diff --git a/.gitignore b/.gitignore
index 059c47a..b475da3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -166,3 +166,4 @@ cython_debug/
#.idea/
.DS_Store
output/svg
+*.mp4
\ No newline at end of file
diff --git a/boom.ipynb b/boom.ipynb
index 5af2b86..afe08d0 100644
--- a/boom.ipynb
+++ b/boom.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": 95,
"metadata": {},
"outputs": [],
"source": [
@@ -118,7 +118,7 @@
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": 96,
"metadata": {},
"outputs": [
{
@@ -155,7 +155,7 @@
},
{
"cell_type": "code",
- "execution_count": 40,
+ "execution_count": 97,
"metadata": {},
"outputs": [],
"source": [
@@ -284,12 +284,13 @@
},
{
"cell_type": "code",
- "execution_count": 41,
+ "execution_count": 98,
"metadata": {},
"outputs": [],
"source": [
"plane_a = DiamondPlane3D(markers)\n",
"\n",
+ "OFFSET = 0.000\n",
"markers_b = generate_diamond_corners((20, 21, 22, 23), params)\n",
"plane_b = DiamondPlane3D(markers_b)\n",
"# plane_b.translate(np.array([0, 0, 0.1]))\n",
@@ -300,6 +301,9 @@
"plane_b.rotate(np.pi, np.array([0, 1, 0]))\n",
"plane_b.translate(tmp_c)\n",
"plane_b.translate(np.array([0, 0, params.total_side_length]))\n",
+ "plane_b.translate(np.array([0, 0, -OFFSET]))\n",
+ "# OFFSET for plane_b\n",
+ "# plane_b.translate(np.array([0, 0.001, 0]))\n",
"\n",
"markers_c = generate_diamond_corners((24, 25, 26, 27), params)\n",
"plane_c = DiamondPlane3D(markers_c)\n",
@@ -309,12 +313,13 @@
"plane_c.translate(tmp)\n",
"plane_c.translate(np.array([0, params.total_side_length-params.border_length, 0]))\n",
"plane_c.rotate(np.pi/2, np.array([0, 1, 0]))\n",
- "plane_c.translate(np.array([0, 0, params.total_side_length]))"
+ "plane_c.translate(np.array([0, 0, params.total_side_length]))\n",
+ "plane_c.translate(np.array([0, 0, -OFFSET]))"
]
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": 99,
"metadata": {},
"outputs": [
{
@@ -1722,7 +1727,7 @@
},
{
"cell_type": "code",
- "execution_count": 43,
+ "execution_count": 100,
"metadata": {},
"outputs": [
{
@@ -1775,7 +1780,7 @@
},
{
"cell_type": "code",
- "execution_count": 44,
+ "execution_count": 101,
"metadata": {},
"outputs": [],
"source": [
@@ -1787,7 +1792,7 @@
},
{
"cell_type": "code",
- "execution_count": 45,
+ "execution_count": 102,
"metadata": {},
"outputs": [
{
@@ -1843,7 +1848,7 @@
" [2.30233590e-17, 2.49000005e-01, 3.50000129e-02]])}"
]
},
- "execution_count": 45,
+ "execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
@@ -1854,7 +1859,7 @@
},
{
"cell_type": "code",
- "execution_count": 46,
+ "execution_count": 103,
"metadata": {},
"outputs": [],
"source": [
diff --git a/cali.py b/cali.py
index 31939ed..f067b4c 100644
--- a/cali.py
+++ b/cali.py
@@ -37,10 +37,10 @@ class ArucoDictionary(Enum):
Dict_ArUco_ORIGINAL = aruco.DICT_ARUCO_ORIGINAL
-IMAGE_FOLDER = Path("dumped/cam")
+IMAGE_FOLDER = Path("dumped/batch_two/b")
OUTPUT_FOLDER = Path("output")
DICTIONARY = ArucoDictionary.Dict_4X4_50
-CALIBRATION_PARQUET: Optional[Path] = OUTPUT_FOLDER / "usbcam_cal.parquet"
+CALIBRATION_PARQUET: Optional[Path] = OUTPUT_FOLDER / "af_03.parquet"
class CameraParams(TypedDict):
diff --git a/find_cute_object.py b/find_cute_object.py
index c86f70c..9c15990 100644
--- a/find_cute_object.py
+++ b/find_cute_object.py
@@ -1,10 +1,9 @@
-import re
import cv2
from cv2 import aruco
from datetime import datetime
from loguru import logger
from pathlib import Path
-from typing import cast, Final
+from typing import Optional, cast, Final
import awkward as ak
from cv2.typing import MatLike
import numpy as np
@@ -42,6 +41,7 @@ def main():
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)
@@ -123,6 +123,8 @@ def main():
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
@@ -131,6 +133,17 @@ def main():
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__":
diff --git a/get_ext.ipynb b/get_ext.ipynb
new file mode 100644
index 0000000..50f68b0
--- /dev/null
+++ b/get_ext.ipynb
@@ -0,0 +1,396 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "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": 2,
+ "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-af_03.parquet\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'ops_map'"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "{16: array([[0.152, 0.025, 0. ],\n",
+ " [0.249, 0.025, 0. ],\n",
+ " [0.249, 0.122, 0. ],\n",
+ " [0.152, 0.122, 0. ]]),\n",
+ " 17: array([[0.025, 0.152, 0. ],\n",
+ " [0.122, 0.152, 0. ],\n",
+ " [0.122, 0.249, 0. ],\n",
+ " [0.025, 0.249, 0. ]]),\n",
+ " 18: array([[0.27900001, 0.152 , 0. ],\n",
+ " [0.37599999, 0.152 , 0. ],\n",
+ " [0.37599999, 0.249 , 0. ],\n",
+ " [0.27900001, 0.249 , 0. ]]),\n",
+ " 19: array([[0.152 , 0.27900001, 0. ],\n",
+ " [0.249 , 0.27900001, 0. ],\n",
+ " [0.249 , 0.37599999, 0. ],\n",
+ " [0.152 , 0.37599999, 0. ]]),\n",
+ " 20: array([[1.51999995e-01, 1.70838222e-17, 3.86000000e-01],\n",
+ " [2.48999998e-01, 2.89628965e-17, 3.86000000e-01],\n",
+ " [2.48999998e-01, 2.30233595e-17, 2.88999999e-01],\n",
+ " [1.51999995e-01, 1.11442852e-17, 2.88999999e-01]]),\n",
+ " 21: array([[ 2.50000004e-02, -6.24569833e-18, 2.59000005e-01],\n",
+ " [ 1.22000001e-01, 5.63337574e-18, 2.59000005e-01],\n",
+ " [ 1.22000001e-01, -3.06161408e-19, 1.62000002e-01],\n",
+ " [ 2.50000004e-02, -1.21852355e-17, 1.62000002e-01]]),\n",
+ " 22: array([[2.79000014e-01, 2.48603320e-17, 2.59000005e-01],\n",
+ " [3.75999987e-01, 3.67394027e-17, 2.59000005e-01],\n",
+ " [3.75999987e-01, 3.07998655e-17, 1.62000002e-01],\n",
+ " [2.79000014e-01, 1.89207949e-17, 1.62000002e-01]]),\n",
+ " 23: array([[ 1.51999995e-01, 1.53080704e-18, 1.31999986e-01],\n",
+ " [ 2.48999998e-01, 1.34098813e-17, 1.31999986e-01],\n",
+ " [ 2.48999998e-01, 7.47034601e-18, 3.50000129e-02],\n",
+ " [ 1.51999995e-01, -4.40872829e-18, 3.50000129e-02]]),\n",
+ " 24: array([[1.53080852e-18, 2.49000005e-01, 3.86000000e-01],\n",
+ " [1.53080852e-18, 1.52000002e-01, 3.86000000e-01],\n",
+ " [7.47034556e-18, 1.52000002e-01, 2.88999999e-01],\n",
+ " [7.47034556e-18, 2.49000005e-01, 2.88999999e-01]]),\n",
+ " 25: array([[9.30731537e-18, 3.76000000e-01, 2.59000005e-01],\n",
+ " [9.30731537e-18, 2.78999999e-01, 2.59000005e-01],\n",
+ " [1.52468525e-17, 2.78999999e-01, 1.62000002e-01],\n",
+ " [1.52468525e-17, 3.76000000e-01, 1.62000002e-01]]),\n",
+ " 26: array([[9.30731537e-18, 1.21999986e-01, 2.59000005e-01],\n",
+ " [9.30731537e-18, 2.50000129e-02, 2.59000005e-01],\n",
+ " [1.52468525e-17, 2.50000129e-02, 1.62000002e-01],\n",
+ " [1.52468525e-17, 1.21999986e-01, 1.62000002e-01]]),\n",
+ " 27: array([[1.70838237e-17, 2.49000005e-01, 1.31999986e-01],\n",
+ " [1.70838237e-17, 1.52000002e-01, 1.31999986e-01],\n",
+ " [2.30233590e-17, 1.52000002e-01, 3.50000129e-02],\n",
+ " [2.30233590e-17, 2.49000005e-01, 3.50000129e-02]])}"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "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",
+ "a_mtx, a_dist = read_camera_calibration(A_CALIBRATION_PARQUET)\n",
+ "b_camera_matrix, b_distortion_coefficients = read_camera_calibration(B_CALIBRATION_PARQUET)\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": 4,
+ "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": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "A_IMG = Path(\"dumped/batch_two/op/video-20241219-142434-op-a.png\")\n",
+ "B_IMG = Path(\"dumped/batch_two/op/video-20241219-142439-op-b.png\")\n",
+ "a_img = cv2.imread(str(A_IMG))\n",
+ "b_img = cv2.imread(str(B_IMG))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/cj/0zmvpygn7m72m42lh6x_hcgw0000gn/T/ipykernel_28395/542219436.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": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/cj/0zmvpygn7m72m42lh6x_hcgw0000gn/T/ipykernel_28395/542219436.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_camera_matrix, b_distortion_coefficients)\n",
+ "# plt.imshow(cv2.cvtColor(b_result_img, cv2.COLOR_BGR2RGB))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'params'"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "[{name: 'a', rvec: [[-1.04], ..., [-2.95]], tvec: [...], ...},\n",
+ " {name: 'b', rvec: [[0.948], ..., [2.97]], tvec: [...], ...}]\n",
+ "--------------------------------------------------------------\n",
+ "type: 2 * {\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",
+ "}"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "\n",
+ " created_by: parquet-cpp-arrow version 14.0.1\n",
+ " num_columns: 5\n",
+ " num_rows: 2\n",
+ " num_row_groups: 1\n",
+ " format_version: 2.6\n",
+ " serialized_size: 0"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "params = AwkwardArray(\n",
+ " [\n",
+ " {\n",
+ " \"name\": \"a\",\n",
+ " \"rvec\": a_rvec,\n",
+ " \"tvec\": a_tvec,\n",
+ " \"camera_matrix\": a_mtx,\n",
+ " \"distortion_coefficients\": a_dist,\n",
+ " },\n",
+ " {\n",
+ " \"name\": \"b\",\n",
+ " \"rvec\": b_rvec,\n",
+ " \"tvec\": b_tvec,\n",
+ " \"camera_matrix\": b_camera_matrix,\n",
+ " \"distortion_coefficients\": b_distortion_coefficients,\n",
+ " },\n",
+ " ]\n",
+ ")\n",
+ "display(\"params\", params)\n",
+ "ak.to_parquet(params, Path(\"output\") / \"params.parquet\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 9,
+ "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)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
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