Edit test.test. Update requirements. Relative paths in anti_spoof_pre…

requirements.txt

@@ -1,8 +1,8 @@
-easydict==1.9
-numpy==1.17.0
-tqdm==4.31.1
-torchvision==0.4.0
-torch==1.2.0
-opencv_python==4.2.0.34
-Pillow==7.1.2
-tensorboardX==2.0
+easydict==1.10
+numpy==1.23.5
+tqdm==4.64.1
+torchvision==0.14.1
+torch==1.13.1
+opencv-python==4.6.0.66
+Pillow==9.3.0
+tensorboardX==2.5.1

src/anti_spoof_predict.py

@@ -6,6 +6,8 @@
 # @Software : PyCharm
 
 import os
+import traceback
+
 import cv2
 import math
 import torch
@@ -27,8 +29,12 @@
 
 class Detection:
     def __init__(self):
-        caffemodel = "./resources/detection_model/Widerface-RetinaFace.caffemodel"
-        deploy = "./resources/detection_model/deploy.prototxt"
+        stack = traceback.extract_stack()
+        dirname = os.path.dirname(stack[-2].filename)
+
+        caffemodel = os.path.join(dirname, '..', 'resources', 'detection_model', 'Widerface-RetinaFace.caffemodel')
+        deploy = os.path.join(dirname, '..', 'resources', 'detection_model', 'deploy.prototxt')
+
         self.detector = cv2.dnn.readNetFromCaffe(deploy, caffemodel)
         self.detector_confidence = 0.6
 
@@ -101,3 +107,4 @@ def predict(self, img, model_path):
 
 
 
+

test.py

@@ -31,10 +31,11 @@ def check_image(image):
         return True
 
 
-def test(image_name, model_dir, device_id):
+def test(image, model_dir, device_id):
     model_test = AntiSpoofPredict(device_id)
     image_cropper = CropImage()
-    image = cv2.imread(SAMPLE_IMAGE_PATH + image_name)
+    # image = cv2.imread(SAMPLE_IMAGE_PATH + image_name)
+    image = cv2.resize(image, (int(image.shape[0] * 3 / 4), image.shape[0]))
     result = check_image(image)
     if result is False:
         return
@@ -62,29 +63,8 @@ def test(image_name, model_dir, device_id):
     # 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)
+    return label
 
 
 if __name__ == "__main__":