Merge pull request #24245 from alexlyulkov/al/update-fast-neural-style-dnn-sample

Replaced torch7 model by ONNX model in fast-neural-style dnn sample

samples/dnn/fast_neural_style.py

@@ -5,15 +5,15 @@ import argparse
 
 parser = argparse.ArgumentParser(
         description='This script is used to run style transfer models from '
-                    'https://github.com/jcjohnson/fast-neural-style using OpenCV')
+                    'https://github.com/onnx/models/tree/main/vision/style_transfer/fast_neural_style using OpenCV')
 parser.add_argument('--input', help='Path to image or video. Skip to capture frames from camera')
-parser.add_argument('--model', help='Path to .t7 model')
+parser.add_argument('--model', help='Path to .onnx model')
 parser.add_argument('--width', default=-1, type=int, help='Resize input to specific width.')
 parser.add_argument('--height', default=-1, type=int, help='Resize input to specific height.')
 parser.add_argument('--median_filter', default=0, type=int, help='Kernel size of postprocessing blurring.')
 args = parser.parse_args()
 
-net = cv.dnn.readNetFromTorch(cv.samples.findFile(args.model))
+net = cv.dnn.readNet(cv.samples.findFile(args.model))
 net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
 
 if args.input:
@@ -31,16 +31,12 @@ while cv.waitKey(1) < 0:
     inWidth = args.width if args.width != -1 else frame.shape[1]
     inHeight = args.height if args.height != -1 else frame.shape[0]
     inp = cv.dnn.blobFromImage(frame, 1.0, (inWidth, inHeight),
-                              (103.939, 116.779, 123.68), swapRB=False, crop=False)
+                               swapRB=True, crop=False)
 
     net.setInput(inp)
     out = net.forward()
 
     out = out.reshape(3, out.shape[2], out.shape[3])
-    out[0] += 103.939
-    out[1] += 116.779
-    out[2] += 123.68
-    out /= 255
     out = out.transpose(1, 2, 0)
 
     t, _ = net.getPerfProfile()
@@ -50,4 +46,7 @@ while cv.waitKey(1) < 0:
     if args.median_filter:
         out = cv.medianBlur(out, args.median_filter)
 
+    out = np.clip(out, 0, 255)
+    out = out.astype(np.uint8)
+
     cv.imshow('Styled image', out)