import numpy as np

import urllib

import cv2

import json

import boto3

AWS_SRC_BUCKET_NAME = 'hackdaysawsmobileapp-userfiles-mobilehub-830251747'

AWS_DEST_BUCKET_NAME = 'hackdaysawsmobileapp-userfiles-mobilehub-830251747'

#AWS_DEST_BUCKET_NAME = 'hackdaysopencvexpres-processed'

def lambda_handler(event, context):

for record in event['Records']:

task = json.loads(record['body'])

doTask(task)

def doTask(task):

taskId = task['UID']

sourceImageName = task['fileName']

operation = task['operation']

print("taskId=" + taskId)

print("sourceImageName=" + sourceImageName)

print("operation=" + operation)

if (operation == "rotate"):

rotateImage(taskId, sourceImageName)

elif (operation == "rmback"):

removeBack(taskId, sourceImageName)

else:

print("Unknown operation: " + operation)

def rotateImage(taskId, sourceImageName):

print("Rotating " + sourceImageName)

sourceImageUrl = "https://s3.eu-central-1.amazonaws.com/" + AWS_SRC_BUCKET_NAME + "/public/" + sourceImageName

sourceImage = loadImage(sourceImageUrl)

# Do transformation

dim = sourceImage.shape

rotationAngle = -30

scaleFactor = 1

# Rotating the image by 90 degrees about the center

# dim[0] stores the no of rows and dim[1] no of columns

rotationMatrix = cv2.getRotationMatrix2D((dim[1] / 2, dim[0] / 2), rotationAngle, scaleFactor)

resultImage = cv2.warpAffine(sourceImage, rotationMatrix, (dim[1], dim[0]))

r, encodedResultImage = cv2.imencode(".png", resultImage)

resultImagePath = "public/" + taskId + "_processed.png"

saveToS3(bytearray(encodedResultImage), AWS_DEST_BUCKET_NAME, resultImagePath, 'image/png')

print("Rotating result saved to " + resultImagePath)

return resultImagePath

def removeBack(taskId, sourceImageName):

print("Removing background " + sourceImageName)

sourceImageUrl = "https://s3.eu-central-1.amazonaws.com/" + AWS_SRC_BUCKET_NAME + "/public/" + sourceImageName

sourceImage = loadImage(sourceImageUrl)

resultImage = segment(sourceImage)

r, encodedResultImage = cv2.imencode(".png", resultImage)

resultImagePath = "public/" + taskId + "_processed.png"

saveToS3(bytearray(encodedResultImage), AWS_DEST_BUCKET_NAME, resultImagePath, 'image/png')

print("Rotating result saved to " + resultImagePath)

return resultImagePath

def loadImage(imageUrl):

resp = urllib.urlopen(imageUrl)

image = np.asarray(bytearray(resp.read()), dtype="uint8")

return cv2.imdecode(image, cv2.IMREAD_COLOR)

def saveToS3(bodyByteArray, bucketName, path, contentType):

s3 = boto3.resource('s3')

bucket = s3.Bucket(bucketName)

bucket.put_object(

ACL='public-read',

ContentType=contentType,

Key=path,

Body=bodyByteArray

)

def getSobel (channel):

sobelx = cv2.Sobel(channel, cv2.CV_16S, 1, 0, borderType=cv2.BORDER_REPLICATE)

sobely = cv2.Sobel(channel, cv2.CV_16S, 0, 1, borderType=cv2.BORDER_REPLICATE)

sobel = np.hypot(sobelx, sobely)

return sobel;

def findSignificantContours (img, sobel_8u):

image, contours, heirarchy = cv2.findContours(sobel_8u, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

# Find level 1 contours

level1 = []

for i, tupl in enumerate(heirarchy[0]):

# Each array is in format (Next, Prev, First child, Parent)

# Filter the ones without parent

if tupl[3] == -1:

tupl = np.insert(tupl, 0, [i])

level1.append(tupl)

# From among them, find the contours with large surface area.

significant = []

tooSmall = sobel_8u.size * 5 / 100 # If contour isn't covering 5% of total area of image then it probably is too small

for tupl in level1:

contour = contours[tupl[0]];

area = cv2.contourArea(contour)

if area > tooSmall:

cv2.drawContours(img, [contour], 0, (0,255,0),2, cv2.LINE_AA, maxLevel=1)

significant.append([contour, area])

significant.sort(key=lambda x: x[1])

return [x[0] for x in significant];

def segment (img):

blurred = cv2.GaussianBlur(img, (5, 5), 0) # Remove noise

# Edge operator

sobel = np.max( np.array([ getSobel(blurred[:,:, 0]), getSobel(blurred[:,:, 1]), getSobel(blurred[:,:, 2]) ]), axis=0 )

# Noise reduction trick, from http://sourceforge.net/p/octave/image/ci/default/tree/inst/edge.m#l182

mean = np.mean(sobel)

# Zero any values less than mean. This reduces a lot of noise.

sobel[sobel <= mean] = 0;

sobel[sobel > 255] = 255;

sobel_8u = np.asarray(sobel, np.uint8)

# Find contours

significant = findSignificantContours(img, sobel_8u)

# Mask

mask = sobel.copy()

mask[mask > 0] = 0

cv2.fillPoly(mask, significant, 255)

# Invert mask

mask = np.logical_not(mask)

#Finally remove the background

img[mask] = 0;

return img