Basic skin detection can be accomplished through channel-based filtering. This method of detection is relatively simple, although it requires some adjustments based on scene light and camera calibration. This quick-and-dirty method can allow you do perform simple skin detection for other applications, like recognizing hand gestures or shapes.
- [Instructor] With an understanding of…the different thresholding types,…we can apply the use case of detecting…and segmenting the skin tones from an image.…This will also cover the use of…composite filtering to improve the result…if no single threshold will work on its own.…Taking a look at the faces.jpeg file…in the chapter three, module four folder,…we can see that there are a variety…of skin tones to account for.…We would find that no single threshold…or adapted threshold would do the job on its own.…Therefore, we need to combine multiple together.…Now, let's jump into our chapter three,…module four python file.…
Noting we already have loaded in…the faces.jpeg image in full color.…The first step is, we actually want…to split this into an hsv file format.…To do this, we'll type hsc = cv2.cvtColor…where the C is capitalized in Color,…then we'll pass in our image,…followed by cv2. and then capitalize…Color_BGR the number two, and then HSV.…Next, let's split the channels individually…using h = hsv[:,:,0]…to use all the pixels for the hue channel,…
AuthorPatrick W. Crawford
- Installing and configuring OpenCV
- Data types and structures
- Image types
- Manipulating pixels
- Scaling and rotating images
- Using video inputs
- Creating custom interfaces
- Object detection
- Face and feature detection
- Template matching
Skill Level Intermediate
NumPy Data Science Essential Trainingwith Charles Kelly3h 54m Intermediate
Python: Programming Efficientlywith Michele Vallisneri2h 15m Intermediate
1. Install and Configure OpenCV
2. Basic Image Operations
3. Object Detection
4. Face and Feature Detection
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