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)
