def test(image_name, model_dir, device_id):
model_test = AntiSpoofPredict(device_id)
image_cropper = CropImage()
image = cv2.imread(SAMPLE_IMAGE_PATH + image_name)
result = check_image(image)
if result is False:
return
image_bbox = model_test.get_bbox(image)
prediction = np.zeros((1, 3))
test_speed = 0
# sum the prediction from single model's result
for model_name in os.listdir(model_dir):
h_input, w_input, model_type, scale = parse_model_name(model_name)
param = {
"org_img": image,
"bbox": image_bbox,
"scale": scale,
"out_w": w_input,
"out_h": h_input,
"crop": True,
}
if scale is None:
param["crop"] = False
img = image_cropper.crop(**param)
start = time.time()
prediction += model_test.predict(img, os.path.join(model_dir, model_name))
test_speed += time.time()-start
# draw result of prediction
label = np.argmax(prediction)
value = prediction[0][label]/2
if label == 1:
print("Image '{}' is Real Face. Score: {:.2f}.".format(image_name, value))
result_text = "RealFace Score: {:.2f}".format(value)
color = (255, 0, 0)
else:
print("Image '{}' is Fake Face. Score: {:.2f}.".format(image_name, value))
result_text = "FakeFace Score: {:.2f}".format(value)
color = (0, 0, 255)
print("Prediction cost {:.2f} ms".format(test_speed))
cv2.rectangle(
image,
(image_bbox[0], image_bbox[1]),
(image_bbox[0] + image_bbox[2], image_bbox[1] + image_bbox[3]),
color, 2)
cv2.putText(
image,
result_text,
(image_bbox[0], image_bbox[1] - 5),
cv2.FONT_HERSHEY_COMPLEX, 0.5*image.shape[0]/1024, color)
format_ = os.path.splitext(image_name)[-1]
result_image_name = image_name.replace(format_, "_result" + format_)
cv2.imwrite(SAMPLE_IMAGE_PATH + result_image_name, image)
def test(image_name, model_dir, device_id):
## If input is an image from a folder ##
# image_name = cv2.imread(image_name)
model_test = AntiSpoofPredict(device_id)
image_cropper = CropImage()
image_bbox = model_test.get_bbox(image_name)
prediction = np.zeros((1, 3))
test_speed = 0
for model_name in os.listdir(model_dir):
h_input, w_input, model_type, scale = parse_model_name(model_name)
param = {
"org_img": image_name,
"bbox": image_bbox,
"scale": scale,
"out_w": w_input,
"out_h": h_input,
"crop": True,
}
if scale is None:
param["crop"] = False
img = image_cropper.crop(**param)
start = time.time()
prediction += model_test.predict(img,
os.path.join(model_dir, model_name))
test_speed += time.time() - start
# draw result of prediction
label = np.argmax(prediction)
value = prediction[0][label] / 2
if label == 1 and value >= 0.6:
print("Real Face.")
result_text = "RealFace"
color = (255, 0, 0)
else:
print("Fake Face.")
result_text = "FakeFace"
color = (0, 0, 255)
print("Prediction speed {:.2f} s".format(test_speed))
return args
# if __name__ == "__main__":
args = arg_parse()
# test(args.image_name, args.model_dir, args.device_id)
# def test(image_name, model_dir, device_id):
image_name = args.image_name
model_dir = args.model_dir
device_id = args.device_id
model_test = AntiSpoofPredict(device_id)
image_cropper = CropImage()
image = cv2.imread(SAMPLE_IMAGE_PATH + image_name)
result = check_image(image)
if result is True:
image_bbox = model_test.get_bbox(image)
prediction = np.zeros((1, 3))
test_speed = 0
model_names = [os.path.join(model_dir, "epoch_77.pth")]
# sum the prediction from single model's result
for model_name in model_names:
# h_input, w_input, model_type, scale = parse_model_name(model_name)
h_input, w_input, model_type, scale = 80, 80, None, 1
param = {
"org_img": image,
"bbox": image_bbox,
"scale": scale,
"out_w": w_input,
"out_h": h_input,
"crop": False,
}