Line 32 performs the actual connected-component analysis using the scikit-image library. This operation takes any pixel value p >= 200 and sets it to 255 (white). Thresh = cv2.threshold(blurred, 200, 255, cv2.THRESH_BINARY) ![]() To reveal the brightest regions in the blurred image we need to apply thresholding: # threshold the image to reveal light regions in the Notice how our image is now (1) grayscale and (2) blurred. The output of these operations can be seen below: Figure 2: Converting our image to grayscale and blurring it. Gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)īlurred = cv2.GaussianBlur(gray, (11, 11), 0) 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: # load the image, convert it to grayscale, and blur it ![]() We only need a single switch here, -image, which is the path to our input image. If you don’t already have imutils installed on your system, you can use pip to install it for you: $ pip install -upgrade imutilsįrom there, Lines 10-13 parse our command line arguments. We’ll also be using imutils, my set of convenience functions used to make applying image processing operations easier. 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. Lines 2-7 import our required Python packages. # construct the argument parse and parse the argumentsĪp.add_argument("-i", "-image", required=True, From there, insert the following code: # import the necessary packages To get started, open up a new file and name it detect_bright_spots.py. Our goal is to detect these five lightbulbs in the image and uniquely label them. 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). 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. ![]() 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. 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.
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