Image augmentation for randomly rotating, shearing, translating and changing lighting conditions.

Available items

The developer of this repository has not created any items for sale yet. Need a bug fixed? Help with integration? A different license? **Create a request here**:

Readme

In many deep learning applications, we often come across data sets where one type of data may be seen more than other types. For example, in a traffic sign identification task, there may be more stop signs than speed limit signs. Therefore, in these cases, we need to make sure that the trained model is not biased towards the class that has more data. As an example, consider a data set where there are 5 speed limit signs and 20 stop signs. If the model predicts all signs to be stop signs, its accuracy is 80%. Further, f1-score of such a model is 0.88. Therefore, the model has high tendency to be biased toward the 'stop' sign class. In such cases, additional data can be generated to make the size of data sets similar.

One way to collect more data is to take the picture of the same sign from different angles. This can be done easily in openCV by applying affine transformations, such as rotations, translations and shearing. Affine transformations are transformations where the parallel lines before transformation remain parallel after transformation.

Below I present a function that can be used to generate jittered images. The function takes in the original image, range of angle rotation, range of translation and range of shearing and returns a jittered image. As the function chooses true transformation values from a uniform distribution that is specified by these ranges. I also added brighness augmentation which can be chosen to be on or off by setting the brighness flag in transform_image() function.

Note, the same techniques can be applied using image generator in Keras. However, the transform_image() function provided here will help you play with the parameters and see whats happening under the hood.

#importing some useful packages import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec import cv2import numpy as np %matplotlib inline import matplotlib.image as mpimg

def augment_brightness_camera_images(image): image1 = cv2.cvtColor(image,cv2.COLOR_RGB2HSV) random_bright = .25+np.random.uniform() #print(random_bright) image1[:,:,2] = image1[:,:,2]*random_bright image1 = cv2.cvtColor(image1,cv2.COLOR_HSV2RGB) return image1def transform_image(img,ang_range,shear_range,trans_range,brightness=0): ''' This function transforms images to generate new images. The function takes in following arguments, 1- Image 2- ang_range: Range of angles for rotation 3- shear_range: Range of values to apply affine transform to 4- trans_range: Range of values to apply translations over.

`A Random uniform distribution is used to generate different parameters for transformation ''' # Rotation ang_rot = np.random.uniform(ang_range)-ang_range/2 rows,cols,ch = img.shape Rot_M = cv2.getRotationMatrix2D((cols/2,rows/2),ang_rot,1) # Translation tr_x = trans_range*np.random.uniform()-trans_range/2 tr_y = trans_range*np.random.uniform()-trans_range/2 Trans_M = np.float32([[1,0,tr_x],[0,1,tr_y]]) # Shear pts1 = np.float32([[5,5],[20,5],[5,20]]) pt1 = 5+shear_range*np.random.uniform()-shear_range/2 pt2 = 20+shear_range*np.random.uniform()-shear_range/2 # Brightness pts2 = np.float32([[pt1,5],[pt2,pt1],[5,pt2]]) shear_M = cv2.getAffineTransform(pts1,pts2) img = cv2.warpAffine(img,Rot_M,(cols,rows)) img = cv2.warpAffine(img,Trans_M,(cols,rows)) img = cv2.warpAffine(img,shear_M,(cols,rows)) if brightness == 1: img = augment_brightness_camera_images(img) return img`

image = mpimg.imread('stopsign.jpg') plt.imshow(image); plt.axis('off');

gs1 = gridspec.GridSpec(10, 10) gs1.update(wspace=0.01, hspace=0.02) # set the spacing between axes. plt.figure(figsize=(12,12)) for i in range(100): ax1 = plt.subplot(gs1[i]) ax1.set_xticklabels([]) ax1.set_yticklabels([]) ax1.set_aspect('equal') img = transform_image(image,20,10,5,brightness=1)`plt.subplot(10,10,i+1) plt.imshow(img) plt.axis('off')`

plt.show()