| import numpy as np |
| from utils.color import rgb2ycc |
|
|
| def illumination_parameters_estimation(current_image, illumination_estimation_option): |
| ie_method = illumination_estimation_option.lower() |
|
|
| if ie_method == "gw": |
| ie = np.mean(current_image, axis=(0, 1)) |
| ie /= ie[1] |
| return ie |
| elif ie_method == "sog": |
| sog_p = 4. |
| ie = np.mean(current_image**sog_p, axis=(0, 1))**(1/sog_p) |
| ie /= ie[1] |
| return ie |
| elif ie_method == "wp": |
| ie = np.max(current_image, axis=(0, 1)) |
| ie /= ie[1] |
| return ie |
| elif ie_method == "iwp": |
| samples_count = 10 |
| sample_size = 10 |
| rows, cols = current_image.shape[:2] |
| data = np.reshape(current_image, (rows*cols, 3)) |
| maxima = np.zeros((samples_count, 3)) |
| for i in range(samples_count): |
| maxima[i, :] = np.max(data[np.random.randint(low=0, high=rows*cols, size=(sample_size)), :], axis=0) |
| ie = np.mean(maxima, axis=0) |
| ie /= ie[1] |
| return ie |
| else: |
| raise ValueError( |
| 'Bad illumination_estimation_option value! Use the following options: "gw", "wp", "sog", "iwp"') |
| |
| |
| def ratios2floats(ratios): |
| floats = [] |
| for ratio in ratios: |
| floats.append(float(ratio.num) / ratio.den) |
| return floats |
|
|
