How to use test_list_images method in avocado

Best Python code snippet using avocado_python

shell.py

Source:shell.py

1from django.urls import reverse2from django.test.utils import setup_test_environment3from django.contrib.auth.models import User4from django.db.models import Q5from home.models import *6from home.search import *7# from searchengine.forms import *8# import requests9import os10import sys11import random12import cv213# from tabulate import tabulate14setup_test_environment()15from django.test import Client16c = Client()17from django.core.files import File18def populate_user_database():19    os.chdir('./static/books')20    id_=int(input('Enter ID: '))21    b=Book.objects.get(id=id_)22    os.chdir(b.title)23    os.chdir('content')24    ids=[]25    for file in os.listdir('.'):26        print(file)27        with open(file,'r') as f:28            r=f.readlines()29        r=[i.strip() for i in r]30        content='\n'.join(r)31        p=Page(pagetitle=file.strip(), content=content, book=b)32        p.save()33        ids.append(p.id)34    os.chdir('../images')35    for i in ids:36        pt='.'.join(Page.objects.get(id=i).pagetitle.split('.')[:-1])37        # pt=pt.split('.')[:-1]+'jpg'38        p=Page.objects.get(id=i)39        p.image.save(pt,File(open(pt,'rb')))40def flush_user_database():41    p = Page.objects.all()42    if len(p):43        for i in p:44            i.delete()45    b = Book.objects.all()46    if len(b):47        for i in b:48            i.delete()49def processing():50    import os51    import shutil52    import glob53    import xml.etree.ElementTree as et54    valid_image_ext = ['jpg','tif','jpeg','TIF','JPG','JPEG','PNG','png']55    cwd = os.getcwd()56    os.chdir("/home/ndlsearch19/rename_telugu/Telugu")57    book_path_folder = os.getcwd()58    book_name_lan = os.getcwd().split("/")[-1]59    60    #print(book_path_folder)61    book_list = Book.objects.all()62    test_list=[]63    for i in book_list:64        test_list.append(i.title)65    # print(test_list)66    count = 167    book_path_list = glob.glob('*')68    book_path_list = [i for i in book_path_list if os.path.isdir(i)]69    print(book_path_list)70    # cwd = os.getcwd()71    test=[]72    for book_path in book_path_list:73        print(book_path)74        print("/n/n")75        test_list_content = []76        test_list_images = []77        test_list_segment = []78        nameofthebook = book_path.split("/")[-1]79        #print(nameofthebook)80        os.chdir(book_path_folder+"/"+book_path)81        print(os.getcwd())82        images = []83        for i in valid_image_ext:84            images += glob.glob('Images/*.{}'.format(i))85        images = list(set(images))86        # print(images)87        content = glob.glob('Predictions_CRNN/*.txt')88        # print(content)89        #print(len(content))90        #print(len(images))91        segment = glob.glob('Segmentations/*.txt') 92        for i in images:93            i=i.split(".")[0].split("/")[-1]94            test_list_images.append(i)95        if len(content)!=0 and len(content) is not None: 96            for i in content:97                i = i.split(".")[0].split("/")[-1]98                test_list_content.append(i)99        else:100            print("No predictions")101        for i in segment:102            i = i.split(".")[0].split("/")[-1]103            test_list_segment.append(i)104        105        106        107        common_list = list(set(test_list_content) & (set(test_list_images)))108                # common_list_2 = list(set(test_list_content) & (set(test_list_segment)))109        110        print("common list")111        print(len(common_list))112        dir_name_img = os.getcwd() + "/" + "Images"113        dir_name_cont = os.getcwd() + "/" + "Predictions_CRNN"114        dir_name_seg = os.getcwd() + "/" + "Segmentations"115        116        for i in test_list_images:117            if i not in common_list:118                test_list_images.remove(i)119        for i in test_list_content:120            if i not in common_list:121                test_list_content.remove(i)122        for i in test_list_segment:123            if i not in common_list:124                test_list_segment.remove(i)125                                            126        test1=[]127        test2=[]128        test3=[]129        for i in test_list_images:130            i = i + "." + "png" 131            test1.append(i)132        for i in test_list_content:133            i = i + "." + "png.txt"134            test2.append(i)135        for i in test_list_segment:136            i = i + "." + "png.lines.txt"137            test3.append(i)138        for i in images : 139            if i.split("/")[-1] not in test1:140                images.remove(i)141                os.remove(os.path.join(dir_name_img,i.split("/")[-1]))142        for i in content : 143            if i.split("/")[-1] not in test2:144                content.remove(i)145                os.remove(os.path.join(dir_name_cont, i.split("/")[-1]))146        for i in segment: 147            if i.split("/")[-1] not in test3:148                segment.remove(i)149                os.remove(os.path.join(dir_name_seg, i.split("/")[-1]))150        print(len(images))151        print(len(content))152        print(len(segment))153        print(count)154        count+=1155        assert len(content) == len(images)156        #print(len(content[0].split('_')))157        if len(content[0].split('_')) > 2:158            content = ['_'.join(i.split('_')[1:]) for i in content]159            #print(content)160        root = et.parse('./META.XML').getroot()161        print(root)162        meta = {i.tag:i.text for i in root}163        164        if "title" not in meta.keys():165            meta["title"] = None166        167        if "totalpages" not in meta.keys():168            meta["totalpages"] = None169        if meta["title"] is None:170            meta["title"] = "NDLI_"+book_name_lan+str(count)171            count+=1172        #print(count)173        meta["title"] = meta["title"].lower()174        meta["title"] = meta["title"].title()175        if str(meta["title"]) in test_list :176            print("Already Present")177        else:178            if "creator" not in meta.keys():179                meta["creator"] = None180           181            if "digitalrepublisher" not in meta.keys():182                meta["digitalrepublisher"]= None183            if 'creator1' not in meta.keys() or meta["creator1"] is None:184                meta["creator1"] = "Anonymous"185            if meta["creator"] is None:186                meta["creator"]= meta["creator1"]187                188            if meta["digitalrepublisher"] is None:189                meta["digitalrepublisher"] = "Digital Library Of India"190            191            if "subject" not in meta.keys():192                meta["subject"] = "Unavailable"193            194            if meta["digitalrepublisher"] == "PAR Informatics, Hyderabad" or meta["digitalrepublisher"]=="UDL T.T.D, TIRUPATHI"  or meta["digitalrepublisher"]=="UDL TTD TIRUPATHI" or meta["digitalrepublisher"]=="PAR INFORMATICS,HYDERBAD" or meta["digitalrepublisher"]=="UDL T.T.D.Tirupati" or meta["digitalrepublisher"]=="UDL , T.T.D. TIRUPATI" or meta["digitalrepublisher"]=="UDL TTD TIRUPATI" or meta["digitalrepublisher"]=="PAR INFORMATICS,HYD" or meta["digitalrepublisher"]=="<>" or meta['digitalrepublisher']=="Sarvesh Mishra" or meta["digitalrepublisher"]=="UDL TTD TIRUPATI" or meta["digitalrepublisher"]=="Udl Ttd Tirupathi":195                meta["digitalrepublisher"] = "Others"196            if meta["subject"] is None or meta["subject"]=="<>" or meta['subject'] == "&lt;&gt;" or meta["subject"]=="-" or meta["subject"]=="&lt;enter subject of the book&gt;" or meta["subject"]=="<enter subject of the book>":197                meta["subject"] = "Unavailable"198            if meta["subject"]=="RELIGION" or meta['subject']=="Religion" or meta['subject']=="HINDUISM " or meta["subject"]=="hinduism" or meta["subject"]=="HINDUISM" or meta["subject"]=="RELIGION. THEOLOGY" or meta["subject"]=="Religion. Theology":199                meta["subject"] = "Religion and Theology"200            if meta["subject"]=="Literature" or meta["subject"]=="Litrature" or meta["subject"]=="Nobel" or meta["subject"]=="Novel" or meta["subject"] == "Hindi literature" or meta["subject"]=="Hindi Literature" or meta["subject"]=="LITERATURE" or meta["subject"]=="Hindi Literature" or meta["subject"]=="Literature " or meta["subject"]=="Language" or meta["subject"]=="Drama" or meta["subject"]=='drama'  or meta["subject"]=="Hindi drama" or meta["subject"]=="Hindi Drama" or meta["subject"]=="poetry" or meta["subject"]=="lictraturl" or meta["subject"]=="Story" or meta["subject"]=="literature" or meta['subject']=="poetry" or meta["subject"]=="Hindi poetry" or meta["subject"]=="Poetry":201                meta["subject"] = "Language. Linguistics. Literature"202            if meta["subject"]=="Others " or meta["subject"]=="OTHERS":203                meta["subject"]="Digital Library Of India"204            if meta["barcode"] is None:205                meta["barcode"] = "Unavailable"206            207            if meta["totalpages"] is None:208                meta["totalpages"] = "-"209            b = Book(210                isbn= meta['barcode'],211                title=meta['title'].lower().rstrip().title(),212                author=meta['creator'].lower().rstrip().title(),213                language=meta['language'].lower().rstrip().title(),214                genre=meta['subject'].lower().rstrip().title(),215                source=meta['digitalrepublisher'].lower().rstrip().title(),216                numpages = meta["totalpages"]217            )218            shutil.make_archive('Images','zip','.',base_dir='Images')219            shutil.make_archive('Predictions_CRNN','zip','.',base_dir='Predictions_CRNN')220            shutil.make_archive('Segmentations','zip','.',base_dir='Segmentations')221            image_zip = os.path.abspath('Images.zip')222            content_zip= os.path.abspath('Predictions_CRNN.zip')223            segment_zip= os.path.abspath('Segmentations.zip')224            os.chdir(cwd)225            b.book_pdf.save('Images.zip',File(open(image_zip,'rb')), save=False)226            b.book_content.save('Predictions_CRNN.zip', File(open(content_zip,'rb')),save=False)227            b.book_segment.save('Segmentations.zip', File(open(segment_zip,'rb')),save=False)228            os.remove(image_zip)229            os.remove(content_zip)230            os.remove(segment_zip)231            b.save()232            print(meta)233            print(os.getcwd())...

test.py

Source:test.py

...11run = 'test_api'12host = 'http://121.199.9.187:8085'13class TestCase(unittest.TestCase):14    @unittest.skipUnless(run == 'test_list_images', 'reason')15    def test_list_images(self):16        p = processor.AliyunImageServiceProcessor("")17        r = p.queryImages(None, None, None, None, None, None, None, None, None, None, None, None, None,)18        print('test_list_images', r, len(r))19    @unittest.skipUnless(run == 'test_create_image', 'reason')20    def test_create_image(self):21        p = processor.AliyunImageServiceProcessor("")22        r = p.createImage(None, None, 'NewImage', 's-62ev59pgw')23        print('test_create_image', r)24    @unittest.skipUnless(run == 'test_query_image_by_id', 'reason')25    def test_query_image_by_id(self):26        p = processor.AliyunImageServiceProcessor("")27        r = p.queryImageId('coreos681_64_20G_aliaegis_20150618.vhd')28        print('test_query_image_by_id', r)29    @unittest.skipUnless(run == 'test_delete_image_by_id', 'reason')...

main.py

Source:main.py

1from model import Model2from layers.activation import Activation3from layers.max_pooling import MaxPooling4from layers.convolution_layer import ConvolutionLayer5from layers.dense_layer import DenseLayer6from layers.flatten_layer import FlattenLayer7from sklearn.model_selection import train_test_split8import os9os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' 10import tensorflow as tf11import numpy as np12from tensorflow import keras13from tensorflow.keras import preprocessing14from tensorflow.keras.preprocessing import image_dataset_from_directory15from tensorflow.keras.preprocessing.image import img_to_array16import pickle17train_ds = image_dataset_from_directory(18        directory='train/',19        labels='inferred',20        label_mode='int',21        batch_size=40,22        shuffle=False,23        image_size=(100, 100))24test = image_dataset_from_directory(25        directory='test/',26        labels='inferred',27        label_mode='int',28        batch_size=40,29        shuffle=False,30        image_size=(100, 100))31train_list_images = []32train_list_labels=[]33for images, labels in train_ds.take(1):34    # print(labels[0].numpy())        35    for i in range(len(images)):36        train_list_images.append(images[i].numpy()*(1/255))37        train_list_labels.append(labels[i].numpy())38test_list_images = []39test_list_labels=[]40for images, labels in test.take(1):41    # print(labels[0].numpy())        42    for i in range(len(images)):43        test_list_images.append(images[i].numpy()*(1/255))44        test_list_labels.append(labels[i].numpy())45model = Model()46model.add(ConvolutionLayer(inputs_size=(100,100,3), padding=0, n_filter=2, filter_size=(3,3), n_stride=1))47model.add(Activation())48model.add(MaxPooling((2,2), 2))49model.add(FlattenLayer())50model.add(DenseLayer(units=8, activation='relu'))51model.add(DenseLayer(units=1, activation='sigmoid'))52# for image in list_images:53#     prediction = model.forward(image)54#     print("prediction : " + str(prediction))55# prediction = model.forward(list_images[0])56from sklearn.metrics import accuracy_score57# split traing 90%58X_train, X_test, y_train, y_test = train_test_split(train_list_images, train_list_labels, test_size=0.1)59model.fit(X_train,y_train,2,3,0.1,0.1)60y_predict = np.zeros(len(y_test))61for i, image in enumerate(X_test):62    predict = model.forward(image)63    if (predict[0] > 0.5):64        y_predict[i] = 165    else:66        y_predict[i] = 067accuracy = accuracy_score(y_test,y_predict)68print(accuracy)69# save the model to disk70filename = 'finalized_model.sav'71pickle.dump(model, open(filename, 'wb'))72 73# load the model from disk74loaded_model = pickle.load(open(filename, 'rb'))75y_predict = np.zeros(len(y_test))76for i, image in enumerate(X_test):77    predict = loaded_model.forward(image)78    if (predict[0] > 0.5):79        y_predict[i] = 180    else:81        y_predict[i] = 082accuracy = accuracy_score(y_test,y_predict)83print(accuracy)84# using data test85y_predict = np.zeros(len(test_list_images))86for i, image in enumerate(test_list_images):87    predict = loaded_model.forward(image)88    if (predict[0] > 0.5):89        y_predict[i] = 190    else:91        y_predict[i] = 092accuracy = accuracy_score(test_list_labels,y_predict)...

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