arcjetCV.utils.utils module
- arcjetCV.utils.utils.annotateImage(orig, flags, top=True, left=True)[source]
Annotates the original image with flags indicating overexposure and underexposure.
- Parameters:
orig – original image
flags – dictionary of flags indicating overexposure and underexposure
top – boolean, whether to annotate on the top of the image
left – boolean, whether to annotate on the left side of the image
- arcjetCV.utils.utils.annotate_image_with_frame_number(image, frame_number)[source]
Annotates the given image with the frame number.
- Parameters:
image – image to annotate
frame_number – frame number to annotate
- arcjetCV.utils.utils.clahe_normalize(bgr)[source]
Applies Contrast Limited Adaptive Histogram Equalization (CLAHE) normalization to the given BGR image.
- Parameters:
bgr – BGR image
- Returns:
normalized BGR image
- arcjetCV.utils.utils.getOutlierMask(metrics)[source]
Computes the outlier mask using Local Outlier Factor (LOF).
- Parameters:
metrics – metrics data
- Returns:
outlier mask
- arcjetCV.utils.utils.smooth(x, window_len=11, window='hanning')[source]
Smooths the data using a window with the requested size.
- Parameters:
x – the input signal
window_len – the dimension of the smoothing window; should be an odd integer
window – the type of window from ‘flat’, ‘hanning’, ‘hamming’, ‘bartlett’, ‘blackman’ flat window will produce a moving average smoothing.
- Returns:
the smoothed signal
Example:
`python t = np.linspace(-2, 2, 0.1) x = np.sin(t) + np.random.randn(len(t)) * 0.1 y = smooth(x) `