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Merge pull request #29220 from omrope79:doc_optimizations_v4

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[FOLLOW UP] : Documentation optimizations for the new Sphinx structure #29220

### Pull Request Readiness Checklist

This PR serves as a follow-up to the new documentation system introduced in [#29206](https://github.com/opencv/opencv/pull/29206)
Co-authored by: @abhishek-gola @kirtijindal14 @Akansha-977 @Prasadayus @varun-jaiswal17

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
This commit is contained in:
omrope79
2026-06-05 16:48:27 +05:30
committed by GitHub
parent c9e7878a1d
commit 04aee009aa
43 changed files with 3468 additions and 650 deletions
Download Patch File Download Diff File Expand all files Collapse all files
doc/js_tutorials/js_assets/js_image_classification.html
+2 -19
View File
@@ -11,7 +11,7 @@
<h2>Image Classification Example</h2>
<p>
This tutorial shows you how to write an image classification example with OpenCV.js.<br>
To try the example you should click the <b>modelFile</b> button(and <b>configFile</b> button if needed) to upload inference model.
To try the example you should click the <b>modelFile</b> button to upload an ONNX model file.
You can find the model URLs and parameters in the <a href="#appendix">model info</a> section.
Then You should change the parameters in the first code snippet according to the uploaded model.
Finally click <b>Try it</b> button to see the result. You can choose any other images.<br>
@@ -69,13 +69,6 @@
</div>
</td>
</tr>
<tr>
<td>
<div class="caption">
configFile <input type="file" id="configFile">
</div>
</td>
</tr>
</table>
</div>
@@ -123,7 +116,7 @@ labelsUrl = "https://raw.githubusercontent.com/opencv/opencv/5.x/samples/data/dn
main = async function() {
const labels = await loadLables(labelsUrl);
const input = getBlobFromImage(inputSize, mean, std, swapRB, 'canvasInput');
let net = cv.readNet(configPath, modelPath);
let net = cv.readNet(modelPath);
net.setInput(input);
const start = performance.now();
const result = net.forward();
@@ -201,22 +194,12 @@ softmax = function(result) {
loadImageToCanvas(e, 'canvasInput');
});
let configPath = "";
let configFile = document.getElementById('configFile');
configFile.addEventListener('change', async (e) => {
initStatus();
configPath = await loadModel(e);
document.getElementById('status').innerHTML = `The config file '${configPath}' is created successfully.`;
});
let modelPath = "";
let modelFile = document.getElementById('modelFile');
modelFile.addEventListener('change', async (e) => {
initStatus();
modelPath = await loadModel(e);
document.getElementById('status').innerHTML = `The model file '${modelPath}' is created successfully.`;
configPath = "";
configFile.value = "";
});
utils.loadOpenCv(() => {
doc/js_tutorials/js_assets/js_image_classification_model_info.json
+47 -40
View File
@@ -1,65 +1,72 @@
{
"caffe": [
"onnx": [
{
"model": "alexnet",
"mean": "104, 117, 123",
"std": "1",
"swapRB": "false",
"needSoftmax": "false",
"labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/5.x/samples/data/dnn/classification_classes_ILSVRC2012.txt",
"modelUrl": "http://dl.caffe.berkeleyvision.org/bvlc_alexnet.caffemodel",
"configUrl": "https://raw.githubusercontent.com/BVLC/caffe/master/models/bvlc_alexnet/deploy.prototxt"
},
{
"model": "densenet",
"mean": "127.5, 127.5, 127.5",
"std": "0.007843",
"model": "googlenet",
"inputSize": "224, 224",
"mean": "103.939, 116.779, 123.675",
"std": "1, 1, 1",
"scale": "1",
"swapRB": "false",
"needSoftmax": "true",
"labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/5.x/samples/data/dnn/classification_classes_ILSVRC2012.txt",
"modelUrl": "https://drive.google.com/open?id=0B7ubpZO7HnlCcHlfNmJkU2VPelE",
"configUrl": "https://raw.githubusercontent.com/shicai/DenseNet-Caffe/master/DenseNet_121.prototxt"
},
{
"model": "googlenet",
"mean": "104, 117, 123",
"std": "1",
"swapRB": "false",
"needSoftmax": "false",
"labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/5.x/samples/data/dnn/classification_classes_ILSVRC2012.txt",
"modelUrl": "http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel",
"configUrl": "https://raw.githubusercontent.com/BVLC/caffe/master/models/bvlc_googlenet/deploy.prototxt"
"modelUrl": "https://github.com/onnx/models/raw/69c5d3751dda5349fd3fc53f525395d180420c07/vision/classification/inception_and_googlenet/googlenet/model/googlenet-8.onnx"
},
{
"model": "squeezenet",
"mean": "104, 117, 123",
"std": "1",
"swapRB": "false",
"needSoftmax": "false",
"inputSize": "224, 224",
"mean": "0.485, 0.456, 0.406",
"std": "0.229, 0.224, 0.225",
"scale": "0.003921",
"swapRB": "true",
"needSoftmax": "true",
"labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/5.x/samples/data/dnn/classification_classes_ILSVRC2012.txt",
"modelUrl": "https://raw.githubusercontent.com/forresti/SqueezeNet/master/SqueezeNet_v1.0/squeezenet_v1.0.caffemodel",
"configUrl": "https://raw.githubusercontent.com/forresti/SqueezeNet/master/SqueezeNet_v1.0/deploy.prototxt"
"modelUrl": "https://github.com/onnx/models/raw/main/validated/vision/classification/squeezenet/model/squeezenet1.1-7.onnx?download="
},
{
"model": "VGG",
"mean": "104, 117, 123",
"std": "1",
"swapRB": "false",
"needSoftmax": "false",
"model": "resnet",
"inputSize": "224, 224",
"mean": "123.675, 116.28, 103.53",
"std": "58.395, 57.12, 57.375",
"scale": "1",
"swapRB": "true",
"needSoftmax": "true",
"labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/5.x/samples/data/dnn/classification_classes_ILSVRC2012.txt",
"modelUrl": "http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_19_layers.caffemodel",
"configUrl": "https://gist.githubusercontent.com/ksimonyan/3785162f95cd2d5fee77/raw/f02f8769e64494bcd3d7e97d5d747ac275825721/VGG_ILSVRC_19_layers_deploy.prototxt"
"modelUrl": "https://github.com/onnx/models/raw/main/validated/vision/classification/resnet/model/resnet50-v2-7.onnx"
},
{
"model": "vgg16",
"inputSize": "224, 224",
"mean": "103.939, 116.779, 123.68",
"std": "1, 1, 1",
"scale": "1",
"swapRB": "false",
"needSoftmax": "true",
"labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/5.x/samples/data/dnn/classification_classes_ILSVRC2012.txt",
"modelUrl": "https://github.com/onnx/models/raw/f884b33c3e2371952aad7ea091898f418c830fe5/vision/classification/vgg/model/vgg16-bn-7.onnx"
},
{
"model": "densenet121",
"inputSize": "224, 224",
"mean": "123.675, 116.28, 103.53",
"std": "0.229, 0.224, 0.225",
"scale": "0.003921",
"swapRB": "true",
"needSoftmax": "true",
"labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/5.x/samples/data/dnn/classification_classes_ILSVRC2012.txt",
"modelUrl": "https://github.com/onnx/models/raw/4eff8f9b9189672de28d087684e7085ad977747c/vision/classification/densenet-121/model/densenet-8.onnx"
}
],
"tensorflow": [
{
"model": "inception",
"inputSize": "224, 224",
"mean": "123, 117, 104",
"std": "1",
"scale": "1",
"swapRB": "true",
"needSoftmax": "false",
"labelsUrl": "https://raw.githubusercontent.com/petewarden/tf_ios_makefile_example/master/data/imagenet_comp_graph_label_strings.txt",
"modelUrl": "https://raw.githubusercontent.com/petewarden/tf_ios_makefile_example/master/data/tensorflow_inception_graph.pb"
}
]
}
}
doc/js_tutorials/js_assets/js_image_classification_webnn_polyfill.html
+2 -19
View File
@@ -12,7 +12,7 @@
<h2>Image Classification Example</h2>
<p>
This tutorial shows you how to write an image classification example with OpenCV.js.<br>
To try the example you should click the <b>modelFile</b> button(and <b>configFile</b> button if needed) to upload inference model.
To try the example you should click the <b>modelFile</b> button to upload an ONNX model file.
You can find the model URLs and parameters in the <a href="#appendix">model info</a> section.
Then You should change the parameters in the first code snippet according to the uploaded model.
Finally click <b>Try it</b> button to see the result. You can choose any other images.<br>
@@ -70,13 +70,6 @@
</div>
</td>
</tr>
<tr>
<td>
<div class="caption">
configFile <input type="file" id="configFile">
</div>
</td>
</tr>
</table>
</div>
@@ -124,7 +117,7 @@ labelsUrl = "https://raw.githubusercontent.com/opencv/opencv/5.x/samples/data/dn
main = async function() {
const labels = await loadLables(labelsUrl);
const input = getBlobFromImage(inputSize, mean, std, swapRB, 'canvasInput');
let net = cv.readNet(configPath, modelPath);
let net = cv.readNet(modelPath);
net.setPreferableBackend(6);
net.setInput(input);
let result = net.forward();
@@ -207,22 +200,12 @@ softmax = function(result) {
loadImageToCanvas(e, 'canvasInput');
});
let configPath = "";
let configFile = document.getElementById('configFile');
configFile.addEventListener('change', async (e) => {
initStatus();
configPath = await loadModel(e);
document.getElementById('status').innerHTML = `The config file '${configPath}' is created successfully.`;
});
let modelPath = "";
let modelFile = document.getElementById('modelFile');
modelFile.addEventListener('change', async (e) => {
initStatus();
modelPath = await loadModel(e);
document.getElementById('status').innerHTML = `The model file '${modelPath}' is created successfully.`;
configPath = "";
configFile.value = "";
});
utils.loadOpenCv(() => {
doc/js_tutorials/js_assets/js_image_classification_with_camera.html
+2 -19
View File
@@ -11,7 +11,7 @@
<h2>Image Classification Example with Camera</h2>
<p>
This tutorial shows you how to write an image classification example with camera.<br>
To try the example you should click the <b>modelFile</b> button(and <b>configFile</b> button if needed) to upload inference model.
To try the example you should click the <b>modelFile</b> button to upload an ONNX model file.
You can find the model URLs and parameters in the <a href="#appendix">model info</a> section.
Then You should change the parameters in the first code snippet according to the uploaded model.
Finally click <b>Start/Stop</b> button to start or stop the camera capture.<br>
@@ -69,13 +69,6 @@
</div>
</td>
</tr>
<tr>
<td>
<div class="caption">
configFile <input type="file" id="configFile">
</div>
</td>
</tr>
</table>
</div>
@@ -126,7 +119,7 @@ let cap = new cv.VideoCapture(video);
main = async function(frame) {
const labels = await loadLables(labelsUrl);
const input = getBlobFromImage(inputSize, mean, std, swapRB, frame);
let net = cv.readNet(configPath, modelPath);
let net = cv.readNet(modelPath);
net.setInput(input);
const start = performance.now();
const result = net.forward();
@@ -203,22 +196,12 @@ softmax = function(result) {
}
});
let configPath = "";
let configFile = document.getElementById('configFile');
configFile.addEventListener('change', async (e) => {
initStatus();
configPath = await loadModel(e);
document.getElementById('status').innerHTML = `The config file '${configPath}' is created successfully.`;
});
let modelPath = "";
let modelFile = document.getElementById('modelFile');
modelFile.addEventListener('change', async (e) => {
initStatus();
modelPath = await loadModel(e);
document.getElementById('status').innerHTML = `The model file '${modelPath}' is created successfully.`;
configPath = "";
configFile.value = "";
});
utils.loadOpenCv(() => {
doc/js_tutorials/js_assets/js_object_detection.html
+6 -23
View File
@@ -11,7 +11,7 @@
<h2>Object Detection Example</h2>
<p>
This tutorial shows you how to write an object detection example with OpenCV.js.<br>
To try the example you should click the <b>modelFile</b> button(and <b>configFile</b> button if needed) to upload inference model.
To try the example you should click the <b>modelFile</b> button to upload an ONNX model file.
You can find the model URLs and parameters in the <a href="#appendix">model info</a> section.
Then You should change the parameters in the first code snippet according to the uploaded model.
Finally click <b>Try it</b> button to see the result. You can choose any other images.<br>
@@ -45,13 +45,6 @@
</div>
</td>
</tr>
<tr>
<td>
<div class="caption">
configFile <input type="file" id="configFile">
</div>
</td>
</tr>
</table>
</div>
@@ -84,8 +77,8 @@
<script id="codeSnippet" type="text/code-snippet">
inputSize = [300, 300];
mean = [127.5, 127.5, 127.5];
std = 0.007843;
mean = [0, 0, 0];
std = 1;
swapRB = false;
confThreshold = 0.5;
nmsThreshold = 0.4;
@@ -94,14 +87,14 @@ nmsThreshold = 0.4;
outType = "SSD";
// url for label file, can from local or Internet
labelsUrl = "https://raw.githubusercontent.com/opencv/opencv/5.x/samples/data/dnn/object_detection_classes_pascal_voc.txt";
labelsUrl = "https://raw.githubusercontent.com/opencv/opencv/5.x/samples/data/dnn/object_detection_classes_coco.txt";
</script>
<script id="codeSnippet1" type="text/code-snippet">
main = async function() {
const labels = await loadLables(labelsUrl);
const input = getBlobFromImage(inputSize, mean, std, swapRB, 'canvasInput');
let net = cv.readNet(configPath, modelPath);
let net = cv.readNet(modelPath);
net.setInput(input);
const start = performance.now();
const result = net.forward();
@@ -305,7 +298,7 @@ postProcess = function(result, labels) {
let ctx = canvas.getContext('2d');
let img = new Image();
img.crossOrigin = 'anonymous';
img.src = 'lena.png';
img.src = 'lena.jpg';
img.onload = function() {
ctx.drawImage(img, 0, 0, canvas.width, canvas.height);
};
@@ -330,22 +323,12 @@ postProcess = function(result, labels) {
loadImageToCanvas(e, 'canvasInput');
});
let configPath = "";
let configFile = document.getElementById('configFile');
configFile.addEventListener('change', async (e) => {
initStatus();
configPath = await loadModel(e);
document.getElementById('status').innerHTML = `The config file '${configPath}' is created successfully.`;
});
let modelPath = "";
let modelFile = document.getElementById('modelFile');
modelFile.addEventListener('change', async (e) => {
initStatus();
modelPath = await loadModel(e);
document.getElementById('status').innerHTML = `The model file '${modelPath}' is created successfully.`;
configPath = "";
configFile.value = "";
});
utils.loadOpenCv(() => {
doc/js_tutorials/js_assets/js_object_detection_model_info.json
+23 -18
View File
@@ -1,39 +1,44 @@
{
"caffe": [
"onnx": [
{
"model": "mobilenet_SSD",
"model": "ssd_mobilenet_v1",
"inputSize": "300, 300",
"mean": "127.5, 127.5, 127.5",
"std": "0.007843",
"mean": "0, 0, 0",
"std": "1",
"swapRB": "false",
"outType": "SSD",
"labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/5.x/samples/data/dnn/object_detection_classes_pascal_voc.txt",
"modelUrl": "https://raw.githubusercontent.com/chuanqi305/MobileNet-SSD/master/mobilenet_iter_73000.caffemodel",
"configUrl": "https://raw.githubusercontent.com/chuanqi305/MobileNet-SSD/master/deploy.prototxt"
"labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/5.x/samples/data/dnn/object_detection_classes_coco.txt",
"modelUrl": "https://huggingface.co/onnxmodelzoo/ssd_mobilenet_v1_12/resolve/main/ssd_mobilenet_v1_12.onnx"
},
{
"model": "VGG_SSD",
"model": "ssd_vgg16",
"inputSize": "300, 300",
"mean": "104, 117, 123",
"std": "1",
"swapRB": "false",
"outType": "SSD",
"labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/5.x/samples/data/dnn/object_detection_classes_pascal_voc.txt",
"modelUrl": "https://drive.google.com/uc?id=0BzKzrI_SkD1_WVVTSmQxU0dVRzA&export=download",
"configUrl": "https://drive.google.com/uc?id=0BzKzrI_SkD1_WVVTSmQxU0dVRzA&export=download"
}
],
"darknet": [
"modelUrl": "https://github.com/omrope79/opencv-test-models/releases/download/v1.2.0/ssd_vgg16.onnx"
},
{
"model": "yolov2_tiny",
"model": "yolov4-tiny",
"inputSize": "416, 416",
"mean": "0, 0, 0",
"std": "0.00392",
"swapRB": "false",
"swapRB": "true",
"outType": "YOLO",
"labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/5.x/samples/data/dnn/object_detection_classes_yolov3.txt",
"modelUrl": "https://pjreddie.com/media/files/yolov2-tiny.weights",
"configUrl": "https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov2-tiny.cfg"
"modelUrl": "https://huggingface.co/opencv/opencv_contribution/resolve/main/yolov4/yolov4-tiny.onnx"
},
{
"model": "yolov3",
"inputSize": "416, 416",
"mean": "0, 0, 0",
"std": "0.00392",
"swapRB": "true",
"outType": "YOLO",
"labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/5.x/samples/data/dnn/object_detection_classes_yolov3.txt",
"modelUrl": "https://huggingface.co/qualcomm/Yolo-v3/resolve/226ada6de9dcb32eebad7f74bf526714e2af6136/Yolo-v3.onnx"
}
]
}
}
doc/js_tutorials/js_assets/js_object_detection_with_camera.html
+6 -23
View File
@@ -11,7 +11,7 @@
<h2>Object Detection Example with Camera </h2>
<p>
This tutorial shows you how to write an object detection example with camera.<br>
To try the example you should click the <b>modelFile</b> button(and <b>configInput</b> button if needed) to upload inference model.
To try the example you should click the <b>modelFile</b> button to upload an ONNX model file.
You can find the model URLs and parameters in the <a href="#appendix">model info</a> section.
Then You should change the parameters in the first code snippet according to the uploaded model.
Finally click <b>Start/Stop</b> button to start or stop the camera capture.<br>
@@ -45,13 +45,6 @@
</div>
</td>
</tr>
<tr>
<td>
<div class="caption">
configFile <input type="file" id="configFile">
</div>
</td>
</tr>
</table>
</div>
@@ -84,17 +77,17 @@
<script id="codeSnippet" type="text/code-snippet">
inputSize = [300, 300];
mean = [127.5, 127.5, 127.5];
std = 0.007843;
mean = [0, 0, 0];
std = 1;
swapRB = false;
confThreshold = 0.5;
nmsThreshold = 0.4;
// the type of output, can be YOLO or SSD
// The type of output, can be YOLO or SSD
outType = "SSD";
// url for label file, can from local or Internet
labelsUrl = "https://raw.githubusercontent.com/opencv/opencv/5.x/samples/data/dnn/object_detection_classes_pascal_voc.txt";
labelsUrl = "https://raw.githubusercontent.com/opencv/opencv/5.x/samples/data/dnn/object_detection_classes_coco.txt";
</script>
<script id="codeSnippet1" type="text/code-snippet">
@@ -104,7 +97,7 @@ let cap = new cv.VideoCapture(videoInput);
main = async function(frame) {
const labels = await loadLables(labelsUrl);
const input = getBlobFromImage(inputSize, mean, std, swapRB, frame);
let net = cv.readNet(configPath, modelPath);
let net = cv.readNet(modelPath);
net.setInput(input);
const start = performance.now();
const result = net.forward();
@@ -331,22 +324,12 @@ postProcess = function(result, labels, frame) {
}
});
let configPath = "";
let configFile = document.getElementById('configFile');
configFile.addEventListener('change', async (e) => {
initStatus();
configPath = await loadModel(e);
document.getElementById('status').innerHTML = `The config file '${configPath}' is created successfully.`;
});
let modelPath = "";
let modelFile = document.getElementById('modelFile');
modelFile.addEventListener('change', async (e) => {
initStatus();
modelPath = await loadModel(e);
document.getElementById('status').innerHTML = `The model file '${modelPath}' is created successfully.`;
configPath = "";
configFile.value = "";
});
utils.loadOpenCv(() => {
doc/js_tutorials/js_assets/js_pose_estimation.html
+2 -19
View File
@@ -11,7 +11,7 @@
<h2>Pose Estimation Example</h2>
<p>
This tutorial shows you how to write an pose estimation example with OpenCV.js.<br>
To try the example you should click the <b>modelFile</b> button(and <b>configInput</b> button if needed) to upload inference model.
To try the example you should click the <b>modelFile</b> button to upload an ONNX model file.
You can find the model URLs and parameters in the <a href="#appendix">model info</a> section.
Then You should change the parameters in the first code snippet according to the uploaded model.
Finally click <b>Try it</b> button to see the result. You can choose any other images.<br>
@@ -45,13 +45,6 @@
</div>
</td>
</tr>
<tr>
<td>
<div class="caption">
configFile <input type="file" id="configFile">
</div>
</td>
</tr>
</table>
</div>
@@ -96,7 +89,7 @@ dataset = "COCO";
<script id="codeSnippet1" type="text/code-snippet">
main = async function() {
const input = getBlobFromImage(inputSize, mean, std, swapRB, 'canvasInput');
let net = cv.readNet(configPath, modelPath);
let net = cv.readNet(modelPath);
net.setInput(input);
const start = performance.now();
const result = net.forward();
@@ -268,22 +261,12 @@ postProcess = function(result) {
loadImageToCanvas(e, 'canvasInput');
});
let configPath = "";
let configFile = document.getElementById('configFile');
configFile.addEventListener('change', async (e) => {
initStatus();
configPath = await loadModel(e);
document.getElementById('status').innerHTML = `The config file '${configPath}' is created successfully.`;
});
let modelPath = "";
let modelFile = document.getElementById('modelFile');
modelFile.addEventListener('change', async (e) => {
initStatus();
modelPath = await loadModel(e);
document.getElementById('status').innerHTML = `The model file '${modelPath}' is created successfully.`;
configPath = "";
configFile.value = "";
});
utils.loadOpenCv(() => {
doc/js_tutorials/js_assets/js_pose_estimation_model_info.json
+6 -18
View File
@@ -1,34 +1,22 @@
{
"caffe": [
"onnx": [
{
"model": "body_25",
"inputSize": "368, 368",
"mean": "0, 0, 0",
"std": "0.00392",
"swapRB": "false",
"dataset": "BODY_25",
"modelUrl": "http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/body_25/pose_iter_584000.caffemodel",
"configUrl": "https://raw.githubusercontent.com/CMU-Perceptual-Computing-Lab/openpose/master/models/pose/body_25/pose_deploy.prototxt"
},
{
"model": "coco",
"model": "openpose_coco",
"inputSize": "368, 368",
"mean": "0, 0, 0",
"std": "0.00392",
"swapRB": "false",
"dataset": "COCO",
"modelUrl": "http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/coco/pose_iter_440000.caffemodel",
"configUrl": "https://raw.githubusercontent.com/CMU-Perceptual-Computing-Lab/openpose/master/models/pose/coco/pose_deploy_linevec.prototxt"
"modelUrl": "https://github.com/omrope79/opencv-test-models/releases/download/v1.3.0/openpose_pose_coco.onnx"
},
{
"model": "mpi",
"model": "openpose_mpi",
"inputSize": "368, 368",
"mean": "0, 0, 0",
"std": "0.00392",
"swapRB": "false",
"dataset": "MPI",
"modelUrl": "http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/mpi/pose_iter_160000.caffemodel",
"configUrl": "https://raw.githubusercontent.com/CMU-Perceptual-Computing-Lab/openpose/master/models/pose/mpi/pose_deploy_linevec.prototxt"
"modelUrl": "https://github.com/omrope79/opencv-test-models/releases/download/v1.3.0/openpose_pose_mpi.onnx"
}
]
}
}
doc/js_tutorials/js_assets/js_semantic_segmentation.html
+1 -1
View File
@@ -11,7 +11,7 @@
<h2>Semantic Segmentation Example</h2>
<p>
This tutorial shows you how to write an semantic segmentation example with OpenCV.js.<br>
To try the example you should click the <b>modelFile</b> button(and <b>configInput</b> button if needed) to upload inference model.
To try the example you should click the <b>modelFile</b> button(and <b>configFile</b> button if needed) to upload inference model.
You can find the model URLs and parameters in the <a href="#appendix">model info</a> section.
Then You should change the parameters in the first code snippet according to the uploaded model.
Finally click <b>Try it</b> button to see the result. You can choose any other images.<br>
doc/js_tutorials/js_assets/js_semantic_segmentation_model_info.json
+1 -1
View File
@@ -9,4 +9,4 @@
"modelUrl": "https://drive.google.com/uc?id=1v-hfGenaE9tiGOzo5qdgMNG_gqQ5-Xn4&export=download"
}
]
}
}
doc/js_tutorials/js_assets/js_style_transfer.html
+3 -20
View File
@@ -11,7 +11,7 @@
<h2>Style Transfer Example</h2>
<p>
This tutorial shows you how to write an style transfer example with OpenCV.js.<br>
To try the example you should click the <b>modelFile</b> button(and <b>configFile</b> button if needed) to upload inference model.
To try the example you should click the <b>modelFile</b> button to upload an ONNX model file.
You can find the model URLs and parameters in the <a href="#appendix">model info</a> section.
Then You should change the parameters in the first code snippet according to the uploaded model.
Finally click <b>Try it</b> button to see the result. You can choose any other images.<br>
@@ -45,13 +45,6 @@
</div>
</td>
</tr>
<tr>
<td>
<div class="caption">
configFile <input type="file" id="configFile">
</div>
</td>
</tr>
</table>
</div>
@@ -90,7 +83,7 @@ swapRB = false;
<script id="codeSnippet1" type="text/code-snippet">
main = async function() {
const input = getBlobFromImage(inputSize, mean, std, swapRB, 'canvasInput');
let net = cv.readNet(configPath, modelPath);
let net = cv.readNet(modelPath);
net.setInput(input);
const start = performance.now();
const result = net.forward();
@@ -146,7 +139,7 @@ postProcess = function(result) {
let ctx = canvas.getContext('2d');
let img = new Image();
img.crossOrigin = 'anonymous';
img.src = 'lena.png';
img.src = 'lena.jpg';
img.onload = function() {
ctx.drawImage(img, 0, 0, canvas.width, canvas.height);
};
@@ -171,22 +164,12 @@ postProcess = function(result) {
loadImageToCanvas(e, 'canvasInput');
});
let configPath = "";
let configFile = document.getElementById('configFile');
configFile.addEventListener('change', async (e) => {
initStatus();
configPath = await loadModel(e);
document.getElementById('status').innerHTML = `The config file '${configPath}' is created successfully.`;
});
let modelPath = "";
let modelFile = document.getElementById('modelFile');
modelFile.addEventListener('change', async (e) => {
initStatus();
modelPath = await loadModel(e);
document.getElementById('status').innerHTML = `The model file '${modelPath}' is created successfully.`;
configPath = "";
configFile.value = "";
});
utils.loadOpenCv(() => {
doc/js_tutorials/js_assets/js_style_transfer_model_info.json
+5 -5
View File
@@ -6,7 +6,7 @@
"mean": "0, 0, 0",
"std": "1",
"swapRB": "true",
"modelUrl": "https://media.githubusercontent.com/media/onnx/models/main/vision/style_transfer/fast_neural_style/model/mosaic-9.onnx"
"modelUrl": "https://media.githubusercontent.com/media/onnx/models/main/validated/vision/style_transfer/fast_neural_style/model/mosaic-9.onnx"
},
{
"model": "candy-9.onnx",
@@ -14,7 +14,7 @@
"mean": "0, 0, 0",
"std": "1",
"swapRB": "true",
"modelUrl": "https://media.githubusercontent.com/media/onnx/models/main/vision/style_transfer/fast_neural_style/model/candy-9.onnx"
"modelUrl": "https://media.githubusercontent.com/media/onnx/models/main/validated/vision/style_transfer/fast_neural_style/model/candy-9.onnx"
},
{
"model": "rain-princess-9.onnx",
@@ -22,7 +22,7 @@
"mean": "0, 0, 0",
"std": "1",
"swapRB": "true",
"modelUrl": "https://media.githubusercontent.com/media/onnx/models/main/vision/style_transfer/fast_neural_style/model/rain-princess-9.onnx"
"modelUrl": "https://media.githubusercontent.com/media/onnx/models/main/validated/vision/style_transfer/fast_neural_style/model/rain-princess-9.onnx"
},
{
"model": "udnie-9.onnx",
@@ -30,7 +30,7 @@
"mean": "0, 0, 0",
"std": "1",
"swapRB": "true",
"modelUrl": "https://media.githubusercontent.com/media/onnx/models/main/vision/style_transfer/fast_neural_style/model/udnie-9.onnx"
"modelUrl": "https://media.githubusercontent.com/media/onnx/models/main/validated/vision/style_transfer/fast_neural_style/model/udnie-9.onnx"
},
{
"model": "pointilism-9.onnx",
@@ -38,7 +38,7 @@
"mean": "0, 0, 0",
"std": "1",
"swapRB": "true",
"modelUrl": "https://media.githubusercontent.com/media/onnx/models/main/vision/style_transfer/fast_neural_style/model/pointilism-9.onnx"
"modelUrl": "https://media.githubusercontent.com/media/onnx/models/main/validated/vision/style_transfer/fast_neural_style/model/pointilism-9.onnx"
}
]
}