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I am working in python on openCV 3.0. In order to find the largest white pixel region, first of all thresholded gray image to binary image.

import cv2

import numpy as np

img = cv2.imread('graimage.png') 

img = cv2.resize(img,(400,500))

gray = img.copy()

(thresh, im_bw) = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY )

derp,contours,hierarchy = cv2.findContours(im_bw,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)

cnts = max(cnts, key=cv2.contourArea)

But it shows error as follows.

cv2.error: ..../opencv/modules/imgproc/src/contours.cpp:198: error: (-210) [Start]FindContours supports only CV_8UC1 images when mode != CV_RETR_FLOODFILL otherwise supports CV_32SC1 images only in function cvStartFindContours.

vahid abdi
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Kaira
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    Is your image 8 bit Unsigned char? Or you can write 'img=imread('grainimage.png',0)' to convert into grayscale. – Gunjan naik Jan 11 '16 at 08:18

1 Answers1

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It looks like this was answered in the comments, but just to mark the question as answered:

CV_8UC1 means 8-bit pixels, unsigned, and only one channel, so grayscale. It looks like you're reading it in with 3 color channels, or CV_8UC3. You can check the image type by printing img.dtype and img.shape. The dtype should be uint8, and the shape should be (#, #), indicating two dimensions. I'm guessing you'll see that shape prints (#, #, 3) for your image as-is, indicating three color channels.

As @user3515225 said, you can fix that by reading the image in as grayscale using cv2.imread('img.png', cv2.IMREAD_GRAYSCALE). That assumes you have no use for color anywhere else, though. If you want a separate grayscale copy of the image, then replace gray = img.copy() with gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) instead.

dirtbirb
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