Source code for coseda.pipeline.processimage

import numpy as np
from diffractem.peakfinder8_extension import peakfinder_8



def process_image(i, image_data, x0, y0, cutoff, cutoff_low, threshold, min_snr, min_pix_count, max_pix_count, local_bg_radius, min_res, max_res):
    nPeaks = 0
    X, Y = np.meshgrid(range(image_data.shape[1]), range(image_data.shape[0]))
    R = np.sqrt((X-x0)**2 + (Y-y0)**2).astype(np.float32)
    
    mask = np.ones_like(image_data, dtype=np.int8)
    mask[R > max_res] = 0
    mask[R < min_res] = 0

    pks = peakfinder_8(500, image_data.astype(np.float32), mask, R, image_data.shape[1], image_data.shape[0], 1, 1, threshold, min_snr, min_pix_count, max_pix_count, local_bg_radius)
    nPeaks = len(pks[0])
    #print(nPeaks)
    if pks is None or len(pks[0]) == 0:
        fill = [0] * (500)
        return {
            'index': i,
            'nPeaks': 0,
            'peakTotalIntensity': np.array(fill),
            'peakXPosRaw': np.array(fill),
            'peakYPosRaw': np.array(fill),
        }

    fill = [0] * (500 - nPeaks)
    return {
        'index': i,
        'nPeaks': nPeaks,
        'peakTotalIntensity': np.array(pks[2] + fill),
        'peakXPosRaw': np.array(pks[0] + fill),
        'peakYPosRaw': np.array(pks[1] + fill),
    }