vendor: OpenCV 5.0.0 snapshot at 40738fb16ceddb5fb3fea747585f7ce6abb0605b

samples/dnn/fast_neural_style.py

@@ -0,0 +1,52 @@
+from __future__ import print_function
+import cv2 as cv
+import numpy as np
+import argparse
+
+parser = argparse.ArgumentParser(
+        description='This script is used to run style transfer models from '
+                    '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 .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.readNet(cv.samples.findFile(args.model))
+net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
+
+if args.input:
+    cap = cv.VideoCapture(args.input)
+else:
+    cap = cv.VideoCapture(0)
+
+cv.namedWindow('Styled image', cv.WINDOW_NORMAL)
+while cv.waitKey(1) < 0:
+    hasFrame, frame = cap.read()
+    if not hasFrame:
+        cv.waitKey()
+        break
+
+    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),
+                               swapRB=True, crop=False)
+
+    net.setInput(inp)
+    t0 = cv.getTickCount()
+    out = net.forward()
+    t = (cv.getTickCount() - t0) / cv.getTickFrequency()
+
+    out = out.reshape(3, out.shape[2], out.shape[3])
+    out = out.transpose(1, 2, 0)
+
+    print('%.2f ms' % (t * 1000.0))
+
+    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)