def prepare_patient_images(patient_id, intermediate_crop=0): file_lst = [] prefix = str(patient_id).rjust(4, '0') src_files = helpers.get_files(settings.BASE_PREPROCESSEDIMAGES_DIR, prefix + "*.png") patient_dir = helpers.get_pred_patient_dir(patient_id) helpers.create_dir_if_not_exists(patient_dir) patient_img_dir = helpers.get_pred_patient_img_dir(patient_id) helpers.create_dir_if_not_exists(patient_img_dir) helpers.delete_files(patient_img_dir, "*.png") dummy = numpy.zeros((settings.TARGET_SIZE, settings.TARGET_SIZE)) cv2.imwrite(patient_img_dir + "dummy_overlay.png", dummy) for src_path in src_files: file_name = ntpath.basename(src_path) org_img = cv2.imread(src_path, cv2.IMREAD_GRAYSCALE) cropped_img = helpers.prepare_cropped_sax_image(org_img, clahe=True, intermediate_crop=intermediate_crop, rotate=0) if SCALE_SIZE is not None: cropped_img = cv2.resize(cropped_img, (SCALE_SIZE, SCALE_SIZE), interpolation=cv2.INTER_AREA) cv2.imwrite(patient_img_dir + file_name, cropped_img) file_lst.append([file_name, "dummy_overlay.png"]) with open(patient_img_dir + "pred.lst", "wb") as f: writer = csv.writer(f, delimiter='\t') writer.writerows(file_lst)
dia_line = [str(patient_id) + "_Diastole"] + map(str, pred_row_dia) sys_line = [str(patient_id) + "_Systole"] + map(str, pred_row_sys) #sys_line = "501_Systole" ",".join(map(str,pred_row_sys)) pred_lines.append(dia_line) pred_lines.append(sys_line) crps_dia = sum(agg_crps_dia) / len(agg_crps_dia) crps_sys = sum(agg_crps_sys) / len(agg_crps_sys) print "Crps dia = " + str(crps_dia) print "Crps sys = " + str(crps_sys) print "Crps agg = " + str((crps_dia + crps_sys) / 2) if submission_file != "": with open(submission_file, "wb") as f: writer = csv.writer(f) writer.writerows(pred_lines) if __name__ == "__main__": helpers.create_dir_if_not_exists(settings.BASE_DIR + "submission_files\\") stdevs = generate_stdevs(1, 590, 600, 700, window_size=60) # patient 595 and 599 are corrupt make_predictions(PREDICT_FILE_PATH, 1, 500, stdevs) make_predictions(PREDICT_FILE_PATH, 501, 700, stdevs) make_predictions(PREDICT_FILE_PATH, 1, 700, stdevs) make_predictions(PREDICT_FILE_PATH, 701, 1140, stdevs, submission_file=settings.BASE_DIR + "submission_files\\submission" + MODEL_NAME + ".csv") print "Done , submission written to : " + settings.BASE_DIR + "submission_files\\submission" + MODEL_NAME + ".csv" # make_predictions("G:\\werkdata\\kaggle\\ndsb2\\prediction_data_allscale.csv", 250, 700)
crps_dia = sum(agg_crps_dia) / len(agg_crps_dia) crps_sys = sum(agg_crps_sys) / len(agg_crps_sys) print("Crps dia = " + str(crps_dia)) print("Crps sys = " + str(crps_sys)) print("Crps agg = " + str((crps_dia + crps_sys) / 2)) if submission_file != "": with open(submission_file, "wb") as f: writer = csv.writer(f) writer.writerows(pred_lines) if __name__ == "__main__": helpers.create_dir_if_not_exists(settings.BASE_DIR + "submission_files\\") stdevs = generate_stdevs(1, 590, 600, 700, window_size=60) # patient 595 and 599 are corrupt make_predictions(PREDICT_FILE_PATH, 1, 500, stdevs) make_predictions(PREDICT_FILE_PATH, 501, 700, stdevs) make_predictions(PREDICT_FILE_PATH, 1, 700, stdevs) make_predictions(PREDICT_FILE_PATH, 701, 1140, stdevs, submission_file=settings.BASE_DIR + "submission_files\\submission" + MODEL_NAME + ".csv") print("Done , submission written to : " + settings.BASE_DIR + "submission_files\\submission" + MODEL_NAME + ".csv") # make_predictions("G:\\werkdata\\kaggle\\ndsb2\\prediction_data_allscale.csv", 250, 700)