Update links to OpenCV's face detection network

modules/dnn/misc/face_detector_accuracy.py

@@ -17,7 +17,6 @@ parser = argparse.ArgumentParser(
                     'using COCO evaluation tool, http://cocodataset.org/#detections-eval')
 parser.add_argument('--proto', help='Path to .prototxt of Caffe model or .pbtxt of TensorFlow graph')
 parser.add_argument('--model', help='Path to .caffemodel trained in Caffe or .pb from TensorFlow')
-parser.add_argument('--caffe', help='Indicate that tested model is from Caffe. Otherwise model from TensorFlow is expected.', action='store_true')
 parser.add_argument('--cascade', help='Optional path to trained Haar cascade as '
                                       'an additional model for evaluation')
 parser.add_argument('--ann', help='Path to text file with ground truth annotations')
@@ -141,10 +140,7 @@ with open('annotations.json', 'wt') as f:
 ### Obtain detections ##########################################################
 detections = []
 if args.proto and args.model:
-    if args.caffe:
-        net = cv.dnn.readNetFromCaffe(args.proto, args.model)
-    else:
-        net = cv.dnn.readNetFromTensorflow(args.model, args.proto)
+    net = cv.dnn.readNet(args.proto, args.model)
 
     def detect(img, imageId):
         imgWidth = img.shape[1]