How to use pre_tests method in avocado

Best Python code snippet using avocado_python

utils.py

Source:utils.py

1import os2import cv23import numpy as np4from PIL import Image5def load_train(image_size=33, stride=33, scale=3,dirname=r'dataset\train'):6    dir_list = os.listdir(dirname)7    images = [cv2.cvtColor(cv2.imread(os.path.join(dirname,img)),cv2.COLOR_BGR2GRAY) for img in dir_list]8    #==========================9    #è¿éå¤æéæ ·æ¥é¿ æ¯å¦è½è¢«æ´é¤10    #=========================11    images = [img[0:img.shape[0]-np.remainder(img.shape[0],scale),0:img.shape[1]-np.remainder(img.shape[1],scale)] for img in images]12    trains = images.copy()13    labels = images.copy()14    #========================================15    #å¯¹train image è¿è¡æ¹æ³ç¼©å° äº§çä¸æ¸æ°çå¾å16    #========================================17    trains = [cv2.resize(img, None, fx=1/scale, fy=1/scale, interpolation=cv2.INTER_CUBIC) for img in trains]18    trains = [cv2.resize(img, None, fx=scale/1, fy=scale/1, interpolation=cv2.INTER_CUBIC) for img in trains]19    sub_trains = []20    sub_labels = []21    22    #========================================23    #éè¿éæ ·å½¢ææ ç¾ åè®ç»æ°æ®ï¼24    # ä¸å¼  å¾ç éè¿éæ ·ï¼å¯ä»¥åæå¾å¤ä¸ªå¾ååï¼ä½ä¸ºè®ç»æ°æ®ï¼ä¸°å¯æ ·æ¬25    #========================================26    for train, label in zip(trains, labels):27        v, h = train.shape28        print(train.shape)29        for x in range(0,v-image_size+1,stride):30            for y in range(0,h-image_size+1,stride):31                sub_train = train[x:x+image_size,y:y+image_size]32                sub_label = label[x:x+image_size,y:y+image_size]33                sub_train = sub_train.reshape(image_size,image_size,1)34                sub_label = sub_label.reshape(image_size,image_size,1)35                sub_trains.append(sub_train)36                sub_labels.append(sub_label)37    #========================================38    #ç¼ç ä¸ºnumpy array39    #========================================40    sub_trains = np.array(sub_trains)41    sub_labels = np.array(sub_labels)42    return sub_trains, sub_labels43def load_test(scale=3,dirname=r'dataset\test'):44    #========================================45    # çææµè¯æ°æ®çæ¹å¼ä¸è®ç»æ°æ®ç¸å46    # pre_tests æ¯ç¨æ¥ä¿åç¼©å°åçå¾ç47    #========================================48    dir_list = os.listdir(dirname)49    images = [cv2.cvtColor(cv2.imread(os.path.join(dirname,img)),cv2.COLOR_BGR2GRAY) for img in dir_list]50    images = [img[0:img.shape[0]-np.remainder(img.shape[0],scale),0:img.shape[1]-np.remainder(img.shape[1],scale)] for img in images]51    tests = images.copy()52    labels = images.copy()53    54    pre_tests = [cv2.resize(img, None, fx=1/scale, fy=1/scale, interpolation=cv2.INTER_CUBIC) for img in tests]55    tests = [cv2.resize(img, None, fx=scale/1, fy=scale/1, interpolation=cv2.INTER_CUBIC) for img in pre_tests]56    57    pre_tests = [img.reshape(img.shape[0],img.shape[1],1) for img in pre_tests]58    tests = [img.reshape(img.shape[0],img.shape[1],1) for img in tests]59    labels = [img.reshape(img.shape[0],img.shape[1],1) for img in labels]60    return pre_tests, tests, labels61#========================================62# ä¸é¢å½æ°ç¨æ¥è®¡ç®éæååçå¾çææ 63#========================================64def mse(y, t):65    return np.mean(np.square(y - t))66def psnr(y, t):67    return 20 * np.log10(255) - 10 * np.log10(mse(y, t))68def ssim(x, y):69    mu_x = np.mean(x)70    mu_y = np.mean(y)71    var_x = np.var(x)72    var_y = np.var(y)73    cov = np.mean((x - mu_x) * (y - mu_y))74    c1 = np.square(0.01 * 255)75    c2 = np.square(0.03 * 255)...

lib.py

Source:lib.py

1import os2import cv23import numpy as np4from PIL import Image5import sys6import matplotlib.pyplot as plt7#Two simple function to load the training and testing data8def load_train(image_size=33, stride=33, scale=3, dim=3, load_txt=False):9    dirname = './data'10    dir_list = os.listdir(dirname)11    images = []12    #load the data13    images = [cv2.imread(os.path.join(dirname,img)) for img in dir_list if img != ".DS_Store"]14    images = [img[0:img.shape[0]-np.remainder(img.shape[0],scale),0:img.shape[1]-np.remainder(img.shape[1],scale)] for img in images]15    X_train = images.copy()16    Y_train = images.copy()17    #downsample and upsample to create the LR images18    X_train = [cv2.resize(img, None, fx=1/scale, fy=1/scale, interpolation=cv2.INTER_CUBIC) for img in X_train]19    X_train = [cv2.resize(img, None, fx=scale/1, fy=scale/1, interpolation=cv2.INTER_CUBIC) for img in X_train]20    sub_X_train  = []21    sub_Y_train = []22    #Creating the sub images of 33x33 23    for train, label in zip(X_train, Y_train):24        v = train.shape[0]25        h = train.shape[1]26        for x in range(0,v-image_size+1,stride):27            for y in range(0,h-image_size+1,stride):28                sub_train = train[x:x+image_size,y:y+image_size]29                sub_label = label[x:x+image_size,y:y+image_size]30                if dim == 3: 31                    sub_train = sub_train.reshape(image_size,image_size,3)32                    sub_label = sub_label.reshape(image_size,image_size,3)33                else:34                    sub_train = sub_train.reshape(image_size,image_size,1)35                    sub_label = sub_label.reshape(image_size,image_size,1)36                sub_X_train.append(sub_train)37                sub_Y_train.append(sub_label)38                # ========= VERIFICATION ===========39                # cv2.imshow('image',sub_train)40                # cv2.waitKey(0)41    #convert to numpy array42    sub_X_train = np.array(sub_X_train)43    sub_Y_train = np.array(sub_Y_train)44    return sub_X_train, sub_Y_train45def load_test(scale=3, dim=3):46    dirname = './input/'47    dir_list = os.listdir(dirname)48    #load the data49    images = [cv2.cvtColor(cv2.imread(os.path.join(dirname,img)),cv2.IMREAD_COLOR) for img in dir_list if img != ".DS_Store"]50    images = [img[0:img.shape[0]-np.remainder(img.shape[0],scale),0:img.shape[1]-np.remainder(img.shape[1],scale)] for img in images]51    X_test = images.copy()52    Y_test = images.copy()53    #downsample and upsample to create the LR images54    pre_tests = [cv2.resize(img, None, fx=1/scale, fy=1/scale, interpolation=cv2.INTER_CUBIC) for img in X_test]55    X_test = [cv2.resize(img, None, fx=scale/1, fy=scale/1, interpolation=cv2.INTER_CUBIC) for img in pre_tests]56    #reshape images to add the third channel57    if dim == 3: 58        pre_tests = [img.reshape(img.shape[0],img.shape[1],3) for img in pre_tests]59        X_test = [img.reshape(img.shape[0],img.shape[1],3) for img in X_test] 60        Y_test = [img.reshape(img.shape[0],img.shape[1],3) for img in Y_test] 61    else:62        pre_tests = [img.reshape(img.shape[0],img.shape[1],1) for img in pre_tests]63        X_test = [img.reshape(img.shape[0],img.shape[1],1) for img in X_test] 64        Y_test = [img.reshape(img.shape[0],img.shape[1],1) for img in Y_test]  ...

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