import cv2
import os
from sklearn.model_selection import train_test_split
import numpy as np
import time
from typing import List
[docs]
class FastDataset:
"""
A class for shooting and saving images in grayscale or RGB using OpenCV.
Attributes:
index (int): the index of the camera to use.
img_types (int): the number of types of images to collect.
label (List[str]): a list of strings that represent the name of the directories where the images will be saved.
num (int): the number of frames to capture for each image type.
height (int): the height of the frames to capture.
width (int): the width of the frames to capture.
standby_time (float): the time to wait before capturing each frame.
Examples:
>>> import xeye
>>> # define parameters values
>>> index = 0
>>> img_types = 2
>>> label = ['keyboard', 'mouse']
>>> num = 20
>>> height = 100
>>> width = 100
>>> standby_time = 0
>>> data = xeye.FastDataset(index = index, img_types = img_types, label = label, num = num, height = height, width = width, stand_by_time = standby_time)
>>> data.preview()
>>> data.rgb() # or data.gray()
>>> data.compress_train_test(perc=0.2)
>>> data.compress_all()
>>> data.just_compress(name="batch_test")
"""
def __init__(self, index: int, img_types: int, label: List[str], num: int, height: int, width: int, stand_by_time: float) -> None:
self.index = index
self.img_types = img_types
self.label = label
self.num = num
self.height = height
self.width = width
self._class_dict = {}
self._tensor = {}
self.standby_time = stand_by_time
self._statusGray = 0
self._statusRGB = 0
# clear terminal
if(os.name == 'posix'): # unix
os.system('clear')
else: # windows
os.system('cls')
# camera setting
if self.index == -1:
raise ValueError('(index) Insert valid camera index...')
camera = cv2.VideoCapture(self.index)
if camera.isOpened() == False:
raise ValueError('(index) Insert valid camera index...')
# set how many type of images do you want to collect
if self.img_types == 0:
raise ValueError('(img_types) Number of images types must be greather than 0')
if type(self.img_types) != int:
raise TypeError('(img_types) passed a string, not an integer')
# folder building
if self.label == []:
raise ValueError('(label) Not valid names for images types...')
if len(self.label) != self.img_types:
raise ValueError("(label) You must have a number of labels equal to the number of images types selected...")
for lab in self.label:
if not os.path.exists(lab):
os.mkdir(lab)
# number of frames
if self.num == 0 or self.num < 0:
raise ValueError('(num) You cant inizialize frames number equal or below zero...')
# Image shaping phase
if self.height == 0 or self.height < 0:
raise ValueError('(height) Frame HEIGHT must be greather than 0')
if self.width == 0 or self.width < 0:
raise ValueError('(width) Frame WIDTH must be greather than 0')
# Waiting time in shooting loop
if self.standby_time < 0:
raise ValueError('(standby_time) waiting time must be grater than 0...')
[docs]
def preview(self) -> None:
"""
Opens the camera stream on a window for checking the framing of the image.
Returns:
None
"""
print('\n')
print('--- PREVIEW ---')
camera = cv2.VideoCapture(self.index)
while(True):
status, frame = camera.read()
if not status:
print("frame doesn't been captured")
break
font = cv2.FONT_HERSHEY_COMPLEX
text = 'click on the image window and then press [q] on the keyboard to quit preview'
cv2.putText(frame,text,(5,50),font,0.8,(124,252,0),2) #text,coordinate,font,size of text,color,thickness of font
cv2.startWindowThread()
cv2.imshow("Camera PreView", frame)
if cv2.waitKey(1) == ord('q'):
print('preview closed')
camera.release()
cv2.destroyAllWindows()
cv2.waitKey(1)
break
[docs]
def gray(self) -> None:
"""
Method for shooting images in grayscale.
Returns:
None
"""
print('\n')
print('--- START TAKING PHOTOS ---')
camera = cv2.VideoCapture(self.index)
# Index for files name
i = 0
for folder in self.label:
count = 0
print(f'Press [b] on the keyboard to start data collection of image type: [{folder}]')
userinput = input()
if userinput != 'b':
print("Wrong Input...press 'b'")
while count < self.num:
status, frame = camera.read()
if not status:
print("frame doesn't been captured")
break
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cv2.startWindowThread()
cv2.imshow(f"Camera View for image type [{folder}]",gray)
gray = cv2.resize(gray, (self.width, self.height))
cv2.imwrite(folder+'/'+ str(self.label[i]) + str(count) + '.png', gray)
count=count+1
time.sleep(self.standby_time)
if cv2.waitKey(1) == ord('q'):
break
i += 1
camera.release()
cv2.destroyAllWindows()
cv2.waitKey(1)
# set status
self._statusGray = 1
self._statusRGB = 0
[docs]
def rgb(self) -> None:
"""
Method for shooting images in RGB.
Returns:
None
"""
print('\n')
print('--- START TAKING PHOTOS ---')
camera = cv2.VideoCapture(self.index)
# Index for files name
i = 0
for folder in self.label:
count = 0
print(f'Press [b] on the keyboard to start data collection of image type: [{folder}]')
userinput = input()
if userinput != 'b':
print("Wrong Input...press 'b'")
exit()
while count < self.num:
status, frame = camera.read()
if not status:
print("frame doesn't been captured")
break
cv2.startWindowThread()
cv2.imshow(f"Camera View for image type [{folder}]", frame)
frame = cv2.resize(frame, (self.width, self.height))
cv2.imwrite(folder+'/'+ str(self.label[i]) + str(count) + '.png', frame)
count=count+1
time.sleep(self.standby_time)
if cv2.waitKey(1) == ord('q'):
break
i += 1
camera.release()
cv2.destroyAllWindows()
cv2.waitKey(1)
# Set status
self._statusGray = 0
self._statusRGB = 1
[docs]
def compress_train_test(self, perc: float = 0.1) -> None:
"""
Saves the images shot in datasets divided by train and test like the mnist dataset.
Args:
perc (float): The percentage of images to assign to the test dataset.
Raises:
ValueError: If both rgb and gray functions have not been called before compressing a dataset.
ValueError: If the percentage value for images in the test set is less than or equal to 0.
Returns:
None
"""
# percentage control
if perc <= 0:
raise ValueError('(perc) Percentage value must be greater than 0...')
# data control
if self._statusRGB == 0 and self._statusGray == 0:
raise ValueError('You have to call rgb or gray function before compress a dataset...')
# index for image type
i = 0
# X
if self._statusGray == 1:
self._tensor['X'] = np.empty((0,self.height,self.width))
else:
self._tensor['X'] = np.empty((0,self.height,self.width,3))
# y
self._tensor['y'] = np.empty((0))
# (append) loop
for lab in self.label:
j = 0
if self._statusGray == 1:
self._class_dict['t'+str(i)] = np.empty((self.num, self.height, self.width))
else:
self._class_dict['t'+str(i)] = np.empty((self.num, self.height, self.width, 3))
# loop for convert image format
for file in os.listdir(lab):
if self._statusGray == 1:
img = cv2.imread(lab + '/' + file, cv2.IMREAD_GRAYSCALE)
else:
img = cv2.imread(lab + '/' + file)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# save in _tensor class
self._class_dict['t'+str(i)][j] = img
j += 1
# unique final _tensors
self._tensor['X'] = np.append(self._tensor['X'], self._class_dict['t'+str(i)], axis = 0)
self._tensor['y'] = np.append(self._tensor['y'], np.repeat(i, self.num, axis = 0))
i += 1
# create dataset (mnist style)
self._tensor['X'] = self._tensor['X'].astype('uint8')
self._tensor['y'] = self._tensor['y'].astype('uint8')
self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(self._tensor['X'], self._tensor['y'], test_size=perc, random_state=123)
np.savez('dataset.npz', X_train=self.X_train, X_test=self.X_test, y_train=self.y_train, y_test=self.y_test)
[docs]
def compress_all(self) -> None:
"""
Saves the images shot in a unique dataset.
Raises:
ValueError: If both rgb and gray functions have not been called before compressing a dataset.
Returns:
None
"""
# data control
if self._statusRGB == 0 and self._statusGray == 0:
raise ValueError('You have to call rgb or gray function before compress a dataset...')
# index for image type
i = 0
# X
if self._statusGray == 1:
self._tensor['X'] = np.empty((0,self.height,self.width))
else:
self._tensor['X'] = np.empty((0,self.height,self.width,3))
# y
self._tensor['y'] = np.empty((0))
# (append) loop
for lab in self.label:
j = 0
if self._statusGray == 1:
self._class_dict['t'+str(i)] = np.empty((self.num, self.height, self.width))
else:
self._class_dict['t'+str(i)] = np.empty((self.num, self.height, self.width, 3))
# loop for convert image format
for file in os.listdir(lab):
if self._statusGray == 1:
img = cv2.imread(lab + '/' + file, cv2.IMREAD_GRAYSCALE)
else:
img = cv2.imread(lab + '/' + file)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# save in _tensor class
self._class_dict['t'+str(i)][j] = img
j += 1
# unique final _tensors
self._tensor['X'] = np.append(self._tensor['X'], self._class_dict['t'+str(i)], axis = 0)
self._tensor['y'] = np.append(self._tensor['y'], np.repeat(i, self.num, axis = 0))
i += 1
# create dataset (mnist style)
self._tensor['X'] = self._tensor['X'].astype('uint8')
self._tensor['y'] = self._tensor['y'].astype('uint8')
np.savez('datasetall.npz', x = self._tensor['X'], y = self._tensor['y'])
[docs]
def just_compress(self, name: str = "dataset_raw") -> None:
"""
Saves the images shot in a unique dataset without saving the y variable containing the type of the single image.
Args:
name (str): The name of the dataset (npz) to be saved.
Raises:
ValueError: If both rgb and gray functions have not been called before compressing a dataset.
Returns:
None
"""
# data control
if self._statusRGB == 0 and self._statusGray == 0:
raise ValueError('You have to call rgb or gray function before compress a dataset...')
# check the name for the dataset
if len(str(name)) == 0:
raise ValueError("name: Insert a valide name for the compressed file...")
# index for image type
i = 0
# X
if self._statusGray == 1:
self._tensor['X'] = np.empty((0,self.height,self.width))
else:
self._tensor['X'] = np.empty((0,self.height,self.width,3))
# (append) loop
for lab in self.label:
j = 0
if self._statusGray == 1:
self._class_dict['t'+str(i)] = np.empty((self.num, self.height, self.width))
else:
self._class_dict['t'+str(i)] = np.empty((self.num, self.height, self.width, 3))
# loop for convert image format
for file in os.listdir(lab):
if self._statusGray == 1:
img = cv2.imread(lab + '/' + file, cv2.IMREAD_GRAYSCALE)
else:
img = cv2.imread(lab + '/' + file)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# save in _tensor class
self._class_dict['t'+str(i)][j] = img
j += 1
# unique final _tensors
self._tensor['X'] = np.append(self._tensor['X'], self._class_dict['t'+str(i)], axis = 0)
i += 1
# create dataset (mnist style)
self._tensor['X'] = self._tensor['X'].astype('uint8')
np.savez(f'{name}.npz', x = self._tensor['X'])
[docs]
def var_control(self) -> None:
"""
Print the parameters specified in the init method about the dataset to create.
"""
print('\n')
print('--- PARAMETERS CONTROL ---')
print(f'Camera index: {self.index}')
print(f'Labels of images types: {self.label}')
print(f'Num. of images for type: {self.num}')
print(f'Single frame HEIGHT: {self.height}')
print(f'Single frame WIDTH: {self.width}')