import glob
from PIL import Image
import glob
import cv2
import numpy as np
import matplotlib.pyplot as plt
import os
# filepaths
fp_in_CFD = sorted(glob.glob("/home/imagda/sims-projs/CFD-DeepLearning-UNET/plots/valid_data/plots/montage_car_CFD_*.*"))
fp_in_ML = sorted(glob.glob("/home/imagda/sims-projs/CFD-DeepLearning-UNET/plots/valid_data/plots/montage_car_ML_*.*"))

counter = 0
for fp_img1, fp_img2 in zip(fp_in_CFD, fp_in_ML):
    counter += 1
    img1 = cv2.imread(fp_img1, -1)
    img2 = cv2.imread(fp_img2, -1) # this one has transparency
    img1_crop = img1[183:400, 169:1300]
    img2_crop = img2[183:400, 169:1300]
    numpy_vertical_concat = np.concatenate((img1_crop, img2_crop), axis=0)
    #numpy_horizontal_concat = np.concatenate((img1_crop, img2_crop), axis=1)
    filename = "/home/imagda/sims-projs/CFD-DeepLearning-UNET/plots/valid_data/plots/CFD+ML_"+"{:02d}".format(counter) +".png"
    #file = open('{}{}'.format(name, x) , "w+")
    plt.savefig(filename, dpi = 200)
    plt.imshow( numpy_vertical_concat)

counter = 0
for fp_img1, fp_img2 in zip(fp_in_CFD, fp_in_ML):
    counter += 1
    img1 = cv2.imread(fp_img1, -1)
    img2 = cv2.imread(fp_img2, -1) # this one has transparency
    img1_crop = img1[183:400, 0:200]
    img2_crop = img2[183:400, 800:1000]
    #numpy_vertical_concat = np.concatenate((img1_crop, img2_crop), axis=0)
    numpy_horizontal_concat = img1_crop + img2_crop
    filename = "/home/imagda/sims-projs/CFD-DeepLearning-UNET/plots/valid_data/plots/CFD+ML_"+"{:02d}".format(counter) +".png"
    #file = open('{}{}'.format(name, x) , "w+")
    plt.savefig(filename, dpi = 200)
    plt.imshow( numpy_horizontal_concat)

img1 = cv2.imread(fp_img1)

img1.shape

type(img1)

import cv2
from skimage import io  
    
lion_rgb = cv2.imread('https://i.stack.imgur.com/R6X5p.jpg')
lion_gray = cv2.imread('https://i.stack.imgur.com/f27t5.png')   
# Read Image1
#mountain = cv2.imread(fp_img1, 1)
  
# Read image2
#dog = cv2.imread(fp_img2, 1)
  
# Blending the images with 0.3 and 0.7
img = cv2.addWeighted(lion_rgb, 1, lion_rgb, 1, 0)
plt.imshow(img/266.)
#io.imshow(img)  
# Show the image
#cv2.imshow('image', img)
  
# Wait for a key
#cv2.waitKey(0)
  
# Distroy all the window open
#cv2.distroyAllWindows()

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-20-755843087fee> in <module>
     12 # Blending the images with 0.3 and 0.7
     13 img = cv2.addWeighted(lion_rgb, 1, lion_rgb, 1, 0)
---> 14 plt.imshow(img/266.)
     15 #io.imshow(img)
     16 # Show the image

TypeError: unsupported operand type(s) for /: 'NoneType' and 'float'
rgb.shape

(450, 600, 3)
import numpy as np
from skimage import io

rgb = io.imread('https://i.stack.imgur.com/R6X5p.jpg')
gray = io.imread('https://i.stack.imgur.com/f27t5.png')

rows_rgb, cols_rgb, channels = rgb.shape
rows_gray, cols_gray = gray.shape

rows_comb = max(rows_rgb, rows_gray)
cols_comb = cols_rgb + cols_gray
comb = np.zeros(shape=(rows_comb, cols_comb, channels), dtype=np.uint8)

comb[:rows_rgb, :cols_rgb] = rgb
comb[:rows_gray, cols_rgb:] = gray[:, :, None]

io.imshow(comb)

<matplotlib.image.AxesImage at 0x7f8196101a50>
rgb.shape

(450, 600, 3)