SR_testing_datasets" "\\Set14\\GT\\zebra.png"),cv2.IMREAD_COLOR) nh,nw,nc=img.shape # imghr=cv2.cvtColor(img,cv2.COLOR_BGR2YCR_CB) img_lr, img_pryd=img_preprocess(img,2) one_batch=LapSRNDataBatch(img_lr,img_pryd) net, arg_params, aux_params = mx.model.load_checkpoint("checkpoint\\lapsrn", 100) mod = mx.mod.Module(symbol=net, context=mx.gpu()) provide_data=[('imglr', img_lr.shape)] provide_label=[] for s in range(2): provide_label.append(("loss_s{}_imggt".format(s),img_pryd[s].shape)) mod.bind(for_training=False, data_shapes=provide_data, label_shapes=provide_label) mod.set_params(arg_params, aux_params,allow_missing=True) mod.forward(one_batch) img_sr=mod.get_outputs() # img_sr=img_recover(img_sr) img_lr=img_recover(img_lr) img_hr=img_recover(img_pryd[-1]) cv2.imwrite("results\\lapsrn_imglr.bmp",img_lr) cv2.imwrite("results\\lapsrn_imghr.bmp",img_hr) for s in range(2): img_temp=img_recover(img_sr[s].asnumpy()) cv2.imwrite("results\\lapsrn_imgsr{}.bmp".format(s),img_temp)
def next(self): nrow=0 ncol=0 crop_size=self._crop_size while (nrow<crop_size or ncol<crop_size) \ and self.cur_batch < self.batch_num: img_path=os.path.join(self._datadir, self._img_list[self.cur_batch]) img=cv2.imread(img_path, cv2.IMREAD_COLOR) # img=cv2.cvtColor(img,cv2.COLOR_BGR2YCR_CB) nrow,ncol=img.shape[0:2] self.cur_batch+=1 if self.cur_batch < self.batch_num: # img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB) img_ds=cv2.resize(img,(ncol/self._scale_factor, nrow/self._scale_factor), interpolation=cv2.INTER_CUBIC) img_lr=cv2.resize(img_ds,(ncol, nrow),interpolation=cv2.INTER_CUBIC) img=img.astype(npy.float32) img_lr=img_lr.astype(npy.float32) sub_img_lr=npy.zeros(self._provide_data[0][1],dtype=npy.float32) sub_img_hr=npy.zeros(self._provide_label[0][1],dtype=npy.float32) for i in range(self._crop_num): nrow_start=npy.random.randint(0,nrow-crop_size) ncol_start=npy.random.randint(0,ncol-crop_size) img_crop=img_lr[nrow_start:nrow_start+crop_size, ncol_start:ncol_start+crop_size,:] img_crop=(img_crop-128) /128.0 img_crop = npy.swapaxes(img_crop, 0, 2) img_crop = npy.swapaxes(img_crop, 1, 2) sub_img_lr[i,:,:,:]=img_crop img_crop=img[nrow_start:nrow_start+crop_size, ncol_start:ncol_start+crop_size,:] img_crop=(img_crop-128) /128.0 img_crop = npy.swapaxes(img_crop, 0, 2) img_crop = npy.swapaxes(img_crop, 1, 2) sub_img_hr[i,:,:,:]=img_crop return SRDataBatch(sub_img_lr,sub_img_hr,0) else: raise StopIteration
def next(self): nrow=0 ncol=0 crop_size=self._crop_size while (nrow<crop_size or ncol<crop_size) \ and self.cur_batch < self.batch_num: img_path=os.path.join(self._datadir, self._img_list[self.cur_batch]) img=cv2.imread(img_path, cv2.IMREAD_COLOR) # img=cv2.cvtColor(img,cv2.COLOR_BGR2YCR_CB) nrow,ncol=img.shape[0:2] self.cur_batch+=1 if self.cur_batch < self.batch_num: sub_img_lr=npy.zeros(self._provide_data[0][1],dtype=npy.float32) sub_img_pryd=[] for item in self._provide_label: sub_img_pryd.append(npy.zeros(item[1],dtype=npy.float32)) for i in range(self._crop_num): nrow_start=npy.random.randint(0,nrow-crop_size) ncol_start=npy.random.randint(0,ncol-crop_size) img_crop=img[nrow_start:nrow_start+crop_size, ncol_start:ncol_start+crop_size,:] imggt_size=crop_size for s in range(self._num_scales): img_temp=img_crop.astype(npy.float32) img_temp=(img_temp-128)/128.0 img_temp = npy.swapaxes(img_temp, 0, 2) img_temp = npy.swapaxes(img_temp, 1, 2) sub_img_pryd[self._num_scales-s-1][i,:,:,:]=img_temp imggt_size=imggt_size/2 img_crop=cv2.resize(img_crop,(imggt_size, imggt_size), interpolation=cv2.INTER_CUBIC) img_temp=img_crop.astype(npy.float32) img_temp=(img_temp-128)/128.0 img_temp = npy.swapaxes(img_temp, 0, 2) img_temp = npy.swapaxes(img_temp, 1, 2) sub_img_lr[i,:,:,:]=img_temp return LapSRNDataBatch(sub_img_lr,sub_img_pryd,0) else: raise StopIteration
def hisEqulColor(img): ycrcb=cv2.cvtColor(img,cv2.COLOR_BGR2YCR_CB) channels=cv2.split(ycrcb) # create a CLAHE object clahe = cv2.createCLAHE() channels[0] = clahe.apply(channels[0]) cv2.merge(channels,ycrcb) cv2.cvtColor(ycrcb,cv2.COLOR_YCR_CB2BGR,img)
