I have this image of a bunch of circles, all different colors (red, green, yellow, purple, etc.). I would like to individually crop all the red circles and save them as separate files (ex. circle(1).png, circle(2).png, etc.).
What I have so far is a solution to only show the red circles. I created a mask with cv2.inRange and used a cv2.bitwise_and to only show the red circles. Here is my code:
import cv2
import numpy as np
image = cv2.imread('dots.jpg')
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
lower_red = np.array([150,100,0])
upper_red = np.array([255,255,255])
# Threshold the HSV image to get only red cirlces
mask = cv2.inRange(hsv, lower_red, upper_red)
# Bitwise-AND mask and original image
res = cv2.bitwise_and(image,image, mask=mask)
I guess what I'm looking for is something like cv2.selectROI() but runs automatically (no manual click&drag) and can crop multiple regions. Any ideas or tips appreciated. Thanks
For red, you can choose the HSV range (0,50,20) ~ (5,255,255) and (175,50,20)~(180,255,255) using the colormap given here. Your mask in above code won't detect both red circles in below image, for example. Check this yourself.
You can try below code:
import cv2
import numpy as np
image = cv2.imread('circles.jpg')
img_hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
# Gen lower mask (0-5) and upper mask (175-180) of RED
mask1 = cv2.inRange(img_hsv, (0,50,20), (5,255,255))
mask2 = cv2.inRange(img_hsv, (175,50,20), (180,255,255))
# Merge the mask and crop the red regions
mask = cv2.bitwise_or(mask1, mask2)
# Bitwise-AND mask and original image
res = cv2.bitwise_and(image,image, mask=mask)
# coverting image with red colored region of interest from HSV to RGB
hsv2bgr = cv2.cvtColor(res, cv2.COLOR_HSV2BGR)
# RGB to GRAYSCALE
rgb2gray = cv2.cvtColor(hsv2bgr, cv2.COLOR_BGR2GRAY)
# Applying thresholding to the grayscale image for black & white color
thresh_gray = cv2.threshold(rgb2gray, 20,255, cv2.THRESH_BINARY)[1]
# Find the different contours
contours = cv2.findContours(rgb2gray, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)[0]
#print(len(contours))
i = 0
for c in contours:
_, radius = cv2.minEnclosingCircle(c)
if radius>10:
# create a mask and fill it with white color
mask = np.zeros(image.shape, dtype=np.uint8)
cv2.fillPoly(mask, pts=[c], color=(255, 255, 255))
# Bitwise-AND mask and original image
# output is red circle with black background
masked_image = cv2.bitwise_and(image, mask)
# to get individual red circle with white background
mask_ = cv2.bitwise_not(mask)
circle_ = cv2.bitwise_or(masked_image, mask_)
cv2.imwrite('circle({}).jpg'.format(i), circle_)
i+=1
Input Image: circles.jpg
There are two red circle object in the above input image, hence it will create two files- circle(0).jpg and circle(1).jpg each with individual red circles.
Related
I have this image.
I want to make all the colored headings to white and the text in it to black.
I try below to make the image full black and white.
img_grey = cv2.imread('img.jpg', cv2.IMREAD_GRAYSCALE)
thresh = 170
img_binary = cv2.threshold(img_grey, thresh, 250, cv2.THRESH_BINARY)[1]
cv2.imwrite('bw_img.jpg',img_binary)
Now those headings are black and text within those is white. But i want to make text black and heading layout white. So, can anyone helps me with that?
You may convert the image to HSV, apply threshold to find colored regions, and copy the result of cv2.threshold with cv2.THRESH_BINARY_INV only to the colored regions.
Main stages of the suggested solution:
Convert from BGR to HSV color space, and get the saturation color channel.
all black and white are zero, and colored pixels are above zero.
Apply threshold to the saturation channel.
Find contours on the binarized saturation channel.
Draw the contours as white (255 values) on black background to form a mask.
Apply morphological closing for closing some black gaps.
Get only the area inside the mask from img_binary_inv (result of your code using cv2.THRESH_BINARY_INV).
Copy masked img_binary_inv to img_grey only in pixels that mask is white.
Complete code sample:
import numpy as np
import cv2
img_bgr = cv2.imread('img.jpg') # Read image as BGR
img_grey = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY) # Convert from BGR to grayscale.
thresh = 170
img_binary_inv = cv2.threshold(img_grey, thresh, 255, cv2.THRESH_BINARY_INV)[1] # Apply threshold and invert black/white
# Convert from BGR to HSV color space.
hsv = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HSV)
# Get the saturation color channel - all black and white are zero, and colored pixels are above zero.
s = hsv[:, :, 1]
thresh = 100
s_binary = cv2.threshold(s, thresh, 255, cv2.THRESH_BINARY)[1] # Apply threshold to the saturation channel.
# Find contours on s_binary
cnts = cv2.findContours(s_binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[-2] # Use index [-2] to be compatible to OpenCV 3 and 4
# Draw the contours as white (255 values) on black background.
mask = np.zeros_like(s_binary)
cv2.drawContours(mask, cnts, -1, 255, -1)
# Apply morphological closing for closing some black gaps.
mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, np.ones((3, 3)))
# Get only the area inside the mask from img_binary_inv
masked_binary_inv = cv2.bitwise_or(img_binary_inv, img_binary_inv, mask=mask)
# Copy masked_binary_inv to img_grey only in pixels that mask is white.
cv2.copyTo(masked_binary_inv, mask, img_grey)
cv2.imwrite('img_grey.png', img_grey) # Save result (as PNG and not JPEG for better quality).
# Show images
cv2.imshow('img_bgr', img_bgr)
cv2.imshow('s_binary', s_binary)
cv2.imshow('mask', mask)
cv2.imshow('masked_binary_inv', masked_binary_inv)
cv2.imshow('img_grey', img_grey)
cv2.waitKey()
cv2.destroyAllWindows()
Result (img_grey):
The result doesn't look so good, due to the relatively low quality of the input image.
Intermediate results:
s_binary:
mask:
masked_binary_inv:
I want to blur red color in image ("1.png" is attached) so that its not clearly visible. I tried below code where I can change red color to black color but how can I blur it? Please help.
import cv2
import numpy as np
frame = cv2.imread("1.png")
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# define range of red color in HSV
lower = np.array([0,50,50])
upper = np.array([10,255,255])
# define range of blue color in HSV
# lower = np.array([38, 86, 0])
# upper = np.array([121, 255, 255])
# define range of pink color in HSV
# http://www.workwithcolor.com/pink-color-hue-range-01.htm
# lower = np.array([158, 127, 0])
# upper = np.array([179, 255, 255])
# Threshold the HSV image to get only red colors
mask = cv2.inRange(hsv, lower, upper)
color_only = cv2.bitwise_and(frame, frame, mask = mask)
# convert mask to 3-channel image to perform subtract
mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)
res = cv2.subtract(frame, mask) #negative values become 0 -> black
cv2.imshow("frame", frame)
# cv2.imshow("mask", mask)
# cv2.imshow("color_only", color_only)
cv2.imshow("res", res)
cv2.waitKey()
cv2.destroyAllWindows()
1.png
You need to blur the segmented image and then use alpha blending to composite the blurred ROI with the background image. This code takes you through all the steps:
Read image and segment the color of interest:
import cv2
import numpy as np
frame = cv2.imread("/home/stephen/Desktop/1.png")
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# define range of red color in HSV
lower = np.array([0,50,50])
upper = np.array([10,255,255])
mask = cv2.inRange(hsv, lower, upper)
color_only = cv2.bitwise_and(frame, frame, mask = mask)
### THE BACKGROUND MUST BE MADE WHITE, NOT BLACK ###
color_only[np.where((color_only==[0,0,0]).all(axis=2))] = [255,255,255]
cv2.imshow("color_only", color_only)
Next, blur the segmented image. Note, I am using a 13,13 kernel to blur the image:
blur = cv2.blur(color_only, (13,13))
cv2.imshow('blur', blur)
Next, blur the mask of the segmented image. We are going to use this to combine the images later. It's easy to combine images using a bitwise function, but that approach will not work here because the blurred image no longer occupies the same space as the segmented image:
maskForAlphaBlending = blur
## BLACK OUT THE WHITE BACKGROUND OF THE ALPHA MASK
maskForAlphaBlending[np.where((maskForAlphaBlending==[255,255,255]).all(axis=2))] = [0,0,0]
maskForAlphaBlending = cv2.cvtColor(maskForAlphaBlending, cv2.COLOR_BGR2GRAY)
cv2.imshow('maskForAlphaBlending', maskForAlphaBlending)
Finally alpha blending can be used to composite the blurred segmented image and the green and white background image:
foreground = blur
background = frame
alpha = cv2.cvtColor(maskForAlphaBlending, cv2.COLOR_GRAY2BGR)
foreground = foreground.astype(float)
background = background.astype(float)
alpha = alpha.astype(float)/100
foreground = cv2.multiply(alpha, foreground)
background = cv2.multiply(1.0 - alpha, background)
outImage = cv2.add(foreground, background)
cv2.imshow("outImg", outImage/255)
Note how the red lines are blurred, but the green and white border is not:
I have a picture were I want to change all white-ish pixels to grey, but only for a certain area of the image. Example picture, I just want to change the picture outside of the red rectangle, without changing the image within the red rectangle:
I already have the general code, which was part of someone elses Stackoverflow question, that changes the colour of every white pixel instead of only just the one outside of an area.
image = cv.imread("meme 2.jpg")
hsv = cv.cvtColor(image, cv.COLOR_BGR2HSV)
# Define lower and uppper limits of what we call "white-ish"
sensitivity = 19
lower_white = np.array([0, 0, 255 - sensitivity])
upper_white = np.array([255, sensitivity, 255])
# Mask image to only select white
mask = cv.inRange(hsv, lower_white, upper_white)
# Change image to grey where we found brown
image[mask > 0] = (170, 170, 170)
cv.imwrite(file, image)
Here is one way to do that in Python/OpenCV.
Read the input
Convert to HSV color space
Threshold on desired color to make a mask
Use the mask to change the color of all corresponding pixels in the image
Draw a new rectangular mask for the region where you do not want to change
Invert the new mask for the region where you do want to change
Apply the new mask to the original image
Apply the inverted new mask to the color changed image
Add the two results together to form the final image
Save the results
Input:
import cv2
import numpy as np
# Read image
image = cv2.imread('4animals.jpg')
# Convert to HSV
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
# Define lower and uppper limits of what we call "white-ish"
sensitivity = 19
lower_white = np.array([0, 0, 255 - sensitivity])
upper_white = np.array([255, sensitivity, 255])
# Create mask to only select white
mask = cv2.inRange(hsv, lower_white, upper_white)
# Change image to grey where we found white
image2 = image.copy()
image2[mask > 0] = (170, 170, 170)
# Create new rectangular mask that is white on black background
x,y,w,h = 33,100,430,550
mask2 = np.zeros_like(image)
cv2.rectangle(mask2, (x,y), (x+w,y+h), (255, 255, 255), -1)
# invert mask
mask2_inv = 255 - mask2
# apply mask to image
image_masked = cv2.bitwise_and(image, mask2)
# apply inverted mask to image2
image2_masked = cv2.bitwise_and(image2, mask2_inv)
# add together
result = cv2.add(image_masked, image2_masked)
# save results
cv2.imwrite('4animals_mask.jpg', mask)
cv2.imwrite('4animals_modified.png', image2)
cv2.imwrite('4animals_mask2.jpg', mask2)
cv2.imwrite('4animals_mask2_inv.jpg', mask2_inv)
cv2.imwrite('4animals_masked.jpg', image_masked)
cv2.imwrite('4animals_modified_masked.jpg', image2_masked)
cv2.imwrite('4animals_result.jpg', result)
cv2.imshow('mask', mask)
cv2.imshow('image2', image2)
cv2.imshow('mask2', mask2 )
cv2.imshow('mask2_inv', mask2_inv)
cv2.imshow('image_masked', image_masked)
cv2.imshow('image2_masked', image2_masked)
cv2.imshow('result', result)
cv2.waitKey(0)
cv2.destroyAllWindows()
Color mask:
Rectangle mask:
Inverted rectangle mask:
Color changed image:
Masked input:
Masked color changed image:
Result:
Here is another simpler method in Python/OpenCV. My previous answer was overly complicated.
Read the input
Convert to HSV color space
Create a mask image by color thresholding
Draw a black rectangle on the previous mask for where you do not want to change the color
Apply the new combined mask to the image to change the color in the desired region
Save the result
Input:
import cv2
import numpy as np
# Read image
image = cv2.imread('4animals.jpg')
# Convert to HSV
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
# Define lower and uppper limits of what we call "white-ish"
sensitivity = 19
lower_white = np.array([0, 0, 255 - sensitivity])
upper_white = np.array([255, sensitivity, 255])
# Create mask to only select white
mask = cv2.inRange(hsv, lower_white, upper_white)
# Draw new rectangular mask on old mask that is black inside the rectangle and white outside the rectangle
x,y,w,h = 33,100,430,550
mask2 = mask.copy()
cv2.rectangle(mask2, (x,y), (x+w,y+h), 0, -1)
# Change image to grey where we found white for combined mask
result = image.copy()
result[mask2 > 0] = (170, 170, 170)
# save results
cv2.imwrite('4animals_mask.jpg', mask)
cv2.imwrite('4animals_mask2.jpg', mask2)
cv2.imwrite('4animals_result.jpg', result)
cv2.imshow('mask', mask)
cv2.imshow('mask2', mask2 )
cv2.imshow('result', result)
cv2.waitKey(0)
cv2.destroyAllWindows()
Mask from color thresholding:
Modified mask with rectangle drawn over it:
Result:
I'm using Opencv python in raspberry pi, to analize a heatmap, i'm looking for color red, which represents the highest temperature, i need to detect if in an specific area exist red color, in case it does i can use this information to activate a condition, i'm using a heatmap like this:
for the red color detection i'm using this code:
import cv2
import numpy as np
while(1):
# Take each frame
frame = cv2.imread('heatmap.png')
# Convert BGR to HSV
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# define range of blue color in HSV
lower_red = np.array([-20, 100, 100])
upper_red = np.array([13, 255, 255])
# Threshold the HSV image to get only blue colors
mask = cv2.inRange(hsv, lower_red, upper_red)
# Bitwise-AND mask and original image
res = cv2.bitwise_and(frame,frame, mask= mask)
cv2.imshow('heatmap',frame)
cv2.imshow('mask',mask)
cv2.imshow('res',res)
k = cv2.waitKey(5) & 0xFF
if k == 27:
break
cv2.destroyAllWindows()
the code above give me al the pixels in red, but i need to determine if the heatmap contour is red, i mean the image contour or border would be a red color not permited area, does anyone how i can do that?
Your HSV range is not right. For red, (0,20,20)~(8,255,255), (170,20,20) ~ (180,255,255).
Here is my result:
The code:
#!/usr/bin/python3
# 2018/05/16 13:54:09
import cv2
import numpy as np
img = cv2.imread('heatmap.png')
# Convert BGR to HSV
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
mask1 = cv2.inRange(hsv, (0,20,20), (8,255,255))
mask2 = cv2.inRange(hsv, (170,20,20), (180,255,255))
mask = cv2.bitwise_or(mask1, mask2)
dst = cv2.bitwise_and(img, img, mask=mask)
cv2.imshow("dst", dst)
cv2.imwrite("__.png", dst)
cv2.waitKey()
Some useful links:
Choosing the correct upper and lower HSV boundaries for color detection with`cv::inRange` (OpenCV)
How to define a threshold value to detect only green colour objects in an image :Opencv
I am working on a project that needs to identify the shape of an object in a field. My goal right now is to filter out any colors the background could be such as green, brown, or gray.
import cv2
import numpy as np
img = cv2.imread('pic1.png')
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
lower_green = np.array([0,0, 150])
upper_green = np.array([255, 255, 255])
mask = cv2.inRange(hsv, lower_green, upper_green)
res = cv2.bitwise_and(img, img, mask=mask)
cv2.imshow('res1.png', res)
cv2.waitKey(0)
The image
is showing a sample of the shapes in the field.
This code results in this image:
Why do the values I use for the lower and upper greens remove all the green when I have only included red values?
Are there any ways to get filters that remove only one color so I am able to pick and choose which colors I would want to keep and which to remove?