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‎LICENSE‎

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Apache License
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‎aug_util.py‎

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"""
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Copyright 2018 Defense Innovation Unit Experimental
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All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import numpy as np
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from PIL import Image
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import tensorflow as tf
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from PIL import Image, ImageDraw
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import skimage.filters as filters
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"""
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Image augmentation utilities to be used for processing the dataset. Importantly, these utilities modify
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the images as well as their respective bboxes (for example, in rotation). Includes:
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rotation, shifting, salt-and-pepper, gaussian blurring. Also includes a 'draw_bboxes' function
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for visualizing augmented images and bboxes
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"""
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def rotate_image_and_boxes(img, deg, pivot, boxes):
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"""
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Rotates an image and corresponding bounding boxes. Bounding box rotations are kept axis-aligned,
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so multiples of non 90-degrees changes the area of the bounding box.
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Args:
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img: the image to be rotated in array format
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deg: an integer representing degree of rotation
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pivot: the axis of rotation. By default should be the center of an image, but this can be changed.
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boxes: an (N,4) array of boxes for the image
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Output:
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Returns the rotated image array along with correspondingly rotated bounding boxes
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"""
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if deg < 0:
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deg = 360-deg
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deg = int(deg)
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angle = 360-deg
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padX = [img.shape[0] - pivot[0], pivot[0]]
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padY = [img.shape[1] - pivot[1], pivot[1]]
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imgP = np.pad(img, [padY, padX, [0,0]], 'constant').astype(np.uint8)
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#scipy ndimage rotate takes ~.7 seconds
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#imgR = ndimage.rotate(imgP, angle, reshape=False)
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#PIL rotate uses ~.01 seconds
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imgR = Image.fromarray(imgP).rotate(angle)
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imgR = np.array(imgR)
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theta = deg * (np.pi/180)
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R = np.array([[np.cos(theta),-np.sin(theta)],[np.sin(theta),np.cos(theta)]])
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# [(cos(theta), -sin(theta))] DOT [xmin, xmax] = [xmin*cos(theta) - ymin*sin(theta), xmax*cos(theta) - ymax*sin(theta)]
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# [sin(theta), cos(theta)] [ymin, ymax] [xmin*sin(theta) + ymin*cos(theta), xmax*cos(theta) + ymax*cos(theta)]
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newboxes = []
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for box in boxes:
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xmin, ymin, xmax, ymax = box
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#The 'x' values are not centered by the x-center (shape[0]/2)
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#but rather the y-center (shape[1]/2)
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xmin -= pivot[1]
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xmax -= pivot[1]
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ymin -= pivot[0]
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ymax -= pivot[0]
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bfull = np.array([ [xmin,xmin,xmax,xmax] , [ymin,ymax,ymin,ymax]])
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c = np.dot(R,bfull)
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c[0] += pivot[1]
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c[0] = np.clip(c[0],0,img.shape[1])
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c[1] += pivot[0]
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c[1] = np.clip(c[1],0,img.shape[0])
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if np.all(c[1] == img.shape[0]) or np.all(c[1] == 0):
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c[0] = [0,0,0,0]
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if np.all(c[0] == img.shape[1]) or np.all(c[0] == 0):
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c[1] = [0,0,0,0]
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newbox = np.array([np.min(c[0]),np.min(c[1]),np.max(c[0]),np.max(c[1])]).astype(np.int64)
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if not (np.all(c[1] == 0) and np.all(c[0] == 0)):
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newboxes.append(newbox)
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return imgR[padY[0] : -padY[1], padX[0] : -padX[1]], newboxes
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def shift_image(image,bbox):
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"""
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Shift an image by a random amount on the x and y axis drawn from discrete
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uniform distribution with parameter min(shape/10)
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Args:
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image: the image to be shifted in array format
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bbox: an (N,4) array of boxes for the image
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Output:
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The shifted image and corresponding boxes
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"""
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shape = image.shape[:2]
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maxdelta = min(shape)/10
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dx,dy = np.random.randint(-maxdelta,maxdelta,size=(2))
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newimg = np.zeros(image.shape,dtype=np.uint8)
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nb = []
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for box in bbox:
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xmin,xmax = np.clip((box[0]+dy,box[2]+dy),0,shape[1])
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ymin,ymax = np.clip((box[1]+dx,box[3]+dx),0,shape[0])
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#we only add the box if they are not all 0
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if not(xmin==0 and xmax ==0 and ymin==0 and ymax ==0):
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nb.append([xmin,ymin,xmax,ymax])
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newimg[max(dx,0):min(image.shape[0],image.shape[0]+dx),
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max(dy,0):min(image.shape[1],image.shape[1]+dy)] = \
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image[max(-dx,0):min(image.shape[0],image.shape[0]-dx),
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max(-dy,0):min(image.shape[1],image.shape[1]-dy)]
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return newimg, nb
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def salt_and_pepper(img,prob=.005):
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"""
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Applies salt and pepper noise to an image with given probability for both.
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Args:
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img: the image to be augmented in array format
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prob: the probability of applying noise to the image
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Output:
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Augmented image
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"""
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newimg = np.copy(img)
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whitemask = np.random.randint(0,int((1-prob)*200),size=img.shape[:2])
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blackmask = np.random.randint(0,int((1-prob)*200),size=img.shape[:2])
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newimg[whitemask==0] = 255
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newimg[blackmask==0] = 0
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return newimg
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def gaussian_blur(img, max_sigma=1.5):
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"""
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Use a gaussian filter to blur an image
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Args:
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img: image to be augmented in array format
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max_sigma: the maximum variance for gaussian blurring
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Output:
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Augmented image
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"""
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return filters.gaussian(img,np.random.random()*max_sigma,multichannel=True)*255
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def draw_bboxes(img,boxes):
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"""
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A helper function to draw bounding box rectangles on images
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Args:
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img: image to be drawn on in array format
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boxes: An (N,4) array of bounding boxes
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Output:
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Image with drawn bounding boxes
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"""
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source = Image.fromarray(img)
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draw = ImageDraw.Draw(source)
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w2,h2 = (img.shape[0],img.shape[1])
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idx = 0
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for b in boxes:
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xmin,ymin,xmax,ymax = b
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for j in range(3):
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draw.rectangle(((xmin+j, ymin+j), (xmax+j, ymax+j)), outline="red")
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return source

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