for starters, import opencv (duh) ```python import cv2 ``` I'll be omitting this line moving forward. ## Extracting the size of the image ```python {3} img = cv2.imread('test.png') print(img.shape) ``` Here `img.shape` is a tuple containing 3 values (assuming its an RGB image), for a grayscale image, it'll only have 2 values. These are the `height`, `width` and `channels`. You can multiply these to get the size of an *uncompressed* image. ```python {3} height, width, channels = img.shape height*width*channels ``` 4800000 $ 4800000 $ comes to be around 4.8 MB, while if we check the file size on disk ```bash ➜ du -h test.jpg ``` 532K test.jpg 532KB vs 4.8MB, JPEG Compression is goated. ## Converting between color modes ### tf are color modes? Color Modes could be RGB, Grayscale, HSV You might be familiar with RGB (why are u here if u dont?) To convert a RGB image to a grayscale image. ```python {2} img = cv2.imread('image.jpg') gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) cv2.imshow('Gray Image', gray) ``` Referring to the documentation of the cvtColor function ```python (function) def cvtColor( src: MatLike, code: int, dst: MatLike | None = ..., dstCn: int = ..., hint: AlgorithmHint = ... ) -> MatLike ``` The parameters we're concerned with here are the `src` and `code`, the first being a matrix (`MatLike`) of the source image, which we get by the `imread` function, the latter and the arguably more important parameter being `code` which takes in a constant code which specifies which format to convert the image to. Some valid codes are: - BGR2HSV - BGR2GRAY - BGR2GRAY (the one we're using in this example) We finally end up with another matrix (`MatLike`) that we can operate on with OpenCV ## Displaying Images Images can be displayed using either the OpenCV module or the Matplotlib module. I prefer the latter as it works with my [jup2typ](https://github.com/himanshusardana/jup2typ) script. Both modules export an `imshow` function, so you can use them as: ```python {4} import matplotlib.pyplot as plt cv2.imshow(gray) plt.imshow(gray, cmap='gray') ``` Matplotlib requires an additional `cmap` argument that specifies the color map of the image, otherwise it'll try rendering the grayscale image in RGB, resulting in a weird yellowish-purple image. If you're interested, here's the documentation for the `cmap` argument: ```txt (class) Colormap ─────────────────────────────────────────────────────────────────────────── Baseclass for all scalar to RGBA mappings. Typically, Colormap instances are used to convert data values (floats) from the interval `[0, 1]` to the RGBA color that the respective Colormap represents. For scaling of data into the `[0, 1]` interval see `matplotlib.colors.Normalize`. Subclasses of `matplotlib.cm.ScalarMappable` make heavy use of this `data -> normalize -> map-to-color` processing chain. ``` ## Useful imports ```python {1-3} import cv2 import matplotlib.pyplot as plt from skimage import calcHist ``` ## Some binary magic Specifying 0 as the `base` or the 2nd argument of the int function infers the base from the string representation of the number - `0o` $\rightarrow$ Octal - `0x` $\rightarrow$ Hexadecimal - `0b` $\rightarrow$ Binary ```python int('0b11', 0) int('0x32', base = 0) int('0o32', 0) ``` 3 50 26 | Column1 | Column2 | Column3 | | ------------- | -------------- | -------------- | | Item1 | Item1 | Item1 | | Item1 | Item1 | Item1 | | Item1 | Item1 | Item1 | | Item1 | Item1 | Item1 | This is an info block with a custom title. This is a warning without a title. Don't delete the production database. **2. Quotes** Use standard markdown quotes: > "Simplicity is the ultimate sophistication." > — Leonardo da Vinci

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