我们从Python开源项目中，提取了以下10个代码示例，用于说明如何使用cv2.COLOR_BGR2YCrCb()。
def vilization_and_show(): model = model_EES16() model.load_weights("EES_check.h5") IMG_NAME = "comic.bmp" INPUT_NAME = "input.jpg" img = cv2.imread(IMG_NAME) shape = img.shape img = cv2.resize(img, (shape[1] / 2, shape[0] / 2), cv2.INTER_CUBIC) cv2.imwrite(INPUT_NAME, img) img = cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb) Y = numpy.zeros((1, img.shape[0], img.shape[1], 1)) Y[0, :, :, 0] = img[:, :, 0] feature_map_visilization(model, Y)
def histogram_equalization(image, tile): if (tile < 0): tile = 0 elif (tile > 100): tile = 100 tile = int(tile / 10) img_yuv = cv2.cvtColor(image, cv2.COLOR_BGR2YCrCb) clahe = cv2.createCLAHE(clipLimit=1.0, tileGridSize=(2 ** tile, 2 ** tile)) img_yuv[:, :, 0] = clahe.apply(img_yuv[:, :, 0]) img_out = cv2.cvtColor(img_yuv, cv2.COLOR_YCrCb2BGR) img = exposure.rescale_intensity(img_out) return img
def prepare_training_data(): names = os.listdir(DATA_PATH) names = sorted(names) nums = names.__len__() data = numpy.zeros((nums * Random_Crop, 1, Patch_size, Patch_size), dtype=numpy.double) label = numpy.zeros((nums * Random_Crop, 1, label_size, label_size), dtype=numpy.double) for i in range(nums): name = DATA_PATH + names[i] hr_img = cv2.imread(name, cv2.IMREAD_COLOR) shape = hr_img.shape hr_img = cv2.cvtColor(hr_img, cv2.COLOR_BGR2YCrCb) hr_img = hr_img[:, :, 0] # produce Random_Crop random coordinate to crop training img if(min(shape[0], shape[1]) - label_size < 0): continue Points_x = numpy.random.randint(0, min(shape[0], shape[1]) - label_size, Random_Crop) Points_y = numpy.random.randint(0, min(shape[0], shape[1]) - label_size, Random_Crop) for j in range(Random_Crop): hr_patch = hr_img[Points_x[j]: Points_x[j] + label_size, Points_y[j]: Points_y[j] + label_size] lr_patch = cv2.resize(hr_patch, (label_size / scale, label_size / scale), cv2.INTER_CUBIC) lr_patch = lr_patch.astype(float) / 255. hr_patch = hr_patch.astype(float) / 255. data[i * Random_Crop + j, 0, :, :] = lr_patch label[i * Random_Crop + j, 0, :, :] = hr_patch # cv2.imshow("lr", lr_patch) # cv2.imshow("hr", hr_patch) # cv2.waitKey(0) return data, label
def EEDS_predict(): EEDS = model_EEDS(input_col=128, input_row=128) EEDS.load_weights("EEDS8_model_adam100.h5") IMG_NAME = "butterfly_GT.bmp" INPUT_NAME = "input.jpg" OUTPUT_NAME = "EEDS8_adam100.jpg" import cv2 img = cv2.imread(IMG_NAME) # img = img[:96, :96, :] shape = img.shape img = cv2.resize(img, (shape[1] / 2, shape[0] / 2), cv2.INTER_CUBIC) cv2.imwrite(INPUT_NAME, img) img = cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb) Y = numpy.zeros((1, img.shape[0], img.shape[1], 1)) Y[0, :, :, 0] = img[:, :, 0] img = cv2.resize(img, (shape[1], shape[0]), cv2.INTER_CUBIC) pre = EEDS.predict(Y, batch_size=1) pre[pre[:] > 255] = 255 pre[pre[:] < 0] = 0 pre = pre.astype(numpy.uint8) img[:, :, 0] = pre[0, :, :, 0] img = cv2.cvtColor(img, cv2.COLOR_YCrCb2BGR) cv2.imwrite(OUTPUT_NAME, img) # psnr calculation: im1 = cv2.imread(IMG_NAME, cv2.IMREAD_COLOR) im1 = cv2.cvtColor(im1, cv2.COLOR_BGR2YCrCb) im2 = cv2.imread(INPUT_NAME, cv2.IMREAD_COLOR) im2 = cv2.cvtColor(im2, cv2.COLOR_BGR2YCrCb) im2 = cv2.resize(im2, (img.shape[1], img.shape[0])) im3 = cv2.imread(OUTPUT_NAME, cv2.IMREAD_COLOR) im3 = cv2.cvtColor(im3, cv2.COLOR_BGR2YCrCb) print "Bicubic:" print cv2.PSNR(im1, im2) # [:, :, 0] print "EEDS:" print cv2.PSNR(im1, im3)
def EES_predict(): EES = model_EES(input_col=128, input_row=128) EES.load_weights("EES_model_adam200.h5") IMG_NAME = "butterfly_GT.bmp" INPUT_NAME = "input.jpg" OUTPUT_NAME = "EES_pre_adam200.jpg" import cv2 img = cv2.imread(IMG_NAME) # img = img[:96, :96, :] shape = img.shape img = cv2.resize(img, (shape[1] / 2, shape[0] / 2), cv2.INTER_CUBIC) cv2.imwrite(INPUT_NAME, img) img = cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb) Y = numpy.zeros((1, img.shape[0], img.shape[1], 1)) Y[0, :, :, 0] = img[:, :, 0] img = cv2.resize(img, (shape[1], shape[0]), cv2.INTER_CUBIC) pre = EES.predict(Y, batch_size=1) pre[pre[:] > 255] = 255 pre[pre[:] < 0] = 0 pre = pre.astype(numpy.uint8) img[:, :, 0] = pre[0, :, :, 0] img = cv2.cvtColor(img, cv2.COLOR_YCrCb2BGR) cv2.imwrite(OUTPUT_NAME, img) # psnr calculation: im1 = cv2.imread(IMG_NAME, cv2.IMREAD_COLOR) # im1 = cv2.cvtColor(im1, cv2.COLOR_BGR2YCrCb) im2 = cv2.imread(INPUT_NAME, cv2.IMREAD_COLOR) # im2 = cv2.cvtColor(im2, cv2.COLOR_BGR2YCrCb) im2 = cv2.resize(im2, (img.shape[1], img.shape[0])) im3 = cv2.imread(OUTPUT_NAME, cv2.IMREAD_COLOR) # im3 = cv2.cvtColor(im3, cv2.COLOR_BGR2YCrCb) print "Bicubic:" print cv2.PSNR(im1, im2) print "EES:" print cv2.PSNR(im1, im3)
def EED_predict(): EED = model_EED() EED.load_weights("EED_model_adam100.h5") IMG_NAME = "butterfly_GT.bmp" INPUT_NAME = "input.jpg" OUTPUT_NAME = "EED_pre_adam100.jpg" import cv2 img = cv2.imread(IMG_NAME) shape = img.shape img = cv2.resize(img, (shape[1] / 2, shape[0] / 2), cv2.INTER_CUBIC) cv2.imwrite(INPUT_NAME, img) img = cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb) Y = numpy.zeros((1, img.shape[0], img.shape[1], 1)) Y[0, :, :, 0] = img[:, :, 0] img = cv2.resize(img, (shape[1], shape[0]), cv2.INTER_CUBIC) pre = EED.predict(Y, batch_size=1) pre[pre[:] > 255] = 255 pre = pre.astype(numpy.uint8) img[:, :, 0] = pre[0, :, :, 0] img = cv2.cvtColor(img, cv2.COLOR_YCrCb2BGR) cv2.imwrite(OUTPUT_NAME, img) # psnr calculation: im1 = cv2.imread(IMG_NAME, cv2.IMREAD_COLOR) im1 = cv2.cvtColor(im1, cv2.COLOR_BGR2YCrCb) im2 = cv2.imread(INPUT_NAME, cv2.IMREAD_COLOR) im2 = cv2.cvtColor(im2, cv2.COLOR_BGR2YCrCb) im2 = cv2.resize(im2, (img.shape[1], img.shape[0])) im3 = cv2.imread(OUTPUT_NAME, cv2.IMREAD_COLOR) im3 = cv2.cvtColor(im3, cv2.COLOR_BGR2YCrCb) print "Bicubic:" print cv2.PSNR(im1, im2) print "EES:" print cv2.PSNR(im1, im3)
def prepare_data(_path): names = os.listdir(_path) names = sorted(names) nums = names.__len__() data = numpy.zeros((nums * Random_Crop, 1, Patch_size, Patch_size), dtype=numpy.double) label = numpy.zeros((nums * Random_Crop, 1, label_size, label_size), dtype=numpy.double) for i in range(nums): name = _path + names[i] hr_img = cv2.imread(name, cv2.IMREAD_COLOR) shape = hr_img.shape hr_img = cv2.cvtColor(hr_img, cv2.COLOR_BGR2YCrCb) hr_img = hr_img[:, :, 0] # two resize operation to produce training data and labels lr_img = cv2.resize(hr_img, (shape[1] / scale, shape[0] / scale)) lr_img = cv2.resize(lr_img, (shape[1], shape[0])) # produce Random_Crop random coordinate to crop training img Points_x = numpy.random.randint(0, min(shape[0], shape[1]) - Patch_size, Random_Crop) Points_y = numpy.random.randint(0, min(shape[0], shape[1]) - Patch_size, Random_Crop) for j in range(Random_Crop): lr_patch = lr_img[Points_x[j]: Points_x[j] + Patch_size, Points_y[j]: Points_y[j] + Patch_size] hr_patch = hr_img[Points_x[j]: Points_x[j] + Patch_size, Points_y[j]: Points_y[j] + Patch_size] lr_patch = lr_patch.astype(float) / 255. hr_patch = hr_patch.astype(float) / 255. data[i * Random_Crop + j, 0, :, :] = lr_patch label[i * Random_Crop + j, 0, :, :] = hr_patch[conv_side: -conv_side, conv_side: -conv_side] # cv2.imshow("lr", lr_patch) # cv2.imshow("hr", hr_patch) # cv2.waitKey(0) return data, label # BORDER_CUT = 8
def predict(): srcnn_model = predict_model() srcnn_model.load_weights("3051crop_weight_200.h5") IMG_NAME = "/home/mark/Engineer/SR/data/Set14/flowers.bmp" INPUT_NAME = "input2.jpg" OUTPUT_NAME = "pre2.jpg" import cv2 img = cv2.imread(IMG_NAME, cv2.IMREAD_COLOR) img = cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb) shape = img.shape Y_img = cv2.resize(img[:, :, 0], (shape[1] / 2, shape[0] / 2), cv2.INTER_CUBIC) Y_img = cv2.resize(Y_img, (shape[1], shape[0]), cv2.INTER_CUBIC) img[:, :, 0] = Y_img img = cv2.cvtColor(img, cv2.COLOR_YCrCb2BGR) cv2.imwrite(INPUT_NAME, img) Y = numpy.zeros((1, img.shape[0], img.shape[1], 1), dtype=float) Y[0, :, :, 0] = Y_img.astype(float) / 255. pre = srcnn_model.predict(Y, batch_size=1) * 255. pre[pre[:] > 255] = 255 pre[pre[:] < 0] = 0 pre = pre.astype(numpy.uint8) img = cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb) img[6: -6, 6: -6, 0] = pre[0, :, :, 0] img = cv2.cvtColor(img, cv2.COLOR_YCrCb2BGR) cv2.imwrite(OUTPUT_NAME, img) # psnr calculation: im1 = cv2.imread(IMG_NAME, cv2.IMREAD_COLOR) im1 = cv2.cvtColor(im1, cv2.COLOR_BGR2YCrCb)[6: -6, 6: -6, 0] im2 = cv2.imread(INPUT_NAME, cv2.IMREAD_COLOR) im2 = cv2.cvtColor(im2, cv2.COLOR_BGR2YCrCb)[6: -6, 6: -6, 0] im3 = cv2.imread(OUTPUT_NAME, cv2.IMREAD_COLOR) im3 = cv2.cvtColor(im3, cv2.COLOR_BGR2YCrCb)[6: -6, 6: -6, 0] print "bicubic:" print cv2.PSNR(im1, im2) print "SRCNN:" print cv2.PSNR(im1, im3)
def EES_predict(): IMG_NAME = "./butterfly_GT.bmp" INPUT_NAME = "input.jpg" OUTPUT_NAME = "EES_pre.jpg" label = cv2.imread(IMG_NAME) shape = label.shape img = cv2.resize(label, (shape[1] / scale, shape[0] / scale), cv2.INTER_CUBIC) cv2.imwrite(INPUT_NAME, img) EES = model_EES16() EES.load_weights("EES_check.h5") img = cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb) Y = numpy.zeros((1, img.shape[0], img.shape[1], 1)) Y[0, :, :, 0] = img[:, :, 0].astype(float) / 255. img = cv2.cvtColor(label, cv2.COLOR_BGR2YCrCb) pre = EES.predict(Y, batch_size=1) * 255. pre[pre[:] > 255] = 255 pre = numpy.uint8(pre) img[:, :, 0] = pre[0, :, :, 0] img = cv2.cvtColor(img, cv2.COLOR_YCrCb2BGR) cv2.imwrite(OUTPUT_NAME, img) # psnr calculation: im1 = cv2.imread(IMG_NAME, cv2.IMREAD_COLOR) im1 = cv2.cvtColor(im1, cv2.COLOR_BGR2YCrCb) im2 = cv2.imread(INPUT_NAME, cv2.IMREAD_COLOR) im2 = cv2.cvtColor(im2, cv2.COLOR_BGR2YCrCb) im2 = cv2.resize(im2, (img.shape[1], img.shape[0])) cv2.imwrite("Bicubic.jpg", cv2.cvtColor(im2, cv2.COLOR_YCrCb2BGR)) im3 = cv2.imread(OUTPUT_NAME, cv2.IMREAD_COLOR) im3 = cv2.cvtColor(im3, cv2.COLOR_BGR2YCrCb) print "Bicubic:" print cv2.PSNR(im1[:, :, 0], im2[:, :, 0]) print "EES:" print cv2.PSNR(im1[:, :, 0], im3[:, :, 0]) ##*******************************************************************************************************************//
def prepare_crop_data(_path): names = os.listdir(_path) names = sorted(names) nums = names.__len__() data = [] label = [] for i in range(nums): name = _path + names[i] hr_img = cv2.imread(name, cv2.IMREAD_COLOR) hr_img = cv2.cvtColor(hr_img, cv2.COLOR_BGR2YCrCb) hr_img = hr_img[:, :, 0] shape = hr_img.shape # two resize operation to produce training data and labels lr_img = cv2.resize(hr_img, (shape[1] / scale, shape[0] / scale)) lr_img = cv2.resize(lr_img, (shape[1], shape[0])) width_num = (shape[0] - (BLOCK_SIZE - BLOCK_STEP) * 2) / BLOCK_STEP height_num = (shape[1] - (BLOCK_SIZE - BLOCK_STEP) * 2) / BLOCK_STEP for k in range(width_num): for j in range(height_num): x = k * BLOCK_STEP y = j * BLOCK_STEP hr_patch = hr_img[x: x + BLOCK_SIZE, y: y + BLOCK_SIZE] lr_patch = lr_img[x: x + BLOCK_SIZE, y: y + BLOCK_SIZE] lr_patch = lr_patch.astype(float) / 255. hr_patch = hr_patch.astype(float) / 255. lr = numpy.zeros((1, Patch_size, Patch_size), dtype=numpy.double) hr = numpy.zeros((1, label_size, label_size), dtype=numpy.double) lr[0, :, :] = lr_patch hr[0, :, :] = hr_patch[conv_side: -conv_side, conv_side: -conv_side] data.append(lr) label.append(hr) data = numpy.array(data, dtype=float) label = numpy.array(label, dtype=float) return data, label
