def testThreeClasses1(self): segm = np.array([[0,2,0,0,0], [0,0,0,0,0]]) gt = np.array([[1,0,0,0,0], [0,0,0,0,0]]) res = es.frequency_weighted_IU(segm, gt) # Almost equal! self.assertAlmostEqual(res, (1.0/10.0)*((9.0*8.0/10.0)+(1.0*0.0/1.0)))
def main(): parser = \ argparse.ArgumentParser( prog='parser', formatter_class=argparse.RawDescriptionHelpFormatter, description=textwrap.dedent(''' ************************************************************** Result Evaluation ----------------------- The path to Result and Reference Pictures is user-defined path. ************************************************************** ''') ) parser.add_argument('--result_image_path', type=str, help='the path to the result images') parser.add_argument('--ref_image_path', type=str, help='the path to the davis reference path') args = parser.parse_args() result_image_path = args.result_image_path if not result_image_path: raise Exception("No result image path is given neither in " "command line nor environment arguements") ref_image_path = args.ref_image_path if not ref_image_path: raise Exception("No reference images path is given neither in " "command line nor environment arguements") result_imgs = [] ref_imgs = [] for a, b, result_files in os.walk(result_image_path): result_imgs = result_files for a, b, ref_files in os.walk(ref_image_path): ref_imgs = ref_files # if len(result_imgs) != len(ref_imgs): # raise Exception("The number of image in result and ref is not match. Cannot evaluate.") print("+", "-".center(85, '-'), "+") print("|", "pixel_accuracy".center(20), "mean_IU".center(20), "mean_accuracy".center(20), "frequency_weighted_IU".center(20), "|".rjust(2)) result_image_filenames = os.listdir(result_image_path) ref_image_filenames = os.listdir(ref_image_path) image_converter = ImageConverter() each_pixel_accuracy = [] each_mean_IU = [] each_mean_accuracy = [] each_frequency_weighted_IU = [] for i in range(len(result_imgs)): print("filename:", result_image_filenames[i]) result_imgs[i] = image_converter.image_to_array(os.path.join(result_image_path, result_image_filenames[i])) ref_imgs[i] = image_converter.image_to_array(os.path.join(ref_image_path, ref_image_filenames[i])) i_pixel_accuracy = pixel_accuracy(result_imgs[i], ref_imgs[i]) //i_mean_IU = mean_IU(result_imgs[i], ref_imgs[i]) i_mean_IU = db_eval_iou(ref_imgs[i], result_imgs[i]) i_mean_accuracy = mean_accuracy(result_imgs[i], ref_imgs[i]) i_frequency_weighted_IU = frequency_weighted_IU(result_imgs[i], ref_imgs[i]) each_pixel_accuracy.append(i_pixel_accuracy) each_mean_IU.append(i_mean_IU) each_mean_accuracy.append(i_mean_accuracy) each_frequency_weighted_IU.append(i_frequency_weighted_IU) print("|", str(i_pixel_accuracy).center(20), str(i_mean_IU).center(20), str(i_mean_accuracy).center(20), str(i_frequency_weighted_IU).center(20), "|".rjust(2)) print("| mean value:", str(np.mean(each_pixel_accuracy)).center(12), str(np.mean(each_mean_IU)).center(20), str(np.mean(each_mean_accuracy)).center(20), str(np.mean(each_frequency_weighted_IU)).center(20),"|".rjust(2)) print("+", '-'.center(85, '-'), "+")
def forward(self, bottom, top): label = np.squeeze(bottom[1].data) prediction = np.squeeze(np.argmax(bottom[0].data, axis=1)) top[0].data[...] = se.frequency_weighted_IU(prediction,label)
def testFiveClasses0(self): segm = np.array([[1,2,3,4,3], [0,0,0,0,0]]) gt = np.array([[1,0,3,0,0], [0,0,0,0,0]]) res = es.frequency_weighted_IU(segm, gt) self.assertEqual(res, (1.0/10.0)*((8.0*5.0/8.0)+(1.0*1.0/1.0)+(1.0*1.0/2.0)))
def testFourClasses1(self): segm = np.array([[1,2,3,0,0], [0,0,0,0,0]]) gt = np.array([[1,0,0,0,0], [0,0,0,0,0]]) res = es.frequency_weighted_IU(segm, gt) self.assertEqual(res, (1.0/10.0)*((9.0*7.0/9.0)+(1.0*1.0/1.0)))
def testTwoClasses1(self): segm = np.array([[1,0,0,0,0], [0,0,0,0,0]]) gt = np.array([[0,0,0,0,0], [0,0,0,0,0]]) res = es.frequency_weighted_IU(segm, gt) self.assertEqual(res, (1.0/10.0)*(10.0*9.0/10.0))
def testTwoClasses0(self): segm = np.array([[1,1,1,1,1], [1,1,1,1,1]]) gt = np.array([[0,0,0,0,0], [0,0,0,0,0]]) res = es.frequency_weighted_IU(segm, gt) self.assertEqual(res, 0)
def testOneClass(self): segm = np.array([[0,0], [0,0]]) gt = np.array([[0,0], [0,0]]) res = es.frequency_weighted_IU(segm, gt) self.assertEqual(res, 1.0)
pred[cls_3] = 1 pred[cls_2] = 2 pred[cls_4] = 2 pred[cls_5] = 2 pred[cls_6] = 2 pred[cls_7] = 2 pred[cls_8] = 2 """ print('unique label after is {}'.format(np.unique(label))) print('unique pred after is {}'.format(np.unique(pred))) pa = eval_segm.pixel_accuracy(pred, label) ma = eval_segm.mean_accuracy(pred, label) rate_precision_mean = eval_segm.mean_precision(pred, label) m_iu, iu = eval_segm.mean_IU(pred, label) fw_iu = eval_segm.frequency_weighted_IU(pred, label) pa_list.append(pa) ma_list.append(ma) list_rate_precision_mean.append(rate_precision_mean) iu_list.append(iu) m_iu_list.append(m_iu) fw_iu_list.append(fw_iu) num_true_positives += eval_segm.get_num_true_positives(pred, label) num_false_positives += eval_segm.get_num_false_positives(pred, label) counts.append(count) print(np.array(count), np.array(gt_count[_name])) acc[i] = 1 - ((np.abs(np.array(count) - np.array(gt_count[_name]))) / np.array(gt_count[_name])) # print("pixel_accuracy: " + str(pa)) # print("mean_accuracy: " + str(ma)) # print("IU: " + str(iu))