ssd_mobilenet_v1_coco_2017_11_17

ssd_mobilenet_v1_coco_2017_11_17/README.md

@@ -19,18 +19,22 @@ python demo.py --model ssd_mobilenet_v1_coco_2017_11_17_2026jul.onnx --image exa
 ```
 
 ### C++
-The C++ demo runs inference with OpenCV's DNN module. Adjust the OpenCV paths to your setup:
+The C++ demo runs inference with ONNX Runtime (C++ API) and uses OpenCV only for image I/O.
+Install ONNX Runtime (C++) from https://github.com/microsoft/onnxruntime/releases — this build
+uses `onnxruntime-linux-x64-1.25.0` — and adjust the ONNX Runtime and OpenCV paths to your setup:
 ```bash
+ORT=/path/to/onnxruntime-linux-x64-1.25.0    # ONNX Runtime release dir (contains include/ and lib/)
 OCV=/path/to/opencv                          # OpenCV source tree
 OCVBUILD=/path/to/opencv/build               # OpenCV build directory (generated headers + libs)
 g++ -std=c++17 demo.cpp -o demo \
+  -I$ORT/include \
   -I$OCV/include \
   -I$OCV/modules/core/include \
-  -I$OCV/modules/dnn/include \
   -I$OCV/modules/imgproc/include \
   -I$OCV/modules/imgcodecs/include \
   -I$OCVBUILD \
-  -L$OCVBUILD/lib -Wl,-rpath,$OCVBUILD/lib -lopencv_dnn -lopencv_imgcodecs -lopencv_imgproc -lopencv_core
+  -L$ORT/lib -Wl,-rpath,$ORT/lib -lonnxruntime \
+  -L$OCVBUILD/lib -Wl,-rpath,$OCVBUILD/lib -lopencv_imgcodecs -lopencv_imgproc -lopencv_core
 ./demo --model ssd_mobilenet_v1_coco_2017_11_17_2026jul.onnx --image example_outputs/input_image.png --output example_outputs/output_image.png
 ```
 

ssd_mobilenet_v1_coco_2017_11_17/demo.cpp

@@ -1,4 +1,4 @@
-#include <opencv2/dnn.hpp>
+#include <onnxruntime_cxx_api.h>
 #include <opencv2/imgproc.hpp>
 #include <opencv2/imgcodecs.hpp>
 #include <array>
@@ -35,22 +35,39 @@ int main(int argc, char** argv)
     resize(rgb, rgb, Size(300, 300));
     if (!rgb.isContinuous()) rgb = rgb.clone();
 
-    int blobShape[] = {1, 300, 300, 3};
-    Mat blob(4, blobShape, CV_8U, rgb.data);
-    dnn::Net net = dnn::readNetFromONNX(model, dnn::ENGINE_ORT);
-    net.setInput(blob);
-    std::vector<String> out_str = {"detection_boxes:0", "detection_scores:0", "detection_classes:0", "num_detections:0"};
-    std::vector<Mat> outs;
-    net.forward(outs, out_str);
+    Ort::Env env(ORT_LOGGING_LEVEL_WARNING, "demo");
+    Ort::SessionOptions so;
+    Ort::Session session(env, model.c_str(), so);
+    Ort::AllocatorWithDefaultOptions alloc;
+
+    auto in_name = session.GetInputNameAllocated(0, alloc);
+    const char* in_names[] = {in_name.get()};
+
+    size_t out_count = session.GetOutputCount();
+    std::vector<Ort::AllocatedStringPtr> out_holders;
+    std::vector<std::string> out_str;
+    std::vector<const char*> out_names;
+    for (size_t i = 0; i < out_count; ++i)
+    {
+        out_holders.push_back(session.GetOutputNameAllocated(i, alloc));
+        out_str.push_back(out_holders.back().get());
+        out_names.push_back(out_str.back().c_str());
+    }
+
+    std::array<int64_t, 4> shape = {1, 300, 300, 3};
+    auto mem = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
+    Ort::Value input = Ort::Value::CreateTensor<uint8_t>(mem, rgb.data, 300 * 300 * 3, shape.data(), shape.size());
+
+    auto outs = session.Run(Ort::RunOptions{nullptr}, in_names, &input, 1, out_names.data(), out_names.size());
 
     const float *boxes = 0, *scores = 0, *classes = 0, *num = 0;
-    for (size_t i = 0; i < out_str.size(); ++i)
+    for (size_t i = 0; i < out_count; ++i)
     {
         const std::string& n = out_str[i];
-        if (n.find("detection_boxes") != std::string::npos) boxes = (const float*)outs[i].data;
-        else if (n.find("detection_scores") != std::string::npos) scores = (const float*)outs[i].data;
-        else if (n.find("detection_classes") != std::string::npos) classes = (const float*)outs[i].data;
-        else if (n.find("num_detections") != std::string::npos) num = (const float*)outs[i].data;
+        if (n.find("detection_boxes") != std::string::npos) boxes = outs[i].GetTensorMutableData<float>();
+        else if (n.find("detection_scores") != std::string::npos) scores = outs[i].GetTensorMutableData<float>();
+        else if (n.find("detection_classes") != std::string::npos) classes = outs[i].GetTensorMutableData<float>();
+        else if (n.find("num_detections") != std::string::npos) num = outs[i].GetTensorMutableData<float>();
     }
     if (!boxes || !scores || !classes || !num)
     {

ssd_mobilenet_v1_coco_2017_11_17/demo.py

@@ -4,6 +4,7 @@ import os
 
 import cv2 as cv
 import numpy as np
+import onnxruntime as ort
 
 here = os.path.dirname(os.path.abspath(__file__))
 
@@ -22,10 +23,9 @@ def main():
 
     rgb = cv.resize(cv.cvtColor(img, cv.COLOR_BGR2RGB), (300, 300))
 
-    net = cv.dnn.readNetFromONNX(args.model, cv.dnn.ENGINE_ORT)
-    onames = ["detection_boxes:0", "detection_scores:0", "detection_classes:0", "num_detections:0"]
-    net.setInput(rgb[None].astype(np.uint8))
-    res = net.forward(onames)
+    sess = ort.InferenceSession(args.model, providers=["CPUExecutionProvider"])
+    res = sess.run(None, {sess.get_inputs()[0].name: rgb[None].astype(np.uint8)})
+    onames = [o.name for o in sess.get_outputs()]
     boxes = res[[i for i, n in enumerate(onames) if "detection_boxes" in n][0]].reshape(-1, 4)
     scores = res[[i for i, n in enumerate(onames) if "detection_scores" in n][0]].reshape(-1)
     classes = res[[i for i, n in enumerate(onames) if "detection_classes" in n][0]].reshape(-1)

ssd_mobilenet_v2_coco_2018_03_29/LICENSE

@@ -1,212 +0,0 @@
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ssd_mobilenet_v2_coco_2018_03_29/README.md

@@ -1,52 +0,0 @@
-# SSD MobileNet v2 COCO
-
-Object detection with a Single Shot MultiBox Detector (SSD) built on a MobileNet v2 backbone,
-trained on the COCO dataset. The model was originally distributed as a frozen TensorFlow graph
-(`ssd_mobilenet_v2_coco_2018_03_29.pb`) and converted to ONNX for use with OpenCV's DNN module.
-
-## Model Details
-- **Architecture**: SSD (Single Shot MultiBox Detector) with MobileNet v2 backbone
-- **Input**: RGB image, 300×300, raw uint8, NHWC layout (`image_tensor:0`, shape `[1, 300, 300, 3]`)
-- **Output**: `detection_boxes:0` (normalized `ymin, xmin, ymax, xmax`), `detection_scores:0`, `detection_classes:0` (COCO class ids), `num_detections:0`
-- **Framework**: ONNX (converted from the TensorFlow frozen graph via tf2onnx, opset 18)
-- **Original weights**: http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v2_coco_2018_03_29.tar.gz
-
-## Usage
-
-### Python
-```bash
-python demo.py --model ssd_mobilenet_v2_coco_2018_03_29_2026jul.onnx --image example_outputs/input_image.png --output example_outputs/output_image.png --conf 0.3
-```
-
-### C++
-The C++ demo runs inference with ONNX Runtime (C++ API) and uses OpenCV only for image I/O.
-Install ONNX Runtime (C++) from https://github.com/microsoft/onnxruntime/releases — this build
-uses `onnxruntime-linux-x64-1.25.0` — and adjust the ONNX Runtime and OpenCV paths to your setup:
-```bash
-ORT=/path/to/onnxruntime-linux-x64-1.25.0    # ONNX Runtime release dir (contains include/ and lib/)
-OCV=/path/to/opencv                          # OpenCV source tree
-OCVBUILD=/path/to/opencv/build               # OpenCV build directory (generated headers + libs)
-g++ -std=c++17 demo.cpp -o demo \
-  -I$ORT/include \
-  -I$OCV/include \
-  -I$OCV/modules/core/include \
-  -I$OCV/modules/imgproc/include \
-  -I$OCV/modules/imgcodecs/include \
-  -I$OCVBUILD \
-  -L$ORT/lib -Wl,-rpath,$ORT/lib -lonnxruntime \
-  -L$OCVBUILD/lib -Wl,-rpath,$OCVBUILD/lib -lopencv_imgcodecs -lopencv_imgproc -lopencv_core
-./demo --model ssd_mobilenet_v2_coco_2018_03_29_2026jul.onnx --image example_outputs/input_image.png --output example_outputs/output_image.png
-```
-
-## Conversion
-The ONNX model was exported from the frozen TensorFlow graph with tf2onnx (opset 18)
-via [convert_to_onnx.py](./convert_to_onnx.py) — input `image_tensor:0`, outputs
-`detection_boxes:0`, `detection_scores:0`, `detection_classes:0`, `num_detections:0`.
-Requires `tensorflow`, `tf2onnx`, and `onnx`.
-
-```bash
-python convert_to_onnx.py --pb ../pb/ssd_mobilenet_v2_coco_2018_03_29.pb
-```
-
-## License
-See [LICENSE](./LICENSE) — the model is released by the TensorFlow Authors under the Apache License 2.0.

ssd_mobilenet_v2_coco_2018_03_29/convert_to_onnx.py

@@ -1,40 +0,0 @@
-import argparse
-import datetime
-
-import onnx
-import tensorflow as tf
-import tf2onnx
-
-
-def load_graph_def(pb_path):
-    with tf.io.gfile.GFile(pb_path, "rb") as f:
-        graph_def = tf.compat.v1.GraphDef()
-        graph_def.ParseFromString(f.read())
-    return graph_def
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Export ssd_mobilenet_v2_coco_2018_03_29.pb to ONNX")
-    parser.add_argument("--pb", default="../pb/ssd_mobilenet_v2_coco_2018_03_29.pb")
-    parser.add_argument("--opset", type=int, default=18)
-    args = parser.parse_args()
-
-    graph_def = load_graph_def(args.pb)
-
-    model_proto, _ = tf2onnx.convert.from_graph_def(
-        graph_def,
-        input_names=["image_tensor:0"],
-        output_names=["detection_boxes:0", "detection_scores:0", "detection_classes:0", "num_detections:0"],
-        opset=args.opset,
-    )
-    onnx.checker.check_model(model_proto)
-
-    stamp = datetime.datetime.now().strftime("%Y%b").lower()
-    onnx_path = "ssd_mobilenet_v2_coco_2018_03_29_%s.onnx" % stamp
-    with open(onnx_path, "wb") as f:
-        f.write(model_proto.SerializeToString())
-    print("wrote", onnx_path)
-
-
-if __name__ == "__main__":
-    main()

ssd_mobilenet_v2_coco_2018_03_29/demo.cpp

@@ -1,98 +0,0 @@
-#include <onnxruntime_cxx_api.h>
-#include <opencv2/imgproc.hpp>
-#include <opencv2/imgcodecs.hpp>
-#include <array>
-#include <cstdint>
-#include <iostream>
-#include <string>
-#include <vector>
-
-using namespace cv;
-
-static std::string argVal(int argc, char** argv, const std::string& key, const std::string& def)
-{
-    for (int i = 1; i + 1 < argc; ++i)
-        if (key == argv[i]) return argv[i + 1];
-    return def;
-}
-
-int main(int argc, char** argv)
-{
-    std::string model  = argVal(argc, argv, "--model",  "ssd_mobilenet_v2_coco_2018_03_29_2026jul.onnx");
-    std::string image  = argVal(argc, argv, "--image",  "example_outputs/input_image.png");
-    std::string output = argVal(argc, argv, "--output", "example_outputs/output_image.png");
-    float conf = std::stof(argVal(argc, argv, "--conf", "0.3"));
-
-    Mat img = imread(image);
-    if (img.empty())
-    {
-        std::cerr << "could not read image: " << image << std::endl;
-        return 1;
-    }
-
-    Mat rgb;
-    cvtColor(img, rgb, COLOR_BGR2RGB);
-    resize(rgb, rgb, Size(300, 300));
-    if (!rgb.isContinuous()) rgb = rgb.clone();
-
-    Ort::Env env(ORT_LOGGING_LEVEL_WARNING, "demo");
-    Ort::SessionOptions so;
-    Ort::Session session(env, model.c_str(), so);
-    Ort::AllocatorWithDefaultOptions alloc;
-
-    auto in_name = session.GetInputNameAllocated(0, alloc);
-    const char* in_names[] = {in_name.get()};
-
-    size_t out_count = session.GetOutputCount();
-    std::vector<Ort::AllocatedStringPtr> out_holders;
-    std::vector<std::string> out_str;
-    std::vector<const char*> out_names;
-    for (size_t i = 0; i < out_count; ++i)
-    {
-        out_holders.push_back(session.GetOutputNameAllocated(i, alloc));
-        out_str.push_back(out_holders.back().get());
-        out_names.push_back(out_str.back().c_str());
-    }
-
-    std::array<int64_t, 4> shape = {1, 300, 300, 3};
-    auto mem = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
-    Ort::Value input = Ort::Value::CreateTensor<uint8_t>(mem, rgb.data, 300 * 300 * 3, shape.data(), shape.size());
-
-    auto outs = session.Run(Ort::RunOptions{nullptr}, in_names, &input, 1, out_names.data(), out_names.size());
-
-    const float *boxes = 0, *scores = 0, *classes = 0, *num = 0;
-    for (size_t i = 0; i < out_count; ++i)
-    {
-        const std::string& n = out_str[i];
-        if (n.find("detection_boxes") != std::string::npos) boxes = outs[i].GetTensorMutableData<float>();
-        else if (n.find("detection_scores") != std::string::npos) scores = outs[i].GetTensorMutableData<float>();
-        else if (n.find("detection_classes") != std::string::npos) classes = outs[i].GetTensorMutableData<float>();
-        else if (n.find("num_detections") != std::string::npos) num = outs[i].GetTensorMutableData<float>();
-    }
-    if (!boxes || !scores || !classes || !num)
-    {
-        std::cerr << "missing expected output tensors" << std::endl;
-        return 1;
-    }
-
-    int nd = (int)num[0];
-    int h = img.rows, w = img.cols;
-    Mat out = img.clone();
-    std::vector<std::string> lines;
-    for (int k = 0; k < nd; ++k)
-    {
-        if (scores[k] < conf) continue;
-        float ymin = boxes[k * 4 + 0], xmin = boxes[k * 4 + 1];
-        float ymax = boxes[k * 4 + 2], xmax = boxes[k * 4 + 3];
-        int cls = (int)classes[k];
-        rectangle(out, Point(int(xmin * w), int(ymin * h)), Point(int(xmax * w), int(ymax * h)), Scalar(0, 255, 0), 2);
-        putText(out, format("%d:%.2f", cls, scores[k]), Point(int(xmin * w), int(ymin * h) - 5),
-                FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0), 1);
-        lines.push_back(format("%d %.3f %.3f %.3f %.3f %.3f", cls, scores[k], xmin, ymin, xmax, ymax));
-    }
-
-    imwrite(output, out);
-    std::cout << "ssd_mobilenet_v2_coco_2018_03_29 " << lines.size() << " detections" << std::endl;
-    for (const auto& l : lines) std::cout << l << std::endl;
-    return 0;
-}

ssd_mobilenet_v2_coco_2018_03_29/demo.py

@@ -1,52 +0,0 @@
-import argparse
-import glob
-import os
-
-import cv2 as cv
-import numpy as np
-import onnxruntime as ort
-
-here = os.path.dirname(os.path.abspath(__file__))
-
-
-def main():
-    parser = argparse.ArgumentParser(description="SSD MobileNet v2 COCO (ONNX) object detection demo")
-    parser.add_argument("--model", default=(glob.glob(os.path.join(here, "*.onnx")) or [""])[0])
-    parser.add_argument("--image", default=os.path.join(here, "example_outputs", "input_image.png"))
-    parser.add_argument("--output", default=os.path.join(here, "example_outputs", "output_image.png"))
-    parser.add_argument("--conf", type=float, default=0.3)
-    args = parser.parse_args()
-
-    img = cv.imread(args.image)
-    if img is None:
-        raise SystemExit("could not read image: %s" % args.image)
-
-    rgb = cv.resize(cv.cvtColor(img, cv.COLOR_BGR2RGB), (300, 300))
-
-    sess = ort.InferenceSession(args.model, providers=["CPUExecutionProvider"])
-    res = sess.run(None, {sess.get_inputs()[0].name: rgb[None].astype(np.uint8)})
-    onames = [o.name for o in sess.get_outputs()]
-    boxes = res[[i for i, n in enumerate(onames) if "detection_boxes" in n][0]].reshape(-1, 4)
-    scores = res[[i for i, n in enumerate(onames) if "detection_scores" in n][0]].reshape(-1)
-    classes = res[[i for i, n in enumerate(onames) if "detection_classes" in n][0]].reshape(-1)
-    nd = int(res[[i for i, n in enumerate(onames) if "num_detections" in n][0]].reshape(-1)[0])
-
-    h, w = img.shape[:2]
-    out = img.copy()
-    kept = []
-    for k in range(nd):
-        if scores[k] < args.conf:
-            continue
-        ymin, xmin, ymax, xmax = boxes[k]
-        kept.append((int(classes[k]), float(scores[k]), float(xmin), float(ymin), float(xmax), float(ymax)))
-        cv.rectangle(out, (int(xmin * w), int(ymin * h)), (int(xmax * w), int(ymax * h)), (0, 255, 0), 2)
-        cv.putText(out, "%d:%.2f" % (int(classes[k]), scores[k]), (int(xmin * w), int(ymin * h) - 5), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
-
-    cv.imwrite(args.output, out)
-    print("ssd_mobilenet_v2_coco_2018_03_29", len(kept), "detections")
-    for c, s, xmin, ymin, xmax, ymax in kept:
-        print(c, round(s, 3), round(xmin, 3), round(ymin, 3), round(xmax, 3), round(ymax, 3))
-
-
-if __name__ == "__main__":
-    main()

ssd_mobilenet_v2_coco_2018_03_29/ssd_mobilenet_v2_coco_2018_03_29_2026jul.onnx

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7ba2fdaa87b8cbbb52c16b5c6e31a7452c00e8ad68aec580bfb7b07f5b212619
-size 69584537