test.py

import time

import cv2

from detector.cv_face_detector.model import CVFaceDetector
from detector.insight_detector.model import InsightDetector
from models.m1.model import M1FaceAntiSpoofing
from models.m2.model import M2FaceAntiSpoofing
from models.m3.model import M3FaceAntiSpoofing
from models.m4.model import M4FaceAntiSpoofing
from models.m5.model import M5FaceAntiSpoofing
from models.m6.model import M6FaceAntiSpoofing
from models.m7.model import M7FaceAntiSpoofing
from models.m8.model import M8FaceAntiSpoofing
import os

face_detector = InsightDetector()
spoof_detectors = [M8FaceAntiSpoofing()]
benchmark_dir = "benchmarks"
is_face = False
for spoof_detector in spoof_detectors:
    print("Start ----------------------------- ", type(spoof_detector))
    total_time = 0
    all_count = 0
    correct_count = 0
    errors = []
    for class_name in ["fake", "real"]:
        class_path = os.path.join(benchmark_dir, class_name)
        for image_name in os.listdir(class_path):
            image_path = os.path.join(class_path, image_name)

            bgr = cv2.imread(image_path)

            if bgr is None:
                continue
            if not is_face:
                face_bboxes = face_detector.get_face_bboxes(bgr)
            else:
                face_bboxes = [[0, 0, bgr.shape[1], bgr.shape[0]]]

            if len(face_bboxes) == 0:
                print("No face found " + image_name + " in class " + class_name)
            for box in face_bboxes:
                bbox = box["box"]
                start_time = time.time()
                crop = bgr[bbox[1]:bbox[3], bbox[0]:bbox[2], :]
                if "crop" in box:
                    crop = box["crop"]
                #cv2.imshow("crop", crop)
                #cv2.waitKey(0)
                real_score = spoof_detector.get_real_score(bgr, bbox)

                total_time += time.time() - start_time
                print("Real score for image name " + image_name + " in class " + class_name + " is: ",
                      real_score)

                if class_name == "fake":
                    if real_score < 0.5:
                        is_correct = True
                    else:
                        is_correct = False
                    errors.append(real_score)

                else:
                    if real_score >= 0.5:
                        is_correct = True
                    else:
                        is_correct = False
                    errors.append(1 - real_score)

                if is_correct:
                    correct_count += 1

                all_count += 1
                print("Correct prediction: ", is_correct)

    print("--- Average time for each face: ", total_time / all_count, " seconds")
    print("--- Total count: ", all_count)
    print("--- Correct count: ", correct_count)
    print("--- Accuracy: ", correct_count/all_count)
    print("--- Average error: ", (sum(errors) / len(errors)) * 100, "%")
    print("End ----------------------------- ", type(spoof_detector))