HongChuang/ultralytics-ascend
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 3

Fix MNN example missing BGR to RGB conversion (#18689)

Browse source 
Signed-off-by: Jules <16029431+jules-ai@users.noreply.github.com>
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Jules 2025-01-15 16:56:28 +08:00 • committed by GitHub
parent 24f4721d9d
commit 031954d243
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
1 changed files with 2 additions and 0 deletions
Download patch file Download diff file Expand all files Collapse all files

2
docs/en/integrations/mnn.md
View file

@ -100,6 +100,7 @@ A function that relies solely on MNN for YOLO11 inference and preprocessing is i
image = cv2.resize( image = cv2.resize(
image, (640, 640), 0.0, 0.0, cv2.INTER_LINEAR, -1, [0.0, 0.0, 0.0], [1.0 / 255.0, 1.0 / 255.0, 1.0 / 255.0] image, (640, 640), 0.0, 0.0, cv2.INTER_LINEAR, -1, [0.0, 0.0, 0.0], [1.0 / 255.0, 1.0 / 255.0, 1.0 / 255.0]
) )
image = image[..., ::-1] # BGR to RGB
input_var = np.expand_dims(image, 0) input_var = np.expand_dims(image, 0)
input_var = MNN.expr.convert(input_var, MNN.expr.NC4HW4) input_var = MNN.expr.convert(input_var, MNN.expr.NC4HW4)
output_var = net.forward(input_var) output_var = net.forward(input_var)
@ -210,6 +211,7 @@ A function that relies solely on MNN for YOLO11 inference and preprocessing is i
auto pads = _Const(static_cast<void*>(padvals.data()), {3, 2}, NCHW, halide_type_of<int>()); auto pads = _Const(static_cast<void*>(padvals.data()), {3, 2}, NCHW, halide_type_of<int>());
auto image = _Pad(original_image, pads, CONSTANT); auto image = _Pad(original_image, pads, CONSTANT);
image = resize(image, Size(640, 640), 0, 0, INTER_LINEAR, -1, {0., 0., 0.}, {1./255., 1./255., 1./255.}); image = resize(image, Size(640, 640), 0, 0, INTER_LINEAR, -1, {0., 0., 0.}, {1./255., 1./255., 1./255.});
image = cvtColor(image, COLOR_BGR2RGB);
auto input = _Unsqueeze(image, {0}); auto input = _Unsqueeze(image, {0});
input = _Convert(input, NC4HW4); input = _Convert(input, NC4HW4);
auto outputs = net->onForward({input}); auto outputs = net->onForward({input});