Refactor PDF handling and add new scripts. Removed specific PDF files from LFS tracking and replaced with a wildcard for all PDFs. Added a script to convert PDFs to PNGs and created new Jupyter notebooks for drawing and marker detection. Added new charuco PDF files for 410x410 markers.

.gitattributes

@@ -1,5 +1,2 @@
 *.parquet filter=lfs diff=lfs merge=lfs -text
-charuco_400x400_3x3_s130_m100_no_24.pdf filter=lfs diff=lfs merge=lfs -text
-charuco_1189x841_10x7_s115_m90.pdf filter=lfs diff=lfs merge=lfs -text
-charuco_400x400_3x3_s130_m100_no_16.pdf filter=lfs diff=lfs merge=lfs -text
-charuco_400x400_3x3_s130_m100_no_20.pdf filter=lfs diff=lfs merge=lfs -text
+*.pdf filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -166,3 +166,5 @@ cython_debug/
 #.idea/
 .DS_Store
 output/svg
+*.mp4
+output

cali.py

@@ -9,9 +9,6 @@ from itertools import chain
 from typing import Optional, Sequence, TypedDict, cast
 import awkward as ak
 
-from matplotlib.pyplot import stem
-from numpy import ndarray
-
 
 class ArucoDictionary(Enum):
     Dict_4X4_50 = aruco.DICT_4X4_50
@@ -37,10 +34,10 @@ class ArucoDictionary(Enum):
     Dict_ArUco_ORIGINAL = aruco.DICT_ARUCO_ORIGINAL
 
 
-IMAGE_FOLDER = Path("dumped/usbcam")
+IMAGE_FOLDER = Path("dumped/batch_three/c")
 OUTPUT_FOLDER = Path("output")
 DICTIONARY = ArucoDictionary.Dict_4X4_50
-CALIBRATION_PARQUET: Optional[Path] = OUTPUT_FOLDER / "usbcam_cal.parquet"
+CALIBRATION_PARQUET: Optional[Path] = OUTPUT_FOLDER / "c-af_03.parquet"
 
 
 class CameraParams(TypedDict):
@@ -140,7 +137,7 @@ def main():
             "rotation_vectors": rvecs,
             "translation_vectors": tvecs,
         }
-        ak.to_parquet([parameters], OUTPUT_FOLDER / "calibration.parquet")
+        ak.to_parquet([parameters], CALIBRATION_PARQUET)
     else:
         logger.warning(
             "no calibration data calculated; either no images or already calibrated"

compute_3d_maybe.ipynb

@@ -0,0 +1,157 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import awkward as ak\n",
+    "from awkward import Array as AwakwardArray, Record as AwkwardRecord\n",
+    "from typing import cast\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre>[{prediction: None, trackings: [], frame_num: 0, ...},\n",
+       " {prediction: None, trackings: [], frame_num: 1, ...},\n",
+       " {prediction: None, trackings: [], frame_num: 2, ...},\n",
+       " {prediction: None, trackings: [], frame_num: 3, ...},\n",
+       " {prediction: None, trackings: [], frame_num: 4, ...},\n",
+       " {prediction: None, trackings: [], frame_num: 5, ...},\n",
+       " {prediction: None, trackings: [], frame_num: 6, ...},\n",
+       " {prediction: None, trackings: [], frame_num: 7, ...},\n",
+       " {prediction: None, trackings: [], frame_num: 8, ...},\n",
+       " {prediction: {Akeypoints: [[...]], ...}, trackings: [{...}], ...},\n",
+       " ...,\n",
+       " {prediction: {Akeypoints: [[...]], ...}, trackings: [{...}], ...},\n",
+       " {prediction: {Akeypoints: [[...]], ...}, trackings: [{...}], ...},\n",
+       " {prediction: {Akeypoints: [[...]], ...}, trackings: [{...}], ...},\n",
+       " {prediction: {Akeypoints: [[...]], ...}, trackings: [{...}], ...},\n",
+       " {prediction: {Akeypoints: [[...]], ...}, trackings: [{...}], ...},\n",
+       " {prediction: {Akeypoints: [[...]], ...}, trackings: [{...}], ...},\n",
+       " {prediction: {Akeypoints: [[...]], ...}, trackings: [{...}], ...},\n",
+       " {prediction: {Akeypoints: [[...]], ...}, trackings: [{...}], ...},\n",
+       " {prediction: {Akeypoints: [[...]], ...}, trackings: [{...}], ...}]\n",
+       "-------------------------------------------------------------------\n",
+       "type: 808 * {\n",
+       "    prediction: ?{\n",
+       "        Akeypoints: var * var * var * float64,\n",
+       "        bboxes: var * var * float64,\n",
+       "        scores: var * var * var * float64,\n",
+       "        frame_number: int64,\n",
+       "        reference_frame_size: {\n",
+       "            &quot;0&quot;: int64,\n",
+       "            &quot;1&quot;: int64\n",
+       "        }\n",
+       "    },\n",
+       "    trackings: var * {\n",
+       "        id: int64,\n",
+       "        bounding_boxes: var * var * float64\n",
+       "    },\n",
+       "    frame_num: int64,\n",
+       "    reference_frame_size: {\n",
+       "        height: int64,\n",
+       "        width: int64\n",
+       "    }\n",
+       "}</pre>"
+      ],
+      "text/plain": [
+       "<Array [{prediction: None, ...}, ..., {...}] type='808 * {prediction: ?{Ake...'>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "a_ak = ak.from_parquet(\"pose/a.parquet\")\n",
+    "b_ak = ak.from_parquet(\"pose/b.parquet\")\n",
+    "# display(a_ak)\n",
+    "display(b_ak)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre>{Akeypoints: [[[893, 417], [898, 408], [...], ..., [782, 596], [785, 599]]],\n",
+       " bboxes: [[756, 341, 940, 597]],\n",
+       " scores: [[[0.907], [0.896], [0.916], [0.341], ..., [0.811], [0.835], [0.802]]],\n",
+       " frame_number: 5,\n",
+       " reference_frame_size: {&#x27;0&#x27;: 1080, &#x27;1&#x27;: 1920}}\n",
+       "--------------------------------------------------------------------------------\n",
+       "type: {\n",
+       "    Akeypoints: var * var * var * float64,\n",
+       "    bboxes: var * var * float64,\n",
+       "    scores: var * var * var * float64,\n",
+       "    frame_number: int64,\n",
+       "    reference_frame_size: {\n",
+       "        &quot;0&quot;: int64,\n",
+       "        &quot;1&quot;: int64\n",
+       "    }\n",
+       "}</pre>"
+      ],
+      "text/plain": [
+       "<Record {Akeypoints: [[...]], bboxes: ..., ...} type='{Akeypoints: var * va...'>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a_ak[\"prediction\"][5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "unique_tracking_ids_a = np.unique(ak.to_numpy(ak.flatten(cast(AwakwardArray, a_ak[\"trackings\"][\"id\"]))))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

cvt_all_pdfs.sh

@@ -0,0 +1,14 @@
+#!/bin/bash
+
+# Set input folder
+INPUT_DIR="board"
+DPI=100  # Resolution in DPI
+
+# Iterate over all .pdf files in the folder
+for pdf in "$INPUT_DIR"/*.pdf; do
+    # Strip .pdf to get base name
+    base="${pdf%.pdf}"
+    # Convert to PNG
+    magick convert -density "$DPI" "$pdf" "${base}.png"
+    echo "Converted: $pdf → ${base}.png"
+done

dump_and_play.sh

@@ -0,0 +1,58 @@
+#!/bin/bash
+
+if [ -z $1 ]; then
+    echo "Usage: $0 <port>"
+    exit 1
+else
+    echo "dumping video from port $1"
+fi
+
+TARGET_PORT=$1;
+
+if ! [[ $TARGET_PORT =~ ^[0-9]+$ ]] ; then
+    echo "error: expect a number, got $TARGET_PORT" >&2
+    exit 1
+fi
+
+# See also majestic.yaml
+# Get the current date and time in the format YYYYMMDD-HHMMSS
+DATE=$(date +"%Y%m%d-%H%M%S")
+
+# use mts as MPEG transport stream
+FILENAME="output/video-${DATE}-${TARGET_PORT}.mts"
+# SINK="autovideosink"
+SINK="glimagesink"
+
+
+# Run the GStreamer pipeline with the dynamic filename
+# gst-launch-1.0 -e udpsrc port=$TARGET_PORT \
+# ! 'application/x-rtp,encoding-name=H265,payload=96' \
+# ! rtph265depay \
+# ! h265parse \
+# ! tee name=t \
+# t. ! queue ! $DECODER ! videoconvert ! $SINK \
+# t. ! queue ! mp4mux ! filesink location=$FILENAME
+
+
+# DECODER="nvh265dec"
+# DECODER="vulkanh265dec"
+# DECODER="avdec_h265"
+DECODER="vtdec_hw"
+# DECODER="vtdec"
+# gst-launch-1.0 -e udpsrc port=$TARGET_PORT auto-multicast=true multicast-group=224.0.0.123 \
+# ! 'application/x-rtp,encoding-name=H265,payload=96' \
+# ! rtph265depay \
+# ! tee name=t \
+# ! h265parse \
+# t. ! queue ! $DECODER ! videoconvert ! $SINK \
+# t. ! queue ! mpegtsmux ! filesink location=$FILENAME
+
+# hvc1
+# hev1
+gst-launch-1.0 -e udpsrc port=$TARGET_PORT auto-multicast=true multicast-group=224.0.0.123 \
+! 'application/x-rtp,encoding-name=H265,payload=96' \
+! rtph265depay \
+! tee name=t \
+t. ! queue ! h265parse ! "video/x-h265,stream-format=hvc1" ! $DECODER ! videoconvert ! $SINK \
+t. ! queue ! h265parse ! mpegtsmux ! filesink location=$FILENAME
+

find_cute_box.py

@@ -10,9 +10,14 @@ import numpy as np
 
 NDArray = np.ndarray
 CALIBRATION_PARQUET = Path("output") / "usbcam_cal.parquet"
-DICTIONARY: Final[int] = aruco.DICT_4X4_50
+# 7x7
+DICTIONARY: Final[int] = aruco.DICT_7X7_1000
 # 400mm
 MARKER_LENGTH: Final[float] = 0.4
+RED = (0, 0, 255)
+GREEN = (0, 255, 0)
+BLUE = (255, 0, 0)
+YELLOW = (0, 255, 255)
 
 
 def gen():
@@ -47,23 +52,18 @@ def main():
             # logger.info("markers={}, ids={}", np.array(markers).shape, np.array(ids).shape)
             for m, i in zip(markers, ids):
                 center = np.mean(m, axis=0).astype(int)
-                GREY = (128, 128, 128)
                 logger.info("id={}, center={}", i, center)
-                cv2.circle(frame, tuple(center), 5, GREY, -1)
+                cv2.circle(frame, tuple(center), 5, RED, -1)
                 cv2.putText(
                     frame,
                     str(i),
                     tuple(center),
                     cv2.FONT_HERSHEY_SIMPLEX,
                     1,
-                    GREY,
+                    RED,
                     2,
                 )
                 # BGR
-                RED = (0, 0, 255)
-                GREEN = (0, 255, 0)
-                BLUE = (255, 0, 0)
-                YELLOW = (0, 255, 255)
                 color_map = [RED, GREEN, BLUE, YELLOW]
                 for color, corners in zip(color_map, m):
                     corners = corners.astype(int)

find_cute_object.py

@@ -0,0 +1,150 @@
+import cv2
+from cv2 import aruco
+from datetime import datetime
+from loguru import logger
+from pathlib import Path
+from typing import Optional, cast, Final
+import awkward as ak
+from cv2.typing import MatLike
+import numpy as np
+
+NDArray = np.ndarray
+CALIBRATION_PARQUET = Path("output") / "usbcam_cal.parquet"
+OBJECT_POINTS_PARQUET = Path("output") / "object_points.parquet"
+DICTIONARY: Final[int] = aruco.DICT_4X4_50
+# 400mm
+MARKER_LENGTH: Final[float] = 0.4
+
+
+def gen():
+    API = cv2.CAP_AVFOUNDATION
+    cap = cv2.VideoCapture(0, API)
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            logger.warning("Failed to grab frame")
+            break
+        yield frame
+
+
+def main():
+    aruco_dict = aruco.getPredefinedDictionary(DICTIONARY)
+    cal = ak.from_parquet(CALIBRATION_PARQUET)[0]
+    camera_matrix = cast(MatLike, ak.to_numpy(cal["camera_matrix"]))
+    distortion_coefficients = cast(MatLike, ak.to_numpy(cal["distortion_coefficients"]))
+    ops = ak.from_parquet(OBJECT_POINTS_PARQUET)
+    detector = aruco.ArucoDetector(
+        dictionary=aruco_dict, detectorParams=aruco.DetectorParameters()
+    )
+
+    total_ids = cast(NDArray, ak.to_numpy(ops["ids"])).flatten()
+    total_corners = cast(NDArray, ak.to_numpy(ops["corners"])).reshape(-1, 4, 3)
+    ops_map: dict[int, NDArray] = dict(zip(total_ids, total_corners))
+    logger.info("ops_map={}", ops_map)
+    writer: Optional[cv2.VideoWriter] = None
+
+    for frame in gen():
+        grey = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+        # pylint: disable-next=unpacking-non-sequence
+        markers, ids, rejected = detector.detectMarkers(grey)
+        # `markers` is [N, 1, 4, 2]
+        # `ids` is [N, 1]
+        if ids is not None:
+            markers = np.reshape(markers, (-1, 4, 2))
+            ids = np.reshape(ids, (-1, 1))
+            # logger.info("markers={}, ids={}", np.array(markers).shape, np.array(ids).shape)
+            ips_map: dict[int, NDArray] = {}
+            for cs, id in zip(markers, ids):
+                id = int(id)
+                cs = cast(NDArray, cs)
+                ips_map[id] = cs
+                center = np.mean(cs, axis=0).astype(int)
+                GREY = (128, 128, 128)
+                # logger.info("id={}, center={}", id, center)
+                cv2.circle(frame, tuple(center), 5, GREY, -1)
+                cv2.putText(
+                    frame,
+                    str(id),
+                    tuple(center),
+                    cv2.FONT_HERSHEY_SIMPLEX,
+                    1,
+                    GREY,
+                    2,
+                )
+                # BGR
+                RED = (0, 0, 255)
+                GREEN = (0, 255, 0)
+                BLUE = (255, 0, 0)
+                YELLOW = (0, 255, 255)
+                color_map = [RED, GREEN, BLUE, YELLOW]
+                for color, corners in zip(color_map, cs):
+                    corners = corners.astype(int)
+                    frame = cv2.circle(frame, corners, 5, color, -1)
+            # https://docs.opencv.org/4.x/d9/d0c/group__calib3d.html#ga50620f0e26e02caa2e9adc07b5fbf24e
+            ops: NDArray = np.empty((0, 3), dtype=np.float32)
+            ips: NDArray = np.empty((0, 2), dtype=np.float32)
+            for id, ip in ips_map.items():
+                try:
+                    op = ops_map[id]
+                    assert ip.shape == (4, 2), f"corners.shape={ip.shape}"
+                    assert op.shape == (4, 3), f"op.shape={op.shape}"
+                    ops = np.concatenate((ops, op), axis=0)
+                    ips = np.concatenate((ips, ip), axis=0)
+                except KeyError:
+                    logger.warning("No object points for id={}", id)
+                    continue
+            assert len(ops) == len(ips), f"len(ops)={len(ops)} != len(ips)={len(ips)}"
+            if len(ops) > 0:
+                # https://docs.opencv.org/4.x/d5/d1f/calib3d_solvePnP.html
+                # https://docs.opencv.org/4.x/d5/d1f/calib3d_solvePnP.html#calib3d_solvePnP_flags
+                ret, rvec, tvec= cv2.solvePnP(
+                    objectPoints=ops,
+                    imagePoints=ips,
+                    cameraMatrix=camera_matrix,
+                    distCoeffs=distortion_coefficients,
+                    flags=cv2.SOLVEPNP_SQPNP,
+                )
+                # ret, rvec, tvec, inliners = cv2.solvePnPRansac(
+                #     objectPoints=ops,
+                #     imagePoints=ips,
+                #     cameraMatrix=camera_matrix,
+                #     distCoeffs=distortion_coefficients,
+                #     flags=cv2.SOLVEPNP_SQPNP,
+                # )
+                if ret:
+                    cv2.drawFrameAxes(
+                        frame,
+                        camera_matrix,
+                        distortion_coefficients,
+                        rvec,
+                        tvec,
+                        MARKER_LENGTH,
+                    )
+                else:
+                    logger.warning("Failed to solvePnPRansac")
+        cv2.imshow("frame", frame)
+        if writer is not None:
+            writer.write(frame)
+        if (k := cv2.waitKey(1)) == ord("q"):
+            logger.info("Exiting")
+            break
+        elif k == ord("s"):
+            now = datetime.now().strftime("%Y%m%d%H%M%S")
+            file_name = f"aruco_{now}.png"
+            logger.info("Saving to {}", file_name)
+            cv2.imwrite(file_name, frame)
+        elif k == ord("r"):
+            if writer is not None:
+                writer.release()
+                writer = None
+                logger.info("Recording stopped")
+            else:
+                now = datetime.now().strftime("%Y%m%d%H%M%S")
+                file_name = f"aruco_{now}.mp4"
+                logger.info("Recording to {}", file_name)
+                fourcc = cv2.VideoWriter.fourcc(*"mp4v")
+                writer = cv2.VideoWriter(file_name, fourcc, 20.0, frame.shape[:2][::-1])
+
+
+if __name__ == "__main__":
+    main()

get_ext.ipynb

@@ -0,0 +1,375 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cv2\n",
+    "from cv2 import aruco\n",
+    "from datetime import datetime\n",
+    "from loguru import logger\n",
+    "from pathlib import Path\n",
+    "from typing import Optional, cast, Final\n",
+    "import awkward as ak\n",
+    "from cv2.typing import MatLike\n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "import awkward as ak\n",
+    "from awkward import Record as AwkwardRecord, Array as AwkwardArray"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "NDArray = np.ndarray\n",
+    "OBJECT_POINTS_PARQUET = Path(\"output\") / \"object_points.parquet\"\n",
+    "DICTIONARY: Final[int] = aruco.DICT_4X4_50\n",
+    "# 400mm\n",
+    "MARKER_LENGTH: Final[float] = 0.4\n",
+    "\n",
+    "A_CALIBRATION_PARQUET = Path(\"output\") / \"a-ae_08.parquet\"\n",
+    "B_CALIBRATION_PARQUET = Path(\"output\") / \"b-ae_09.parquet\"\n",
+    "C_CALIBRATION_PARQUET = Path(\"output\") / \"c-af_03.parquet\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "aruco_dict = aruco.getPredefinedDictionary(DICTIONARY)\n",
+    "def read_camera_calibration(path: Path) -> tuple[MatLike, MatLike]:\n",
+    "    cal = ak.from_parquet(path)[0]\n",
+    "    camera_matrix = cast(MatLike, ak.to_numpy(cal[\"camera_matrix\"]))\n",
+    "    distortion_coefficients = cast(MatLike, ak.to_numpy(cal[\"distortion_coefficients\"]))\n",
+    "    return camera_matrix, distortion_coefficients\n",
+    "\n",
+    "ops = ak.from_parquet(OBJECT_POINTS_PARQUET)\n",
+    "detector = aruco.ArucoDetector(\n",
+    "    dictionary=aruco_dict, detectorParams=aruco.DetectorParameters()\n",
+    ")\n",
+    "\n",
+    "total_ids = cast(NDArray, ak.to_numpy(ops[\"ids\"])).flatten()\n",
+    "total_corners = cast(NDArray, ak.to_numpy(ops[\"corners\"])).reshape(-1, 4, 3)\n",
+    "ops_map: dict[int, NDArray] = dict(zip(total_ids, total_corners))\n",
+    "# display(\"ops_map\", ops_map)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def process(\n",
+    "    frame: MatLike,\n",
+    "    cam_mtx: MatLike,\n",
+    "    dist_coeffs: MatLike,\n",
+    "    target: Optional[MatLike] = None,\n",
+    ") -> tuple[MatLike, Optional[MatLike], Optional[MatLike]]:\n",
+    "    if target is None:\n",
+    "        target = frame.copy()\n",
+    "    grey = cv2.cvtColor(target, cv2.COLOR_BGR2GRAY)\n",
+    "    # pylint: disable-next=unpacking-non-sequence\n",
+    "    markers, ids, rejected = detector.detectMarkers(grey)\n",
+    "    # `markers` is [N, 1, 4, 2]\n",
+    "    # `ids` is [N, 1]\n",
+    "    ret_rvec: Optional[MatLike] = None\n",
+    "    ret_tvec: Optional[MatLike] = None\n",
+    "    if ids is not None:\n",
+    "        markers = np.reshape(markers, (-1, 4, 2))\n",
+    "        ids = np.reshape(ids, (-1, 1))\n",
+    "        # logger.info(\"markers={}, ids={}\", np.array(markers).shape, np.array(ids).shape)\n",
+    "        ips_map: dict[int, NDArray] = {}\n",
+    "        for cs, id in zip(markers, ids):\n",
+    "            id = int(id)\n",
+    "            cs = cast(NDArray, cs)\n",
+    "            ips_map[id] = cs\n",
+    "            center = np.mean(cs, axis=0).astype(int)\n",
+    "            GREY = (128, 128, 128)\n",
+    "            # logger.info(\"id={}, center={}\", id, center)\n",
+    "            cv2.circle(target, tuple(center), 5, GREY, -1)\n",
+    "            cv2.putText(\n",
+    "                target,\n",
+    "                str(id),\n",
+    "                tuple(center),\n",
+    "                cv2.FONT_HERSHEY_SIMPLEX,\n",
+    "                1,\n",
+    "                GREY,\n",
+    "                2,\n",
+    "            )\n",
+    "            # BGR\n",
+    "            RED = (0, 0, 255)\n",
+    "            GREEN = (0, 255, 0)\n",
+    "            BLUE = (255, 0, 0)\n",
+    "            YELLOW = (0, 255, 255)\n",
+    "            color_map = [RED, GREEN, BLUE, YELLOW]\n",
+    "            for color, corners in zip(color_map, cs):\n",
+    "                corners = corners.astype(int)\n",
+    "                target = cv2.circle(target, corners, 5, color, -1)\n",
+    "        # https://docs.opencv.org/4.x/d9/d0c/group__calib3d.html#ga50620f0e26e02caa2e9adc07b5fbf24e\n",
+    "        ops: NDArray = np.empty((0, 3), dtype=np.float32)\n",
+    "        ips: NDArray = np.empty((0, 2), dtype=np.float32)\n",
+    "        for id, ip in ips_map.items():\n",
+    "            try:\n",
+    "                op = ops_map[id]\n",
+    "                assert ip.shape == (4, 2), f\"corners.shape={ip.shape}\"\n",
+    "                assert op.shape == (4, 3), f\"op.shape={op.shape}\"\n",
+    "                ops = np.concatenate((ops, op), axis=0)\n",
+    "                ips = np.concatenate((ips, ip), axis=0)\n",
+    "            except KeyError:\n",
+    "                logger.warning(\"No object points for id={}\", id)\n",
+    "                continue\n",
+    "        assert len(ops) == len(ips), f\"len(ops)={len(ops)} != len(ips)={len(ips)}\"\n",
+    "        if len(ops) > 0:\n",
+    "            # https://docs.opencv.org/4.x/d5/d1f/calib3d_solvePnP.html\n",
+    "            # https://docs.opencv.org/4.x/d5/d1f/calib3d_solvePnP.html#calib3d_solvePnP_flags\n",
+    "            ret, rvec, tvec = cv2.solvePnP(\n",
+    "                objectPoints=ops,\n",
+    "                imagePoints=ips,\n",
+    "                cameraMatrix=cam_mtx,\n",
+    "                distCoeffs=dist_coeffs,\n",
+    "                flags=cv2.SOLVEPNP_SQPNP,\n",
+    "            )\n",
+    "            # ret, rvec, tvec, inliners = cv2.solvePnPRansac(\n",
+    "            #     objectPoints=ops,\n",
+    "            #     imagePoints=ips,\n",
+    "            #     cameraMatrix=camera_matrix,\n",
+    "            #     distCoeffs=distortion_coefficients,\n",
+    "            #     flags=cv2.SOLVEPNP_SQPNP,\n",
+    "            # )\n",
+    "            if ret:\n",
+    "                cv2.drawFrameAxes(\n",
+    "                    target,\n",
+    "                    cam_mtx,\n",
+    "                    dist_coeffs,\n",
+    "                    rvec,\n",
+    "                    tvec,\n",
+    "                    MARKER_LENGTH,\n",
+    "                )\n",
+    "                ret_rvec = rvec\n",
+    "                ret_tvec = tvec\n",
+    "    return target, ret_rvec, ret_tvec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A_IMG = Path(\"dumped/batch_three/video-20241224-154256-a.png\")\n",
+    "B_IMG = Path(\"dumped/batch_three/video-20241224-154302-b.png\")\n",
+    "C_IMG = Path(\"dumped/batch_three/video-20241224-154252-c.png\")\n",
+    "C_PRIME_IMG = Path(\"dumped/batch_three/video-20241224-153926-c-prime.png\")\n",
+    "\n",
+    "a_img = cv2.imread(str(A_IMG))\n",
+    "b_img = cv2.imread(str(B_IMG))\n",
+    "c_img = cv2.imread(str(C_IMG))\n",
+    "c_prime_img = cv2.imread(str(C_PRIME_IMG))\n",
+    "\n",
+    "a_mtx, a_dist = read_camera_calibration(A_CALIBRATION_PARQUET)\n",
+    "b_mtx, b_dist = read_camera_calibration(B_CALIBRATION_PARQUET)\n",
+    "c_mtx, c_dist = read_camera_calibration(C_CALIBRATION_PARQUET)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/cj/0zmvpygn7m72m42lh6x_hcgw0000gn/T/ipykernel_79393/542219436.py:22: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  id = int(id)\n"
+     ]
+    }
+   ],
+   "source": [
+    "a_result_img, a_rvec, a_tvec = process(a_img, a_mtx, a_dist)\n",
+    "# plt.imshow(cv2.cvtColor(a_result_img, cv2.COLOR_BGR2RGB))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/cj/0zmvpygn7m72m42lh6x_hcgw0000gn/T/ipykernel_79393/542219436.py:22: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  id = int(id)\n"
+     ]
+    }
+   ],
+   "source": [
+    "b_result_img, b_rvec, b_tvec = process(b_img, b_mtx, b_dist)\n",
+    "# plt.imshow(cv2.cvtColor(b_result_img, cv2.COLOR_BGR2RGB))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/cj/0zmvpygn7m72m42lh6x_hcgw0000gn/T/ipykernel_79393/542219436.py:22: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  id = int(id)\n"
+     ]
+    }
+   ],
+   "source": [
+    "c_result_img, c_rvec, c_tvec = process(c_img, c_mtx, c_dist)\n",
+    "c_prime_result_img, c_prime_rvec, c_prime_tvec = process(c_prime_img, c_mtx, c_dist)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'params'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre>[{name: &#x27;a-ae_08&#x27;, rvec: [[-0.602], ..., [-3.05]], tvec: [...], ...},\n",
+       " {name: &#x27;b-ae_09&#x27;, rvec: [[0.572], ..., [3.02]], tvec: [...], ...},\n",
+       " {name: &#x27;c-af_03&#x27;, rvec: [[-1.98], ..., [-2.4]], tvec: [...], ...},\n",
+       " {name: &#x27;c-prime-af_03&#x27;, rvec: [[-1.99], ...], tvec: [...], ...}]\n",
+       "---------------------------------------------------------------------\n",
+       "type: 4 * {\n",
+       "    name: string,\n",
+       "    rvec: var * var * float64,\n",
+       "    tvec: var * var * float64,\n",
+       "    camera_matrix: var * var * float64,\n",
+       "    distortion_coefficients: var * var * float64\n",
+       "}</pre>"
+      ],
+      "text/plain": [
+       "<Array [{name: 'a-ae_08', rvec: ..., ...}, ...] type='4 * {name: string, rv...'>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pyarrow._parquet.FileMetaData object at 0x311da8900>\n",
+       "  created_by: parquet-cpp-arrow version 14.0.1\n",
+       "  num_columns: 5\n",
+       "  num_rows: 4\n",
+       "  num_row_groups: 1\n",
+       "  format_version: 2.6\n",
+       "  serialized_size: 0"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "params = AwkwardArray(\n",
+    "    [\n",
+    "        {\n",
+    "            \"name\": \"a-ae_08\",\n",
+    "            \"rvec\": a_rvec,\n",
+    "            \"tvec\": a_tvec,\n",
+    "            \"camera_matrix\": a_mtx,\n",
+    "            \"distortion_coefficients\": a_dist,\n",
+    "        },\n",
+    "        {\n",
+    "            \"name\": \"b-ae_09\",\n",
+    "            \"rvec\": b_rvec,\n",
+    "            \"tvec\": b_tvec,\n",
+    "            \"camera_matrix\": b_mtx,\n",
+    "            \"distortion_coefficients\": b_dist,\n",
+    "        },\n",
+    "        {\n",
+    "            \"name\": \"c-af_03\",\n",
+    "            \"rvec\": c_rvec,\n",
+    "            \"tvec\": c_tvec,\n",
+    "            \"camera_matrix\": c_mtx,\n",
+    "            \"distortion_coefficients\": c_dist\n",
+    "        },\n",
+    "        {\n",
+    "            \"name\": \"c-prime-af_03\",\n",
+    "            \"rvec\": c_prime_rvec,\n",
+    "            \"tvec\": c_prime_tvec,\n",
+    "            \"camera_matrix\": c_mtx,\n",
+    "            \"distortion_coefficients\": c_dist\n",
+    "        }\n",
+    "    ]\n",
+    ")\n",
+    "display(\"params\", params)\n",
+    "ak.to_parquet(params, Path(\"output\") / \"params.parquet\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cv2.imwrite(\"output/a_result_img.png\", a_result_img)\n",
+    "cv2.imwrite(\"output/b_result_img.png\", b_result_img)\n",
+    "cv2.imwrite(\"output/c_result_img.png\", c_result_img)\n",
+    "cv2.imwrite(\"output/c_prime_result_img.png\", c_prime_result_img)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

run_capture.py

@@ -0,0 +1,132 @@
+from datetime import datetime
+from os import PathLike
+from pathlib import Path
+import signal
+from subprocess import Popen, TimeoutExpired
+from typing import Any, Literal
+from loguru import logger
+import click
+import loguru
+
+# pacman -S python-loguru
+# pacman -S python-click
+
+Mode = Literal["preview", "save", "save_preview"]
+MODE_LIST: list[Mode] = ["preview", "save", "save_preview"]
+MULTICAST_ADDR = "224.0.0.123"
+
+
+class DumpCommand:
+    port: int
+    output_path: str
+
+    def __init__(self, port: int, output_path: PathLike | str):
+        self.port = port
+        self.output_path = str(output_path)
+
+    def save_and_decode_nv_pipeline(self):
+        # note that capabilties SHOULD NOT have spaces in between
+        # `gst-launch-1.0` could tolerate that, but not the API itself
+        return f"""gst-launch-1.0 -e udpsrc port={self.port} \
+        ! 'application/x-rtp,encoding-name=H265,payload=96' \
+        ! rtph265depay \
+        ! h265parse \
+        ! tee name=t \
+        t. ! queue ! nvh265dec ! videoconvert ! autovideosink \
+        t. ! queue ! mp4mux ! filesink location={self.output_path}
+        """
+
+    def save_and_decode_nv_pipeline_multicast(self):
+        return f"""gst-launch-1.0 -e udpsrc port={self.port} \
+            auto-multicast=true \
+            multicast-group={MULTICAST_ADDR} \
+        ! 'application/x-rtp,encoding-name=H265,payload=96' \
+        ! rtph265depay \
+        ! h265parse \
+        ! tee name=t \
+        t. ! queue ! vtdec_hw ! videoconvert ! autovideosink \
+        t. ! queue ! mp4mux ! filesink location={self.output_path}
+        """
+        # `vtdec_hw` for macos
+        # `nvh265dec` for nv
+
+    def save_pipeline(self):
+        return f"""gst-launch-1.0 -e udpsrc port={self.port} \
+        ! 'application/x-rtp, encoding-name=H265, payload=96' \
+        ! rtph265depay \
+        ! queue ! h265parse ! mp4mux ! filesink location={self.output_path}
+        """
+
+    def decode_cv_only(self):
+        return f"""gst-launch-1.0 -e udpsrc port={self.port} \
+        ! 'application/x-rtp,encoding-name=H265,payload=96' \
+        ! rtph265depay \
+        ! h265parse \
+        ! nvh265dec \
+        ! videoconvert \
+        ! autovideosink
+        """
+
+    def get_pipeline_from_mode(self, mode: Mode):
+        if mode == "save":
+            return self.save_pipeline()
+        elif mode == "save_preview":
+            return self.save_and_decode_nv_pipeline_multicast()
+        elif mode == "preview":
+            return self.decode_cv_only()
+        raise ValueError(f"Unknown mode: {mode}")
+
+
+def test_filename(
+    port: int,
+    output_dir: PathLike | str,
+    date: datetime,
+    prefix="video_",
+    suffix=".mp4",
+):
+    date_str = date.strftime("%Y-%m-%d_%H-%M-%S")
+    assert suffix.startswith("."), "suffix should start with a dot"
+    file_name = f"{prefix}{date_str}_{port}{suffix}"
+    return Path(output_dir) / file_name
+
+
+# nmap -sS --open -p 22 192.168.2.0/24
+
+
+@click.command()
+@click.option("-o", "--output", type=click.Path(exists=True), default="output")
+@click.option("-m", "--mode", type=click.Choice(MODE_LIST), default="save_preview")
+def main(output: str, mode: Mode):
+    ports = [5601, 5602, 5603, 5604, 5605, 5606]
+    output_dir = Path(output)
+    now = datetime.now()
+    commands = [
+        DumpCommand(port, test_filename(port, output_dir, now)) for port in ports
+    ]
+    ps: list[Popen] = []
+    run_flag: bool = True
+
+    def handle_sigint(signum: int, frame: Any):
+        nonlocal run_flag
+        run_flag = False
+        logger.info("Received SIGINT, stopping all processes")
+
+    for command in commands:
+        p = Popen(command.get_pipeline_from_mode(mode), shell=True)
+        ps.append(p)
+
+    signal.signal(signal.SIGINT, handle_sigint)
+    while run_flag:
+        pass
+
+    for p in ps:
+        p.send_signal(signal.SIGINT)
+    for p in ps:
+        try:
+            p.wait(3)
+        except TimeoutExpired:
+            logger.warning("Command `{}` timeout", p.args)
+
+
+if __name__ == "__main__":
+    main()  # pylint: disable=no-value-for-parameter