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Removing background from galaxy photos using Python


By : Adepoju Ademola Chri
Date : October 17 2020, 08:10 PM
it fixes the issue Try this:
Basic idea of this solution is, get the contours of the image after performing threshold() and detect the biggest contour among contours.
code :
import cv2
image = cv2.imread("test.jpg", 1)
img = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
cv2.threshold(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU,img)
im2, contours, hier = cv2.findContours(img, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)

if len(contours) != 0:
    #find the biggest area
    c = max(contours, key = cv2.contourArea)
    x,y,w,h = cv2.boundingRect(c)
    cv2.rectangle(image,(x,y),(x+w,y+h),(0,255,0),1)

cv2.imshow("Result", image)
cv2.waitKey(0)


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Removing background from image (MATLAB, Java, or Python preferred)


By : Ruben Kvamme
Date : March 29 2020, 07:55 AM
To fix the issue you can do What I would like to do is take an image of several plants and remove the background lines and noise from the image. The end result will then hopefully be able to be turned into a binary image where every 1 value is just part of the plant. Is there any way for me to do this automatically without having to use a manual threshold? One solution I've looked into is just estimating the value of what is not noise and then subtracting, but this results in removing parts of plants. An example image for input is: http://i.imgur.com/utAX6yu.png
code :
I=im2bw(I,graythresh(I));
imshow(I)

removing space in between photos


By : Annika H Schauer
Date : March 29 2020, 07:55 AM
To fix the issue you can do Because your li's are inline-block, you must set font-size:0 for your ul.

Samsung Galaxy S7 show device photos rotated


By : Ankita Thakur
Date : March 29 2020, 07:55 AM
I wish did fix the issue. If you use an image-loading library, and you care about EXIF orientation headers, choose an image-loading library that applies those headers for you (e.g., Picasso, at least for local images). This may require some sort of an opt-in call (e.g., considerExifParams(true), per your comment).
Not all images will have an EXIF orientation header. It depends on whether it is a photo, what sort of camera hardware took the photo, whether the camera app that took the photo rotated the image for you, etc.

How to recognize multiple photos on a white background using openCV-python?


By : user2881036
Date : March 29 2020, 07:55 AM
To fix the issue you can do Here's an approach based on the assumption that the photos will not be intersecting each other
Convert to grayscale and Gaussian blur Threshold image Find contours and obtain bounding box contours Extract ROI
code :
x,y,w,h = cv2.boundingRect(c)
ROI = original[y:y+h, x:x+w]
import cv2

image = cv2.imread('1.png')
original = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (3, 3), 0)
thresh = cv2.threshold(blurred, 230,255,cv2.THRESH_BINARY_INV)[1]

# Find contours
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]

# Iterate thorugh contours and filter for ROI
image_number = 0
for c in cnts:
    x,y,w,h = cv2.boundingRect(c)
    cv2.rectangle(image, (x, y), (x + w, y + h), (36,255,12), 2)
    ROI = original[y:y+h, x:x+w]
    cv2.imwrite("ROI_{}.png".format(image_number), ROI)
    image_number += 1

cv2.imshow('thresh', thresh)
cv2.imshow('image', image)
cv2.waitKey(0)

Removing background color from image opencv python


By : user3703540
Date : March 29 2020, 07:55 AM
I think the issue was by ths following , Here's a simple approach with the assumption that there is only one specimen per image.
code :
import cv2
import numpy as np

# Kmeans color segmentation
def kmeans_color_quantization(image, clusters=8, rounds=1):
    h, w = image.shape[:2]
    samples = np.zeros([h*w,3], dtype=np.float32)
    count = 0

    for x in range(h):
        for y in range(w):
            samples[count] = image[x][y]
            count += 1

    compactness, labels, centers = cv2.kmeans(samples,
            clusters, 
            None,
            (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10000, 0.0001), 
            rounds, 
            cv2.KMEANS_RANDOM_CENTERS)

    centers = np.uint8(centers)
    res = centers[labels.flatten()]
    return res.reshape((image.shape))

# Load image and perform kmeans
image = cv2.imread('2.jpg')
original = image.copy()
kmeans = kmeans_color_quantization(image, clusters=4)

# Convert to grayscale, Gaussian blur, adaptive threshold
gray = cv2.cvtColor(kmeans, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (3,3), 0)
thresh = cv2.adaptiveThreshold(blur,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV,21,2)

# Draw largest enclosing circle onto a mask
mask = np.zeros(original.shape[:2], dtype=np.uint8)
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
for c in cnts:
    ((x, y), r) = cv2.minEnclosingCircle(c)
    cv2.circle(image, (int(x), int(y)), int(r), (36, 255, 12), 2)
    cv2.circle(mask, (int(x), int(y)), int(r), 255, -1)
    break

# Bitwise-and for result
result = cv2.bitwise_and(original, original, mask=mask)
result[mask==0] = (255,255,255)

cv2.imshow('thresh', thresh)
cv2.imshow('result', result)
cv2.imshow('mask', mask)
cv2.imshow('kmeans', kmeans)
cv2.imshow('image', image)
cv2.waitKey()
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