Copied initial tools.

Dockerfile

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+FROM nvcr.io/nvidia/tensorflow:22.08-tf2-py3
+
+ARG DEBIAN_FRONTEND=noninteractive
+ENV LANG C.UTF-8
+ENV LC_ALL C.UTF-8
+WORKDIR /
+
+RUN apt-get update && apt-get install -y --no-install-recommends feh
+RUN apt-get update && apt-get install -y --no-install-recommends python3-opencv
+RUN pip uninstall -y opencv-python && pip install --no-cache "opencv-python<4.3"
+
+# Show matplotlib images
+RUN apt-get update && apt-get install -y --no-install-recommends python3-tk
+
+# Update pip to allow installation of skelda in editable mode
+RUN pip3 install --upgrade --no-cache-dir pip
+
+# Install pytorch
+RUN pip3 install --upgrade --no-cache-dir "torch<2.0"
+RUN pip3 install --upgrade --no-cache-dir "torchvision<0.15"
+
+# Install MMPose
+RUN pip3 install --upgrade --no-cache-dir openmim
+RUN mim install mmengine
+RUN mim install "mmpose>=1.1.0"
+# Fix an error when importing mmpose
+RUN pip3 install --upgrade --no-cache-dir numpy scipy
+RUN git clone --depth=1 --branch=main https://github.com/open-mmlab/mmpose.git
+
+# Download pretrained model
+COPY scripts/utils_2d_pose.py /
+RUN python3 -c "from utils_2d_pose import load_model; load_model();"
+RUN python3 -c "from utils_2d_pose import load_wb_model; load_wb_model();"
+
+# Fix an undefined symbol error with ompi
+RUN echo "ldconfig" >> ~/.bashrc
+
+COPY ./skelda/ /skelda/
+RUN pip3 install --no-cache-dir -e /skelda/
+
+WORKDIR /SimplePoseTriangulation/
+CMD ["/bin/bash"]

README.md

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 # SimplePoseTriangulation
+
+Triangulation of multiple persons from multiple camera views.
+
+<br>
+
+## Build
+
+- Clone this project with submodules: 
+    ```bash
+    git clone --recurse-submodules https://gitlab.com/Percipiote/SimplePoseTriangulation.git
+    cd SimplePoseTriangulation/
+    ```
+
+- Build docker container:
+  ```bash
+  docker build --progress=plain -t simpleposetriangulation .
+  ./run_container.sh
+  ```

pyproject.toml

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+[tool.isort]
+profile = "black"
+known_first_party = "skelda,draw_utils,test_triangulate,utils_2d_pose,triangulate_poses"

run_container.sh

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+#! /bin/bash
+
+xhost +
+docker run --privileged --rm --network host -it \
+  --gpus all --shm-size=16g --ulimit memlock=-1 --ulimit stack=67108864 \
+  --volume "$(pwd)"/:/SimplePoseTriangulation/ \
+  --volume "$(pwd)"/../datasets/:/datasets/ \
+  --volume "$(pwd)"/skelda/:/skelda/ \
+  --volume /tmp/.X11-unix:/tmp/.X11-unix \
+  --env DISPLAY --env QT_X11_NO_MITSHM=1 \
+  simpleposetriangulation

scripts/draw_utils.py

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+import matplotlib.pyplot as plt
+import numpy as np
+
+from skelda import utils_view
+
+# ==================================================================================================
+
+
+def show_poses2d(bodies, images, joint_names, title=""):
+    num_imgs = len(images)
+    rowbreak = int(num_imgs / 2.0 + 0.5)
+    fig, axs = plt.subplots(2, rowbreak, figsize=(30, 20))
+    fig.suptitle(title, fontsize=20)
+
+    if isinstance(bodies, np.ndarray):
+        bodies = bodies.tolist()
+
+    # Draw skeletons into images
+    for i, image in enumerate(images):
+        colors = plt.cm.hsv(np.linspace(0, 1, len(bodies[i]), endpoint=False)).tolist()
+        colors = [[int(c[0] * 255), int(c[1] * 255), int(c[2] * 255)] for c in colors]
+
+        for j, body in enumerate(bodies[i]):
+            image = utils_view.draw_body_in_image(image, body, joint_names, colors[j])
+
+    # Rescale image range for plotting
+    images = [img / 255.0 for img in images]
+
+    if rowbreak == 1:
+        axs[0].imshow(images[0])
+        if len(images) == 2:
+            axs[1].imshow(images[1])
+        else:
+            # Optionally delete last empty plot
+            fig.delaxes(axs[1])
+
+    else:
+        for i in range(rowbreak):
+            axs[0][i].imshow(images[i])
+            if i + rowbreak < num_imgs:
+                axs[1][i].imshow(images[i + rowbreak])
+            else:
+                # Optionally delete last empty plot
+                fig.delaxes(axs[1][i])
+
+    return fig

scripts/utils_2d_pose.py

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+import os
+
+import numpy as np
+from mmpose.apis import MMPoseInferencer
+
+# ==================================================================================================
+
+filepath = os.path.dirname(os.path.realpath(__file__)) + "/"
+
+# ==================================================================================================
+
+
+def load_model():
+    print("Loading mmpose model ...")
+
+    model = MMPoseInferencer(
+        pose2d="/mmpose/projects/rtmpose/rtmpose/body_2d_keypoint/rtmpose-l_8xb256-420e_coco-384x288.py",
+        pose2d_weights="https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-body7_pt-body7_420e-384x288-3f5a1437_20230504.pth",
+        det_model="/mmpose/projects/rtmpose/rtmdet/person/rtmdet_nano_320-8xb32_coco-person.py",
+        det_weights="https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmdet_nano_8xb32-100e_coco-obj365-person-05d8511e.pth",
+        det_cat_ids=[0],
+    )
+
+    print("Loaded mmpose model")
+    return model
+
+
+def load_wb_model():
+    print("Loading mmpose whole body model ...")
+
+    model = MMPoseInferencer(
+        pose2d="/mmpose/projects/rtmpose/rtmpose/wholebody_2d_keypoint/rtmpose-l_8xb32-270e_coco-wholebody-384x288.py",
+        pose2d_weights="https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-coco-wholebody_pt-aic-coco_270e-384x288-eaeb96c8_20230125.pth",
+        det_model="/mmpose/projects/rtmpose/rtmdet/person/rtmdet_nano_320-8xb32_coco-person.py",
+        det_weights="https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmdet_nano_8xb32-100e_coco-obj365-person-05d8511e.pth",
+        det_cat_ids=[0],
+    )
+
+    print("Loaded mmpose model")
+    return model
+
+
+# ==================================================================================================
+
+
+def get_2d_pose(model, imgs, num_joints=17):
+    """See: https://mmpose.readthedocs.io/en/latest/user_guides/inference.html#basic-usage"""
+
+    result_generator = model(imgs, show=False)
+    new_poses = []
+    for _ in range(len(imgs)):
+        result = next(result_generator)
+
+        poses = []
+        for i in range(len(result["predictions"][0])):
+            kpts = result["predictions"][0][i]["keypoints"]
+            scores = result["predictions"][0][i]["keypoint_scores"]
+
+            kpts = np.array(kpts)
+            scores = np.array(scores).reshape(-1, 1)
+            scores = np.clip(scores, 0, 1)
+            pose = np.concatenate((kpts, scores), axis=-1)
+            poses.append(pose)
+
+        if len(poses) == 0:
+            poses.append(np.zeros([num_joints, 3]))
+
+        poses = np.array(poses)
+        new_poses.append(poses)
+
+    return new_poses