OpenCV in Lambda: Created an API to convert images to Pokémon ASCII art style using AWS and OpenCV

import requests
import cv2
import numpy as np
import tempfile
import urllib.request
from google.colab import files

# Cropping an image
def crop(image):
    # Loading images
    img = cv2.imread(image)
    # Convert the original image to grayscale
    gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # Convert grayscale image to black and white
    ret_, bw_img = cv2.threshold(gray_img, 1, 255, cv2.THRESH_BINARY)
    # Extraction of contours in black and white images
    contours, h_ = cv2.findContours(bw_img, cv2.RETR_EXTERNAL, method=cv2.CHAIN_APPROX_NONE)
    # Calculate the banding box from the contour.
    rect = cv2.boundingRect(contours[-1])
    # Crop the source image with the coordinates of the bounding box
    crop_img = img[rect[1]:rect[1]+rect[3]-1, rect[0]:rect[0]+rect[2]-1]
    return crop_img

# Specify the numbers of the Pokémon you want, like lists = [1,4,7,10,16,24,25,26].
# If you try to download too many images at once, you will end up losing some, so download about 10 at a time.
for i in lists:
    url = "https://pokeapi.co/api/v2/pokemon/{}/".format(i)
    tmp_input = '{}.png'.format(i)
    r = requests.get(url, timeout=5)
    r = r.json()
    image =r['sprites']['front_default']
    urllib.request.urlretrieve(image, tmp_input)
    img = crop(tmp_input)
    img2 = cv2.resize(img , (15, 15))
    cv2.imwrite(tmp_input , img2)
    files.download(tmp_input)

# app.py

import json
import cv2
import base64
import numpy as np
import glob

def calc_hue_hist(img):
    # Convert to H (Hue) S (Saturation) V (Lightness) format.
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    #hsv = img
    # Separate each component.
    h, s, v = cv2.split(hsv)
    # Calculated pixel values of an image (histogram)
    h_hist = calc_hist(h)
    return h_hist

def calc_hist(img):
    # Calculate the histogram.
    hist = cv2.calcHist([img], channels=[0], mask=None, histSize=[256], ranges=[0, 256])
    # Normalize the histogram.
    hist = cv2.normalize(hist, hist, 0, 255, cv2.NORM_MINMAX)
    # (n_bins, 1) -> (n_bins,)
    hist = hist.squeeze(axis=-1)
    return hist

def base64_to_cv2(image_base64):
    # base64 image to cv2
    image_bytes = base64.b64decode(image_base64)
    np_array = np.fromstring(image_bytes, np.uint8)
    image_cv2 = cv2.imdecode(np_array, cv2.IMREAD_COLOR)
    return image_cv2

def cv2_to_base64(image_cv2):
    # cv2 image to base64
    image_bytes = cv2.imencode('.jpg', image_cv2)[1].tostring()
    image_base64 = base64.b64encode(image_bytes).decode()
    return image_base64

# Receive json in event { 'img': base64img }
def handler(event, context):
    input_img = event['img'] #Assign the second image to the read object "trimmed_img"
    input_img = base64_to_cv2(input_img)
    height, width, channels = input_img.shape[:3]

    max_ruizido = 0
    tmp_num = 5
    crop_size = 15
    ch_names = {0: 'Hue', 1: 'Saturation', 2: 'Brightness'}
    poke_imgs = glob.glob('pokemons/*.png')
    for i in range(height//crop_size):
        for j in range(width//crop_size):
            # Split the image into crop_size*crop_size
            trimed_img = input_img[crop_size*i : crop_size*(i+1), crop_size*j: crop_size*(j+1)]
            max_ruizido = -1
            tmp_num = 5
            h_hist_2 = calc_hue_hist(trimed_img)
            for poke_name in poke_imgs:
                poke_img = cv2.imread(poke_name) #Assign the image to the read object "poke_img"
                # Get a histogram of the Hue components of an image
                h_hist_1 = calc_hue_hist(poke_img)
                h_comp_hist = cv2.compareHist(h_hist_1, h_hist_2, cv2.HISTCMP_CORREL)
                if max_ruizido < h_comp_hist:
                    max_ruizido = h_comp_hist
                    tmp_num = poke_name
            if j == 0:
                # The leftmost image
                tmp_img = cv2.imread(tmp_num)
            else:
                # Images to be merged
                tmp_img2 = cv2.imread(tmp_num)
                # Consolidated images
                tmp_img = cv2.hconcat([tmp_img, tmp_img2])
        if i == 0:
            # Now that the first line is complete, move on to the next line
            output_img = tmp_img
        else:
            output_img = cv2.vconcat([output_img, tmp_img])

    return {'data': cv2_to_base64(output_img)}

FROM public.ecr.aws/lambda/python:3.7

RUN pip install opencv-python
RUN pip install numpy

COPY app.py /var/task/
COPY pokemons/ /var/task/pokemons/

FROM public.ecr.aws/lambda/python:3.7
RUN pip install opencv-python
RUN pip install numpy
COPY app.py /var/task/
COPY pokemons/ /var/task/pokemons/
CMD [ "app.handler" ]