AWS_LAMBDA-functions.py

import json
import cv2
import numpy as np
import base64
import os
import functions


def decode(encoded_img):
    aux_path = '/tmp/tmp.png'
    with open(aux_path, "wb") as f:
        f.write(base64.b64decode(encoded_img))
        f.close()
    out = cv2.imread(aux_path)
    return out


def encode(img):
    aux_path = '/tmp/tmp.png'
    cv2.imwrite(aux_path, img)

    with open(aux_path, "rb") as f:
        code = base64.b64encode(f.read())
        f.close()
    return code.decode("utf-8")

def converter(img):
    img = cv2.resize(img, (80,80))
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    copy = img.copy()
    copy = cv2.cvtColor(copy, cv2.COLOR_RGB2GRAY)
    blurred = cv2.GaussianBlur(copy,(5,5), 0)
    thresh = cv2.threshold(blurred, 18, 255, cv2.THRESH_BINARY)[1]
    contour = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    contour = contour[0][0]
    contour = contour[:,0,:]
    x1 = tuple(contour[contour[:,0].argmin()])[0]
    y1 = tuple(contour[contour[:,1].argmin()])[1]
    x2 = tuple(contour[contour[:,0].argmax()])[0]
    y2 = tuple(contour[contour[:,1].argmax()])[1]
    x = int(x2-x1)*4//100
    y = int(y2-y1)*5//100
    copy_2 = img.copy()
    if x2-x1 > 100 and y2-y1 > 100:
        copy_2 = copy_2[y1+y:y2-y, x1+x:x2-x]
        copy_2 = cv2.resize(copy_2, (80,80))
    lab = cv2.cvtColor(copy_2, cv2.COLOR_RGB2LAB)
    l, a, b = cv2.split(lab)
    clahe = cv2.createCLAHE(clipLimit=5.0, tileGridSize=((8,8)))
    cl = clahe.apply(l)
    l_img = cv2.merge((cl, a, b))
    last = cv2.cvtColor(l_img, cv2.COLOR_LAB2RGB)
    med_last = cv2.medianBlur(last, 3)
    background = cv2.medianBlur(last, 37)
    mask = cv2.addWeighted(med_last,1, background,-1,255)
    final_img = cv2.bitwise_and(mask, med_last)
    #print(final_img.shape)
    # final_img = np.expand_dims(final_img, axis=0)
    return (np.squeeze(final_img))