Detecting multiple bright spots in an image with Python and OpenCV
Detecting multiple bright spots in an image with Python and OpenCV
Normally when I do code-based tutorials on the PyImageSearch blog I follow a pretty standard template of:
- Explaining what the problem is and how we are going to solve it.
- Providing code to solve the project.
- Demonstrating the results of executing the code.
This template tends to work well for 95% of the PyImageSearch blog
posts, but for this one, I’m going to squash the template together into a
single step.
I feel that the problem of detecting the brightest regions of an
image is pretty self-explanatory so I don’t need to dedicate an entire
section to detailing the problem.
I also think that explaining each block of code followed by
immediately showing the
output of executing that respective block of code will help you better understand what’s going on.
So, with that said, take a look at the following image:
Figure 1: The example image that we are detecting multiple bright objects in using computer vision and image processing techniques (
source image).
In this image we have five lightbulbs.
Our goal is to detect these five lightbulbs in the image and uniquely label them.
To get started, open up a new file and name it
detect_bright_spots.py . From there, insert the following code:
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# 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())
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Lines 2-7 import our required Python packages. We’ll be using
scikit-image in this tutorial, so if you don’t already have it installed on your system be sure to
follow these install instructions.
We’ll also be using
imutils, my set of convenience functions used to make applying image processing operations easier.
If you don’t already have
imutils installed on your system, you can use
pip to install it for you:
From there,
Lines 10-13 parse our command line arguments. We only need a single switch here,
--image , which is the path to our input image.
To start detecting the brightest regions in an image, we first need
to load our image from disk followed by converting it to grayscale and
smoothing (i.e., blurring) it to reduce high frequency noise:
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# 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)
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The output of these operations can be seen below:
Figure 2: Converting our image to grayscale and blurring it.
Notice how our
image is now (1) grayscale and (2) blurred.
To reveal the
brightest regions in the blurred image we need to apply thresholding:
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# threshold the image to reveal light regions in the
# blurred image
thresh = cv2.threshold(blurred, 200, 255, cv2.THRESH_BINARY)[1]
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This operation takes any pixel value
p >= 200 and sets it to
255 (white). Pixel values
< 200 are set to
0 (black).
After thresholding we are left with the following image:
Figure 3: Applying thresholding to reveal the brighter regions of the image.
Note how the bright areas of the image are now all
white while the rest of the image is set to
black.
However, there is a bit of noise in this image (i.e., small blobs),
so let’s clean it up by performing a series of erosions and dilations:
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# 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)
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After applying these operations you can see that our
thresh
image is much “cleaner”, although we do still have a few left over
blobs that we’d like to exclude (we’ll handle that in our next step):
Figure 4:
Utilizing a series of erosions and dilations to help “clean up” the
thresholded image by removing small blobs and then regrowing the
remaining regions.
The critical step in this project is to
label each of the
regions in the above figure; however, even after applying our erosions
and dilations we’d still like to filter out any leftover “noisy”
regions.
An excellent way to do this is to perform a
connected-component analysis:
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# 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:
continue
# 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)
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Line 32 performs the actual connected-component analysis using the scikit-image library. The
labels variable returned from
measure.label has the exact same dimensions as our
thresh image — the only difference is that
labels stores a
unique integer for
each blob in
thresh .
We then initialize a
mask on
Line 33 to store only the large blobs.
On
Line 36 we start looping over each of the unique
labels . If the
label is zero then we know we are examining the background region and can safely ignore it (
Lines 38 and 39).
Otherwise, we construct a mask for
just the current
label on
Lines 43 and 44.
I have provided a GIF animation below that visualizes the construction of the
labelMask for each
label . Use this animation to help yourself understand how each of the individual components are accessed and displayed:
Figure 5: A visual animation of applying a connected-component analysis to our thresholded image.
Line 45 then counts the number of non-zero pixels in the
labelMask . If
numPixels exceeds a pre-defined threshold (in this case, a total of
300 pixels), then we consider the blob “large enough” and add it to our
mask .
The output
mask can be seen below:
Figure 6: After applying a connected-component analysis we are left with only the larger blobs in the image (which are also bright).
Notice how any small blobs have been filtered out and only the large blobs have been retained.
The last step is to draw the labeled blobs on our image:
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# find the contours in the mask, then sort them from left to
# right
cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
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)
cv2.circle(image, (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)
cv2.waitKey(0)
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First, we need to detect the contours in the
mask image and then sort them from left-to-right (
Lines 54-57).
Once our contours have been sorted we can loop over them individually (
Line 60).
For each of these contours we’ll compute the
minimum enclosing circle (
Line 63) which represents the
area that the bright region encompasses.
We then uniquely label the region and draw it on our
image (
Lines 64-67).
Finally,
Lines 70 and 71 display our output results.
To visualize the output for the lightbulb image be sure to download
the source code + example images to this blog post using the
“Downloads” section found at the bottom of this tutorial.
From there, just execute the following command:
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$ python detect_bright_spots.py --image images/lights_01.png
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You should then see the following output image:
Figure 7: Detecting multiple bright regions in an image with Python and OpenCV.
Notice how each of the lightbulbs has been uniquely labeled with a
circle drawn to encompass each of the individual bright regions.
You can visualize a a second example by executing this command:
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$ python detect_bright_spots.py --image images/lights_02.png
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Figure 8: A second example of detecting multiple bright regions using computer vision and image processing techniques (
source image).
This time there are
many lightbulbs in the input image!
However, even with many bright regions in the image our method is still
able to correctly (and uniquely) label each of them.
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