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test.py
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test.py
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# -*- coding: utf-8 -*-
# @Time : 20-6-9 下午3:06
# @Author : zhuying
# @Company : Minivision
# @File : test.py
# @Software : PyCharm
import os
import cv2
import numpy as np
import argparse
import warnings
import time
from tqdm import tqdm
from src.anti_spoof_predict import AntiSpoofPredict
from src.generate_patches import CropImage
from src.utility import parse_model_name
warnings.filterwarnings('ignore')
SAMPLE_IMAGE_PATH = "./images/sample/"
SAMPLE_VIDEO = "rtsp://admin:[email protected]:554/"
# 因为安卓端APK获取的视频流宽高比为3:4,为了与之一致，所以将宽高比限制为3:4
def check_image(image):
height, width, channel = image.shape
if width/height != 3/4:
print("Image is not appropriate!!!\nHeight/Width should be 4/3.")
return False
else:
return True
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 i, model_name in enumerate(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()
this_predict = model_test.predict(img, os.path.join(model_dir, model_name))
print(f"Infer {i}: {this_predict}")
prediction += this_predict
test_speed += time.time()-start
print("=============-.-=============")
# 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} s".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):
model_test_1 = AntiSpoofPredict(device_id)
model_test_2 = AntiSpoofPredict(device_id)
models = [model_test_1, model_test_2]
image_cropper = CropImage()
cap = cv2.VideoCapture(SAMPLE_VIDEO)
ret = True
params = []
ret, image = cap.read()
result = check_image(image)
image_bbox = models[0].get_bbox(image)
for i, model_name in enumerate(os.listdir(model_dir)):
fake_img = image
h_input, w_input, model_type, scale = parse_model_name(model_name)
param = {
"org_img": fake_img,
"bbox": image_bbox,
"scale": scale,
"out_w": w_input,
"out_h": h_input,
"crop": True,
}
if scale is None:
param["crop"] = False
fake_img = image_cropper.crop(**param)
params.append(param)
this_predict = models[i].predict(fake_img, os.path.join(model_dir, model_name))
out = cv2.VideoWriter('outpy.avi',cv2.VideoWriter_fourcc('M','J','P','G'), 15, (image.shape[1],image.shape[0]))
while ret:
ret, image = cap.read()
if not ret:
break
# result = check_image(image)
# if result is False:
# continue
image_bbox = models[i].get_bbox(image)
prediction = np.zeros((1, 3))
test_speed = 0
# sum the prediction from single model's result
for i, param in enumerate(params):
param["org_img"] = image
param["bbox"] = image_bbox
img = image_cropper.crop(**param)
start = time.time()
this_predict = models[i]._predict(img)
print(f"Infer {i}: {this_predict}")
prediction += this_predict
test_speed += time.time()-start
print("=============-.-=============")
# 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} s".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.imshow("images", image)
if cv2.waitKey(1) == ord('q'):
break
out.write(image)
cap.release()
out.release()
if __name__ == "__main__":
desc = "test"
parser = argparse.ArgumentParser(description=desc)
parser.add_argument(
"--device_id",
type=int,
default=0,
help="which gpu id, [0/1/2/3]")
parser.add_argument(
"--model_dir",
type=str,
default="./resources/anti_spoof_models/pth",
help="model_lib used to test")
parser.add_argument(
"--image_name",
type=str,
default="image_F1.jpg",
help="image used to test")
args = parser.parse_args()
_test(args.image_name, args.model_dir, args.device_id)