def calculate_angular_error_metric()

in ParametricLights.py [0:0]

• 28 lines of code
• 15 McCabe index (conditional complexity)

def calculate_angular_error_metric(gt_lights, my_lights, top_k=5):
        # for each gt light find closest predicted light
        if len(gt_lights) < 1 or len(my_lights) < 1:
            print('No lights here')
            return -1
        # trying to compensate for  python sizing
        if len(np.shape(gt_lights)) < 2:
            # there is just 1 light
            gt_lights = np.stack((gt_lights, gt_lights))
        if len(np.shape(my_lights)) < 2:
            # there is just 1 light
            my_lights = np.stack((my_lights, my_lights))

        top_k = np.minimum(top_k, np.shape(gt_lights)[0])
        top_k = np.minimum(top_k, np.shape(my_lights)[0])

        err = 0.0
        if len(gt_lights) > 0 and len(my_lights) > 0:
            for i in range(0, top_k):
                mne = float("inf")
                for j in range(0, top_k):
                    e_ = angularErrorBetweenLights(gt_lights[i], my_lights[j])
                    if e_ < mne:
                        mne = e_
                err += mne

            for i in range(0, top_k):
                mne = float("inf")
                for j in range(0, top_k):
                    e_ = angularErrorBetweenLights(my_lights[i], gt_lights[j])
                    if e_ < mne:
                        mne = e_

                err += mne

            err /= (top_k + top_k)

        return err