image looks overexposed (nearly all white) when using np.zeros with OpenCV imshow

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I am writing a code in openCV python for copying an image for practice purpose only, though np.copy() command is already available. Code is as below:

import numpy as np
import cv2 as cv

img = cv.imread('Photos/image_1.jpg')
r, c, d = img.shape
img_copy = np.zeros((r, c, d))
for i in range(r):
    for j in range(c):
        for k in range(d):
            img_copy[i, j, k] = img[i, j, k]

cv.imshow('original image', img)
cv.imshow('copied image', img_copy)
cv.waitKey(0)

The img_copy is not shown instead black image is shown. If I use img_copy = np.ones(rows, cols, 3), and apply same for loop, still then a white image is shown, original image is not shown. Can any one explain why this occures? Original and copied images are shown below.

enter image description hereenter image description here

opencv numpy python
1 Answers

This issue was caused by incompatible data types. To determine the data type of the image, use

img = cv.imread('Photos/image_1.jpg')
print(img.dtype)

In my testing case, the output data type was uint8

Changing

img_copy = np.zeros((r, c, d))

to

img_copy = np.zeros((r, c, d), dtype=np.uint8)

will fix this issue

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