To elaborate more of what my expected outcome is, I have attached a test.png file, and what I want this script to do.
Essentially you would somehow have to find all the black pixels and some how find the start and stop location of them.
Then I want to enlarge that, so to speak, I want to extract the selected pixels (the contents in the green square) into it's own image and save it.
All my images are either Black and White, no other color.
Original Image
Outcome Image
Extracted from Outcome Image
If anyone could help me out with this, it'd be super appreciated! I've tried using Opencv Python to somehow find black pixels but all my attempts have failed.
Here is one way to do that in Python/OpenCV.
Read the input
Convert to gray
Invert polarity (regions for contours need to be white)
Threshold
Get contours
Get bounding boxes
Crop regions in input according to bounding boxes
Save to disk
Input:
import cv2
import numpy as np
# read image
img = cv2.imread("black_pixels.png")
# convert img to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# invert polarity
gray = 255 - gray
# do adaptive threshold on gray image
thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY)[1]
# Get contours
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
i = 1
for c in cnts:
# create white image
result = np.full_like(img, (255,255,255))
# get bounding box
x,y,w,h = cv2.boundingRect(c)
# crop region of img using bounding box
region = img[y:y+h, x:x+w]
# save region to new image
cv2.imwrite("black_region_{0}.png".format(i), region)
i = i + 1
# display it
cv2.imshow("IMAGE", img)
cv2.imshow("GRAY", gray)
cv2.imshow("THRESHOLD", thresh)
cv2.waitKey(0)
Result:
Related
As shown in the images, I have a masked MRI in which certain muscles have been segmented out. I am trying to get the area of these muscles in pixels. Currently, I can get all the non-zero pixels in the image using np.count_nonzero(result), however, I am looking to get the individual areas of each left and right muscle, not just the total.
I know I can manually crop/select an ROI of each area, but I do not want to do this for 100s of images. Also not all muscles are cleanly on each side as seen by the bottom image (cannot just split the image in half). The images are represented as 2D arrays.
Thank you!
You can use OpenCV Library to get the number of pixels for each object in the image.
import cv2 # OpenCV library
# Read the image
image = cv2.imread(r'D:\image_sample.jpg')
# Convert the image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Get the mask (predefined threshold)
mask = cv2.inRange(gray, 200, 255)
#Find all Objects contour's
contours, hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
for i in range(len(contours)):
m=max(contours[i][0])
area = cv2.contourArea(contours[i])
print(f"Area{i+1} = {area} pixels")
image = cv2.drawContours(image, contours, i, (0,255,255*i), -1)
img_text = cv2.putText(image, str(area), m, cv2.FONT_HERSHEY_PLAIN, 1, (255,0,255), 1, cv2.LINE_AA, False)
#Show the resut
cv2.imshow('image with contours', image)
cv2.waitKey()
I have the following image, which is a scanned printed paper with 4 images. I printed 4 images in the same sheet of paper to save printing resources:
However, now I need to extract image by image and, for each of them, create an individual image file. Is there any easy way of doing that with Python, Matlab or any other programming language?
Here is one way to do that in Python/OpenCV. But it requires that the pictures colors at their sides be sufficiently different from the background color. If so, you can threshold the image, then get contours and use their bounding boxes to crop out each image.
Read the input
Threshold based on the background color
Invert the threshold, so that the background is black
Apply morphology open and close to fill the picture regions and remove noise
Get the external contours
For each contour, get its bounding box and crop the input image and save it to disk
Input:
import cv2
import numpy as np
# read image
img = cv2.imread('4faces.jpg')
# threshold on background color
lowerBound = (230,230,230)
upperBound = (255,255,255)
thresh = cv2.inRange(img, lowerBound, upperBound)
# invert so background black
thresh = 255 - thresh
# apply morphology to ensure regions are filled and remove extraneous noise
kernel = np.ones((7,7), np.uint8)
thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
kernel = np.ones((11,11), np.uint8)
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
# get contours
contours = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
# get bounding boxes and crop input
i = 1
for cntr in contours:
# get bounding boxes
x,y,w,h = cv2.boundingRect(cntr)
crop = img[y:y+h, x:x+w]
cv2.imwrite("4faces_crop_{0}.png".format(i), crop)
i = i + 1
# save threshold
cv2.imwrite("4faces_thresh.png",thresh)
# show thresh and result
cv2.imshow("thresh", thresh)
cv2.waitKey(0)
cv2.destroyAllWindows()
Thresholded image after morphology cleaning:
Cropped Images:
I am working with videos, that have borders (margins) around them. Some have it along all 4 sides, some along left&right only and some along top&bottom only. Length of these margins is also not fixed.
I am extracting frames from these videos, as for example,
and
Both of these contain borders on the top and bottom.
Can anyone please suggest some methods to remove these borders from these images (in Python, preferably).
I came across some methods, like this on Stackoverflow, but this deals with an ideal situation where borders are perfectly black (0,0,0). But in my case, they may not be pitch black, and also may contain jittery noises too.
Any help/suggestions would be highly appreciated.
Here is one way to do that in Python/OpenCV.
Read the image
Convert to grayscale and invert
Threshold
Apply morphology to remove small black or white regions then invert again
Get the contour of the one region
Get the bounding box of that contour
Use numpy slicing to crop that area of the image to form the resulting image
Save the resulting image
import cv2
import numpy as np
# read image
img = cv2.imread('gymnast.png')
# convert to grayscale
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# invert gray image
gray = 255 - gray
# gaussian blur
blur = cv2.GaussianBlur(gray, (3,3), 0)
# threshold
thresh = cv2.threshold(blur,236,255,cv2.THRESH_BINARY)[1]
# apply close and open morphology to fill tiny black and white holes
kernel = np.ones((5,5), np.uint8)
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
# invert thresh
thresh = 255 -thresh
# get contours (presumably just one around the nonzero pixels)
# then crop it to bounding rectangle
contours = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
cntr = contours[0]
x,y,w,h = cv2.boundingRect(cntr)
crop = img[y:y+h, x:x+w]
cv2.imshow("IMAGE", img)
cv2.imshow("THRESH", thresh)
cv2.imshow("CROP", crop)
cv2.waitKey(0)
cv2.destroyAllWindows()
# save cropped image
cv2.imwrite('gymnast_crop.png',crop)
cv2.imwrite('gymnast_crop.png',crop)
Input:
Thresholded and cleaned image:
Cropped Result:
I have two pictures of the same dimension and I want to detect and replace the white region in the first picture (black image) at the same location in the second picture. Is there any way to do this using OpenCV? I want to replace the blue region in the original image with the white region in the first picture.
First picture
Original image
If I'm understanding you correctly, you want to replace the white ROI on the black image onto the original image. Here's a simple approach:
Obtain binary image. Load image, grayscale, Gaussian blur, then Otsu's threshold
Extract ROI and replace. Find contours with cv2.findContours then filter using contour approximation with cv2.arcLength and cv2.approxPolyDP. With the assumption that the region is a rectangle, if the contour approximation result is 4 then we have found our desired region. In addition, we filter using cv2.contourArea to ensure that we don't include noise. Finally we obtain the bounding box coordinates with cv2.boundingRect and extract the ROI with Numpy slicing. Finally we replace the ROI into the original image.
Detected region to extract/replace highlighted in green
Extracted ROI
Result
Code
import cv2
# Load images, grayscale, Gaussian blur, Otsu's threshold
original = cv2.imread('1.jpg')
image = cv2.imread('2.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (3,3), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
# Find contours, filter using contour approximation + area, then extract
# ROI using Numpy slicing and replace into original image
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.015 * peri, True)
area = cv2.contourArea(c)
if len(approx) == 4 and area > 1000:
x,y,w,h = cv2.boundingRect(c)
ROI = image[y:y+h,x:x+w]
original[y:y+h, x:x+w] = ROI
cv2.imshow('thresh', thresh)
cv2.imshow('ROI', ROI)
cv2.imshow('original', original)
cv2.waitKey()
I am stitching multiple images into one. In an intermediate step, I get an image like:
It isn't necessary at all that the image starts from left and has black region in the right. I want to obtain a rectangular image from this one that doesn't contain the black region. That is, something like:
Can someone please suggest me a way to do that?
Here is one way to crop out the excess black on the right side of the image:
Read the image
Convert to grayscale
Threshold
Apply closing and opening morphology to remove small black and white spots.
Get the surrounding contour
Crop the contour
Save the result
Input:
import cv2
import numpy as np
# read image
img = cv2.imread('road.jpg')
# convert to grayscale
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# threshold
_,thresh = cv2.threshold(gray,5,255,cv2.THRESH_BINARY)
# apply close and open morphology to fill tiny black and white holes
kernel = np.ones((5,5), np.uint8)
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
# get contours (presumably just one around the nonzero pixels)
# then crop it to bounding rectangle
contours = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
for cntr in contours:
x,y,w,h = cv2.boundingRect(cntr)
crop = img[y:y+h,x:x+w]
# show cropped image
cv2.imshow("CROP", crop)
cv2.waitKey(0)
cv2.destroyAllWindows()
# save cropped image
cv2.imwrite('road_crop.png',crop)