Python + opencv detection light highlights

Procedural ape without hair loss 2020-11-13 05:33:50
python opencv detection light highlights

In this blog post, I share an article about looking for light highlights in images ( The highlight of the image ) A tutorial for , for example , Detect the bright spots of five lights in the image and mark , The effect of the project is as follows :

The first 1 Step : Import and open the original image , The implementation code is as follows :

# import the necessary packages
from imutils import contours
from skimage import measure
import numpy as np
import argparse
import imutils
import cv2
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
help="path to the image file")
args = vars(ap.parse_args())

The first 2 Step : Start detecting the brightest area in the image , First, you need to load the image from disk , Then it is converted into gray image and smoothed and filtered , To reduce high frequency noise , The implementation code is as follows :

#load the image, convert it to grayscale, and blur it
image = cv2.imread(args["image"])
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (11, 11), 0)

Import light image , The effect after filtration is as follows :

The first 3 Step : Threshold processing , To show the brightest area of the blurred image , Set the pixel value p >= 200, Set to 255( white ), Pixel values < 200, Set to 0( black ), The implementation code is as follows :

# threshold the image to reveal light regions in the
# blurred image
thresh = cv2.threshold(blurred, 200, 255, cv2.THRESH_BINARY)[1]

The effect is as follows :

  The first 4 Step : At this point, you can see that there is noise in the image ( Speckles ), So it needs to be removed by operation , The implementation code is as follows :

# perform a series of erosions and dilations to remove
# any small blobs of noise from the thresholded image
thresh = cv2.erode(thresh, None, iterations=2)
thresh = cv2.dilate(thresh, None, iterations=4)

here “ clean ” Here's the image of :

The first 5 Step : The key step in this project is to mark each area in the above figure , Even after the application of corrosion and expansion , Still want to filter out the remaining small areas . A good way to do this is to perform link component analysis , The implementation code is as follows :

# perform a connected component analysis on the thresholded
# image, then initialize a mask to store only the "large"
# components
labels = measure.label(thresh, neighbors=8, background=0)
mask = np.zeros(thresh.shape, dtype="uint8")
# loop over the unique components
for label in np.unique(labels):
# if this is the background label, ignore it
if label == 0:
# otherwise, construct the label mask and count the
# number of pixels
labelMask = np.zeros(thresh.shape, dtype="uint8")
labelMask[labels == label] = 255
numPixels = cv2.countNonZero(labelMask)
# if the number of pixels in the component is sufficiently
# large, then add it to our mask of "large blobs"
if numPixels > 300:
mask = cv2.add(mask, labelMask)

In the above code , The first 4 Line usage scikit-image The library performs the actual link component analysis .measure.lable Back to label It's the same size as the threshold image , The only difference is label It stores the positive integer corresponding to each spot in the threshold image .

Then in the first 5 Line initializes a mask to store large spots .

The first 7 The line begins to loop through each label The positive integer label in , If the label is zero , It means that the background is being detected and can be safely ignored (9,10 That's ok ). otherwise , Build a mask for the current region .

Here's a GIF Animation , It visually constructs the... For each tag labelMask. Use this animation to help you understand how to access and display each individual component :

The first 15 Right labelMask Count non-zero pixels in the . If numPixels Exceeded a pre-defined threshold ( In this case , The total number is 300 Pixels ), So think of this spot “ Large enough ”, And add it to the mask . The output mask is shown in the figure below :

The first 6 Step : At this point, all the small spots in the image are filtered out , Only the big spots are preserved . The last step is to draw marked spots on the image of , The implementation code is as follows :

# find the contours in the mask, then sort them from left to
# right
cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
cnts = imutils.grab_contours(cnts)
cnts = contours.sort_contours(cnts)[0]
# loop over the contours
for (i, c) in enumerate(cnts):
# draw the bright spot on the image
(x, y, w, h) = cv2.boundingRect(c)
((cX, cY), radius) = cv2.minEnclosingCircle(c), (int(cX), int(cY)), int(radius),
(0, 0, 255), 3)
cv2.putText(image, "#{}".format(i + 1), (x, y - 15),
cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
# show the output image
cv2.imshow("Image", image)

  Last run program , It can detect and mark the light spot , Each light bulb is uniquely marked with a circle , Circles surround each individual bright area , The effect is as follows :

This article comes from :Detecting multiple bright spots in an image with Python and OpenCV

本文为[Procedural ape without hair loss]所创,转载请带上原文链接,感谢

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