test
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import numpy as np
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import pandas as pd
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import os
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from skimage.io import imread
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import cv2 as cv
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from pathlib import Path
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import random
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from shutil import copyfile, rmtree
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import json
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import seaborn as sns
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import matplotlib.pyplot as plt
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import matplotlib
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from keras.preprocessing import image
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from sklearn.model_selection import GridSearchCV, train_test_split
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import warnings
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warnings.filterwarnings('ignore')
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pd.set_option('display.max_columns', 30*30 + 1)
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import tensorflow as tf
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def load_train_images(container_path, newSize=(64, 64), interpol=cv.INTER_AREA, colorConv=None):
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'''
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Obraz jest wczytywany przez funkcję imread z skimage (zwraca ndr)
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:param container_path: katalog z podkatalogami oznaczającymi kategorie do klasyfikacji
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:param newSize: docelowey rozmiar obrazu dla funkcji resize z OpenCV
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:param interpol: typ interpolacji rozmiaru obrazu dla funkcji resize z OpenCV
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:param colorConv: stała konwersji - konversja przez funkcję cvtColor z OpenCV
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:return:
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'''
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image_dir = Path(container_path)
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categories_name = []
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for file in os.listdir(image_dir):
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d = os.path.join(image_dir, file)
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if os.path.isdir(d):
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categories_name.append(file)
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folders = [directory for directory in image_dir.iterdir() if directory.is_dir()]
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train_img = []
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categories_count=[]
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labels=[]
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for i, direc in enumerate(folders):
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count = 0
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for obj in direc.iterdir():
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if os.path.isfile(obj) and os.path.basename(os.path.normpath(obj)) != 'desktop.ini':
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labels.append(os.path.basename(os.path.normpath(direc)))
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count += 1
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img = imread(obj)#zwraca ndarry postaci xSize x ySize x colorDepth
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if colorConv:# przykładowo: cv.COLOR_BGR2GRAY
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img = cv.cvtColor(img, colorConv)# zwraca ndarray, , domyślnie rgb zwraca jako bgr
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img = cv.resize(img, newSize, interpolation=interpol)# zwraca ndarray
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img = img / 255#normalizacja
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train_img.append(img)
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categories_count.append(count)
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X={}
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X["data"] = np.array(train_img)
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X["categories_name"] = categories_name
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X["categories_count"] = categories_count
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X["labels"]=labels
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return X
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def load_test_images(container_path, newSize=(64, 64), interpol=cv.INTER_AREA, colorConv=None):
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'''
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Obraz jest wczytywany przez funkcję imread z skimage (zwraca ndr)
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:param container_path: katalog z podkatalogami oznaczającymi kategorie do klasyfikacji
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:param newSize: docelowey rozmiar obrazu dla funkcji resize z OpenCV
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:param interpol: typ interpolacji rozmiaru obrazu dla funkcji resize z OpenCV
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:param colorConv: stała konwersji - konversja przez funkcję cvtColor z OpenCV
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:return:
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'''
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image_path = Path(container_path)
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labels_path = image_path.parents[0] / 'test_labels.json'
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#with labels_path.open("r", encoding ="utf-8") as f:
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jsonString = labels_path.read_text()
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objects = json.loads(jsonString)
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print(objects)
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categories_name = []
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categories_count=[]
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count = 0
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c = objects[0]['value']
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for e in objects:
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if e['value'] != c:
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print(count)
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print(c)
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categories_count.append(count)
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c = e['value']
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count = 1
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else:
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count += 1
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if not e['value'] in categories_name:
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categories_name.append(e['value'])
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categories_count.append(count)
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test_img = []
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labels=[]
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for e in objects:
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p = image_path / e['filename']
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img = imread(p)#zwraca ndarry postaci xSize x ySize x colorDepth
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if colorConv:# przykładowo: cv.COLOR_BGR2GRAY
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img = cv.cvtColor(img, colorConv)# zwraca ndarray, , domyślnie rgb zwraca jako bgr
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img = cv.resize(img, newSize, interpolation=interpol)# zwraca ndarray
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img = img / 255#normalizacja
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test_img.append(img)
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labels.append(e['value'])
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X={}
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X["data"] = np.array(test_img)
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X["categories_name"] = categories_name
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X["categories_count"] = categories_count
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X["labels"]=labels
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return X
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def gen_train_test(container_dir):
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'''
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Zwraca podkatalogi train i test wewnątrz katalogu z listą podkatalogów oznaczających katagorie
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Podkatalog train ma strukturę identyczną z oryginalną (podkatalogi kategorii)
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Podkatalog test zawiera listę plików testowych i plik .csv z dwukolumnową kolumną: nazwa pliku, nazwa klasy
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:param X:
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:return:
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'''
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image_path = Path(container_dir)
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categories_name = []
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for file in os.listdir(image_path):
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d = os.path.join(image_path, file)
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if os.path.isdir(d):
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categories_name.append(file)
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folders = [directory for directory in image_path.iterdir() if directory.is_dir()]#zwraca listę obiektów Path
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train_test_image_path = image_path.absolute().parents[0] / 'train_test_sw'
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if not train_test_image_path.is_dir():
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train_test_image_path.mkdir(parents=True, exist_ok=True)
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train_image_path = train_test_image_path / 'train_sw'
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test_image_path = train_test_image_path / 'test_sw'
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if train_image_path.is_dir():
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rmtree(train_image_path)
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if test_image_path.is_dir():
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rmtree(test_image_path)
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train_image_path.mkdir(parents=True, exist_ok=True)
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test_image_path.mkdir(parents=True, exist_ok=True)
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labels = []
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for i, direc in enumerate(folders):
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lf = list(direc.glob('*'))
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count = len(lf)
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train_filenames = random.sample(lf, int(0.8*count))
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train_category_image_path = train_image_path / direc.parts[len(direc.parts)-1]
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train_category_image_path.mkdir(parents=True, exist_ok=True)
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for src_path in train_filenames:
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dest_path = train_image_path / src_path.parts[len(src_path.parts)-2] / src_path.parts[len(src_path.parts)-1]
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copyfile(src_path,dest_path)
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for src_path in direc.glob('*'):
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if not src_path in train_filenames:
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dest_path = test_image_path / src_path.parts[len(src_path.parts)-1]
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copyfile(src_path,dest_path)
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labels.append({"filename": src_path.parts[len(src_path.parts)-1], "value":src_path.parts[len(src_path.parts)-2]})
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print(dest_path)
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jsonString = json.dumps(labels, indent=4)
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print(jsonString)
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json_path = train_test_image_path / "test_labels.json"
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with json_path.open("w", encoding ="utf-8") as f:
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f.write(jsonString)
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print("koniec")
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def show_image_corelations(X):
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matplotlib.use('TkAgg')
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# Apply the default theme
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sns.set_theme()
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sns.relplot(
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data=X,
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x="categories_name", y="categories_count",
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)
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plt.show()
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def show_categories(X, dim = (2,0,1)):
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'''
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Wizualizacja kategorii: 1D,2D
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:param X: słownik zwracany przez funkcję load_images
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:param dim: wymiar i współrzędne wizualizacji - krotka (n,mx,my): n - wymiar, mx,my: wizualizowane indeksy składowych obrazu
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(1,0,0) - dane monochromatyczne - wizualizcja 1D
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(2,mx,my) - dane 3D lub 4D, (mx,my) dwa indeksy piksela obrazu ze zbioru {0,1,2}
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:return:
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'''
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if dim == (1,0,0):
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#wizualizacja 1D
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print("test")
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else:
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#wizualizacja 2D danych X["data"]
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print("test")
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