A Script to Analyse Effective Focal Length in Photographs

analyse_fl.py

#!/usr/bin/python
import sys
full_imgsizes = {'NIKON D750':  6000,
                 'NIKON D7200': 6000,
                 'NIKON D3300': 6000,
                 'NIKON D7100': 6000,
                 'DSC-RX100':   5472,
                 'NEX-5':       4592,
                 'NIKON D90':   4288,
                 'NIKON D80':   3872}

aspect_ratios = {'NIKON D750':  1.5,
                 'NIKON D7200': 1.5,
                 'NIKON D7100': 1.5,
                 'NIKON D3300': 1.5,
                 'DSC-RX100':   1.5,
                 'NEX-5':       1.5,
                 'NIKON D90':   1.5,
                 'NIKON D80':   1.5}

limits = [16.0, 18.0, 20.0, 24.0, 28.0, 35.0, 50.0, 70.0, 85.0, 105.0, 135.0, 180.0, 200.0, 300.0, 400.0, 450.0, 500.0, 600.0, 800.0, 900.0, 1200.0, float("inf")]

def normalise(fls):
    fls.sort()
    l_idx = 0
    fl_idx = 0
    nfls = {}
    for nfl in limits:
        nfls[nfl] = 0

    while l_idx < len(limits) and fl_idx < len(fls):
        fl = fls[fl_idx]
        if abs(limits[l_idx] - fl) < abs(limits[l_idx + 1] - fl):
            nfls[limits[l_idx]] += 1
            fl_idx += 1
        else:
            l_idx += 1
            if l_idx == len(limits):
                break
    return nfls

def calculate_fl(model, imgsize, efocal):
    if model not in full_imgsizes or imgsize == '' or efocal == '':
        return 0.0

    eq_fl = float(efocal.split(' ')[0])

    x,y = imgsize.split('x')
    long_edge, short_edge = max(float(x), float(y)), min(float(x), float(y))

    if long_edge <= 0 or short_edge <= 0:
        return 0.0

    img_aspect = long_edge/short_edge
    cam_aspect = aspect_ratios[model]
    if img_aspect < cam_aspect:
        long_edge = short_edge * cam_aspect
    
    full_long_edge = full_imgsizes[model]
    return eq_fl * full_long_edge/long_edge


def main():
    fls = []
    for line in sys.__stdin__.readlines():
        (fname,model,imgsize,efocal) = line.strip().split(',')
        fl = calculate_fl(model, imgsize, efocal)
        if fl <= 0:
            #print >> sys.stderr, line
            sys.stderr.write(line)
            continue
        fls.append(fl)

    nfls = normalise(fls)
    for fl in limits:
        print ("%.0f,%d" % (fl, nfls[fl]))

if __name__ == '__main__':
    main()