x

.gitignore

@@ -10,3 +10,5 @@ wheels/
 .venv
 .hypothesis
 samples
+*.jpg
+*.parquet

app/camera/__init__.py

@@ -1,6 +1,7 @@
 from collections import OrderedDict, defaultdict
 from dataclasses import dataclass
 from datetime import datetime
+import string
 from typing import Any, TypeAlias, TypedDict, Optional, Sequence
 
 from beartype import beartype
@@ -522,10 +523,14 @@ def to_homogeneous(points: Num[Array, "N 2"] | Num[Array, "N 3"]) -> Num[Array,
         raise ValueError(f"Invalid shape for points: {points.shape}")
 
 
+import awkward as ak
+
+
 @jaxtyped(typechecker=beartype)
 def point_line_distance(
     points: Num[Array, "N 3"] | Num[Array, "N 2"],
     line: Num[Array, "N 3"],
+    description: str,
     eps: float = 1e-9,
 ):
     """
@@ -544,6 +549,12 @@ def point_line_distance(
     """
     numerator = abs(line[:, 0] * points[:, 0] + line[:, 1] * points[:, 1] + line[:, 2])
     denominator = jnp.sqrt(line[:, 0] * line[:, 0] + line[:, 1] * line[:, 1])
+    # line_data = {"a": line[:, 0], "b": line[:, 1], "c": line[:, 2]}
+    # line_x_y = {"x": points[:, 0], "y": points[:, 1]}
+    # ak.to_parquet(
+    #     line_data, f"/home/admin/Code/CVTH3PE/line_a_b_c_{description}.parquet"
+    # )
+    # ak.to_parquet(line_x_y, f"/home/admin/Code/CVTH3PE/line_x_y_{description}.parquet")
     return numerator / (denominator + eps)
 
 
@@ -571,7 +582,7 @@ def left_to_right_epipolar_distance(
     """
     F_t = fundamental_matrix.transpose()
     line1_in_2 = jnp.matmul(left, F_t)
-    return point_line_distance(right, line1_in_2)
+    return point_line_distance(right, line1_in_2, "left_to_right")
 
 
 @jaxtyped(typechecker=beartype)
@@ -597,7 +608,7 @@ def right_to_left_epipolar_distance(
         $$x^{\\prime T}Fx = 0$$
     """
     line2_in_1 = jnp.matmul(right, fundamental_matrix)
-    return point_line_distance(left, line2_in_1)
+    return point_line_distance(left, line2_in_1, "right_to_left")
 
 
 def distance_between_epipolar_lines(

filter_object_by_box.py

@@ -0,0 +1,223 @@
+import awkward as ak
+import numpy as np
+from pathlib import Path
+from matplotlib import pyplot as plt
+import cv2
+from typing import Optional, cast, Final, TypedDict
+from typing import (
+    Any,
+    Generator,
+    Optional,
+    Sequence,
+    TypeAlias,
+    TypedDict,
+    cast,
+    overload,
+)
+from jaxtyping import Array, Float, Num, jaxtyped
+from shapely import box
+from app.visualize.whole_body import visualize_whole_body
+import pyproj
+from shapely.geometry import Polygon
+from sympy import false, true
+
+NDArray: TypeAlias = np.ndarray
+
+
+# 盒子各个面的三维三角形集合
+box_triangles_list = [
+    ["4", "6", "7"],
+    ["4", "5", "6"],
+    ["2", "5", "6"],
+    ["1", "2", "5"],
+    ["1", "2", "3"],
+    ["0", "1", "3"],
+    ["0", "3", "7"],
+    ["0", "4", "7"],
+    ["2", "6", "7"],
+    ["2", "3", "7"],
+    ["0", "4", "5"],
+    ["0", "1", "5"],
+]
+
+
+class Camera_Params(TypedDict):
+    rvec: Num[NDArray, "3"]
+    tvec: Num[NDArray, "3"]
+    camera_matrix: Num[Array, "3 3"]
+    dist: Num[Array, "N"]
+    width: int
+    height: int
+
+
+class KeypointDataset(TypedDict):
+    frame_index: int
+    boxes: Num[NDArray, "N 4"]
+    kps: Num[NDArray, "N J 2"]
+    kps_scores: Num[NDArray, "N J"]
+
+
+# 三维坐标系根据相机内外参计算该镜头下的二维重投影坐标
+def reprojet_3d_to_2d(point_3d, camera_param):
+    point_2d, _ = cv2.projectPoints(
+        objectPoints=point_3d,
+        rvec=np.array(camera_param.params.Rt[:3, :3]),
+        tvec=np.array(camera_param.params.Rt[:3, 3]),
+        cameraMatrix=np.array(camera_param.params.K),
+        distCoeffs=np.array(camera_param.params.dist_coeffs),
+    )
+
+    point_2d = point_2d.reshape(-1).astype(int)
+    return point_2d
+
+
+# 计算盒子三维坐标系
+def calculaterCubeVersices(position, dimensions):
+    [cx, cy, cz] = position
+    [width, height, depth] = dimensions
+    halfWidth = width / 2
+    halfHeight = height / 2
+    halfDepth = depth / 2
+
+    return [
+        [cx - halfWidth, cy - halfHeight, cz - halfDepth],
+        [cx + halfWidth, cy - halfHeight, cz - halfDepth],
+        [cx + halfWidth, cy + halfHeight, cz - halfDepth],
+        [cx - halfWidth, cy + halfHeight, cz - halfDepth],
+        [cx - halfWidth, cy - halfHeight, cz + halfDepth],
+        [cx + halfWidth, cy - halfHeight, cz + halfDepth],
+        [cx + halfWidth, cy + halfHeight, cz + halfDepth],
+        [cx - halfWidth, cy + halfHeight, cz + halfDepth],
+    ]
+
+
+# 获得盒子三维坐标系
+def calculater_box_3d_points():
+    # 盒子原点位置，相对于六面体中心偏移
+    box_ori_potision = [0.205 + 0.2, 0.205 + 0.50, -0.205 - 0.2]
+    # 盒子边长，宽：1.5米，高：1.5米，深度：1.8米
+    box_geometry = [0.65, 1.8, 1.5]
+    filter_box_points_3d = calculaterCubeVersices(box_ori_potision, box_geometry)
+    filter_box_points_3d = {
+        str(index): element for index, element in enumerate(filter_box_points_3d)
+    }
+
+    return filter_box_points_3d
+
+
+# 计算盒子坐标系的二维重投影数据
+def calculater_box_2d_points(filter_box_points_3d, camera_param):
+    box_points_2d = dict()
+    for element_index, elment_point_3d in enumerate(filter_box_points_3d.values()):
+        box_points_2d[str(element_index)] = reprojet_3d_to_2d(
+            np.array(elment_point_3d), camera_param
+        ).tolist()
+
+    return box_points_2d
+
+
+# 盒子总的二维平面各三角形坐标点
+def calculater_box_common_scope(box_points_2d):
+    box_triangles_all_points = []
+    # 遍历三角形个数
+    for i in range(len(box_triangles_list)):
+        # 获取单个三角形二维平面坐标点
+        single_triangles = []
+        for element_key in box_triangles_list[i]:
+            single_triangles.append(box_points_2d[element_key])
+        box_triangles_all_points.append(single_triangles)
+
+    return box_triangles_all_points
+
+
+def calculate_triangle_union(triangles):
+    """
+    计算多个三角形的并集区域
+
+    参数:
+    triangles: 包含多个三角形的列表，每个三角形由三个点的坐标组成
+
+    返回:
+    union_area: 并集区域的面积
+    union_polygon: 表示并集区域的多边形对象
+    """
+    # 创建多边形对象列表
+    polygons = [Polygon(tri) for tri in triangles]
+
+    # 计算并集
+    union_polygon = polygons[0]
+    for polygon in polygons[1:]:
+        union_polygon = union_polygon.union(polygon)
+
+    # 计算并集面积
+    union_area = union_polygon.area
+
+    return union_area, union_polygon
+
+
+# 射线法，判断坐标点是否在box二维重投影的区域内
+def point_in_polygon(p, polygon):
+    x, y = p
+    n = len(polygon)
+    intersections = 0
+    on_boundary = False
+
+    for i in range(n):
+        xi, yi = polygon[i]
+        xj, yj = polygon[(i + 1) % n]  # 闭合多边形
+
+        # 检查点是否在顶点上
+        if (x == xi and y == yi) or (x == xj and y == yj):
+            on_boundary = True
+            break
+
+        # 检查点是否在线段上（非顶点情况）
+        if (min(xi, xj) <= x <= max(xi, xj)) and (min(yi, yj) <= y <= max(yi, yj)):
+            cross = (x - xi) * (yj - yi) - (y - yi) * (xj - xi)
+            if cross == 0:
+                on_boundary = True
+                break
+
+        # 计算射线与边的交点（非水平边）
+        if (yi > y) != (yj > y):
+            slope = (xj - xi) / (yj - yi) if (yj - yi) != 0 else float("inf")
+            x_intersect = xi + (y - yi) * slope
+            if x <= x_intersect:
+                intersections += 1
+
+    if on_boundary:
+        return false
+    return intersections % 2 == 1  # 奇数为内部，返回True
+
+
+# 获取并集区域坐标点
+def get_contours(union_polygon):
+    if union_polygon.geom_type == "Polygon":
+        # 单一多边形
+        x, y = union_polygon.exterior.xy
+
+        contours = [(list(x)[i], list(y)[i]) for i in range(len(x))]
+        contours = np.array(contours, np.int32)
+    return contours
+
+
+# 筛选落在盒子二维重投影区域内的关键点信息
+def filter_kps_box(kps, contours):
+    # 存放筛选后的目标框
+    # new_boxes_data = []
+    # 存放筛选后的2d姿态点数据
+    # new_kps_data = []
+    # 存放筛选后的2d姿态置信度
+    # 遍历未筛选的目标框
+
+    x1, y1 = kps[0]
+    x2, y2 = kps[16]
+    # 保留目标框中心在范围内的坐标点
+    x_center = (x1 + x2) / 2
+    y_centet = (y1 + y2) / 2
+    if point_in_polygon([x1, y1], contours) and point_in_polygon([x2, y2], contours):
+        # if point_in_polygon([x_center, y_centet], contours) :
+        return true
+    else:
+        return false
+    # return new_kps_data

playground.py

@@ -65,8 +65,10 @@ from app.visualize.whole_body import visualize_whole_body
 NDArray: TypeAlias = np.ndarray
 
 # %%
-DATASET_PATH = Path("samples") / "04_02"
-AK_CAMERA_DATASET: ak.Array = ak.from_parquet(DATASET_PATH / "camera_params.parquet")
+CAMERA_PATH = Path(
+    "/home/admin/Documents/ActualTest_QuanCheng/camera_ex_params_1_2025_4_20/camera_params"
+)
+AK_CAMERA_DATASET: ak.Array = ak.from_parquet(CAMERA_PATH / "camera_params.parquet")
 DELTA_T_MIN = timedelta(milliseconds=10)
 display(AK_CAMERA_DATASET)
 
@@ -102,6 +104,13 @@ class ExternalCameraParams(TypedDict):
 
 
 # %%
+
+# %%
+DATASET_PATH = Path(
+    "/home/admin/Documents/ActualTest_QuanCheng/camera_ex_params_1_2025_4_20/detect_result/segement_1"
+)
+
+
 def read_dataset_by_port(port: int) -> ak.Array:
     P = DATASET_PATH / f"{port}.parquet"
     return ak.from_parquet(P)
@@ -110,6 +119,7 @@ def read_dataset_by_port(port: int) -> ak.Array:
 KEYPOINT_DATASET = {
     int(p): read_dataset_by_port(p) for p in ak.to_numpy(AK_CAMERA_DATASET["port"])
 }
+display(KEYPOINT_DATASET)
 
 
 # %%
@@ -184,6 +194,8 @@ def preprocess_keypoint_dataset(
             )
 
 
+# %%
+
 # %%
 DetectionGenerator: TypeAlias = Generator[Detection, None, None]
 
@@ -326,13 +338,31 @@ def homogeneous_to_euclidean(
 
 # %%
 FPS = 24
-image_gen_5600 = preprocess_keypoint_dataset(KEYPOINT_DATASET[5600], from_camera_params(AK_CAMERA_DATASET[AK_CAMERA_DATASET["port"] == 5600][0]), FPS, datetime(2024, 4, 2, 12, 0, 0))  # type: ignore
 image_gen_5601 = preprocess_keypoint_dataset(KEYPOINT_DATASET[5601], from_camera_params(AK_CAMERA_DATASET[AK_CAMERA_DATASET["port"] == 5601][0]), FPS, datetime(2024, 4, 2, 12, 0, 0))  # type: ignore
 image_gen_5602 = preprocess_keypoint_dataset(KEYPOINT_DATASET[5602], from_camera_params(AK_CAMERA_DATASET[AK_CAMERA_DATASET["port"] == 5602][0]), FPS, datetime(2024, 4, 2, 12, 0, 0))  # type: ignore
+image_gen_5603 = preprocess_keypoint_dataset(KEYPOINT_DATASET[5603], from_camera_params(AK_CAMERA_DATASET[AK_CAMERA_DATASET["port"] == 5603][0]), FPS, datetime(2024, 4, 2, 12, 0, 0))  # type: ignore
+image_gen_5604 = preprocess_keypoint_dataset(KEYPOINT_DATASET[5604], from_camera_params(AK_CAMERA_DATASET[AK_CAMERA_DATASET["port"] == 5604][0]), FPS, datetime(2024, 4, 2, 12, 0, 0))  # type: ignore
+image_gen_5605 = preprocess_keypoint_dataset(KEYPOINT_DATASET[5605], from_camera_params(AK_CAMERA_DATASET[AK_CAMERA_DATASET["port"] == 5605][0]), FPS, datetime(2024, 4, 2, 12, 0, 0))  # type: ignore
+image_gen_5606 = preprocess_keypoint_dataset(KEYPOINT_DATASET[5606], from_camera_params(AK_CAMERA_DATASET[AK_CAMERA_DATASET["port"] == 5606][0]), FPS, datetime(2024, 4, 2, 12, 0, 0))  # type: ignore
+image_gen_5607 = preprocess_keypoint_dataset(KEYPOINT_DATASET[5607], from_camera_params(AK_CAMERA_DATASET[AK_CAMERA_DATASET["port"] == 5607][0]), FPS, datetime(2024, 4, 2, 12, 0, 0))  # type: ignore
+image_gen_5608 = preprocess_keypoint_dataset(KEYPOINT_DATASET[5608], from_camera_params(AK_CAMERA_DATASET[AK_CAMERA_DATASET["port"] == 5608][0]), FPS, datetime(2024, 4, 2, 12, 0, 0))  # type: ignore
+image_gen_5609 = preprocess_keypoint_dataset(KEYPOINT_DATASET[5609], from_camera_params(AK_CAMERA_DATASET[AK_CAMERA_DATASET["port"] == 5609][0]), FPS, datetime(2024, 4, 2, 12, 0, 0))  # type: ignore
+
 
 display(1 / FPS)
 sync_gen = sync_batch_gen(
-    [image_gen_5600, image_gen_5601, image_gen_5602], timedelta(seconds=1 / FPS)
+    [
+        image_gen_5601,
+        # image_gen_5602,
+        # image_gen_5603,
+        image_gen_5604,
+        image_gen_5605,
+        image_gen_5606,
+        # image_gen_5607,
+        image_gen_5608,
+        image_gen_5609,
+    ],
+    timedelta(seconds=1 / FPS),
 )
 
 # %%
@@ -345,7 +375,7 @@ display(sorted_detections)
 display(
     list(
         map(
-            lambda x: {"timestamp": str(x.timestamp), "camera": x.camera.id},
+            lambda x: {"timestamp": str(x.timestamp), "camera": x.camera.id, "keypoint":x.keypoints.shape},
             sorted_detections,
         )
     )
@@ -413,6 +443,7 @@ for el in clusters_detections[0]:
 p = plt.imshow(im)
 display(p)
 
+
 # %%
 im_prime = np.zeros((HEIGHT, WIDTH, 3), dtype=np.uint8)
 for el in clusters_detections[1]:

temp_test_camera_ex_params.ipynb

@@ -0,0 +1,193 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "11cc2345",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import awkward as ak\n",
+    "import numpy as np\n",
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "84348d97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "CAMERA_INDEX ={\n",
+    "    2:\"5602\",\n",
+    "    4:\"5604\",\n",
+    "}\n",
+    "index = 4\n",
+    "CAMERA_PATH = Path(\"/home/admin/Documents/ActualTest_QuanCheng/camera_ex_params_1_2025_4_20/camera_params\")\n",
+    "camera_data = ak.from_parquet(CAMERA_PATH / CAMERA_INDEX[index]/ \"extrinsic.parquet\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1d771740",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre>[{rvec: [[-2.26], [0.0669], [-2.15]], tvec: [[0.166], ...]},\n",
+       " {rvec: [[2.07], [0.144], [2.21]], tvec: [[0.143], ...]},\n",
+       " {rvec: [[2.09], [0.0872], [2.25]], tvec: [[0.141], ...]},\n",
+       " {rvec: [[2.16], [0.172], [2.09]], tvec: [[0.162], ...]},\n",
+       " {rvec: [[2.15], [0.18], [2.09]], tvec: [[0.162], ...]},\n",
+       " {rvec: [[-2.22], [0.117], [-2.14]], tvec: [[0.162], ...]},\n",
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+       " {rvec: [[-2.26], [0.124], [-2.09]], tvec: [[0.171], ...]},\n",
+       " ...,\n",
+       " {rvec: [[-2.2], [0.0998], [-2.17]], tvec: [[0.158], ...]},\n",
+       " {rvec: [[-2.2], [0.0998], [-2.17]], tvec: [[0.158], ...]},\n",
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+       " {rvec: [[-2.3], [0.0733], [-2.1]], tvec: [[0.175], ...]},\n",
+       " {rvec: [[2.1], [0.16], [2.17]], tvec: [[0.149], ...]},\n",
+       " {rvec: [[2.1], [0.191], [2.13]], tvec: [[0.153], ...]},\n",
+       " {rvec: [[2.11], [0.196], [2.12]], tvec: [[0.154], ...]},\n",
+       " {rvec: [[2.19], [0.171], [2.08]], tvec: [[0.166], ...]},\n",
+       " {rvec: [[2.24], [0.0604], [2.12]], tvec: [[0.166], ...]}]\n",
+       "---------------------------------------------------------------------------\n",
+       "backend: cpu\n",
+       "nbytes: 10.1 kB\n",
+       "type: 90 * {\n",
+       "    rvec: var * var * float64,\n",
+       "    tvec: var * var * float64\n",
+       "}</pre>"
+      ],
+      "text/plain": [
+       "<Array [{rvec: [...], tvec: [...]}, ..., {...}] type='90 * {rvec: var * var...'>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(camera_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "59fde11b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = []\n",
+    "for element in camera_data:\n",
+    "    rvec = element[\"rvec\"]\n",
+    "    if rvec[0]<0:\n",
+    "        data.append({\"rvec\": rvec, \"tvec\": element[\"tvec\"]})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "4792cbc4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<pyarrow._parquet.FileMetaData object at 0x7799cbf62d40>\n",
+       "  created_by: parquet-cpp-arrow version 19.0.1\n",
+       "  num_columns: 2\n",
+       "  num_rows: 30\n",
+       "  num_row_groups: 1\n",
+       "  format_version: 2.6\n",
+       "  serialized_size: 0"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ak.to_parquet(ak.from_iter(data),\"/home/admin/Documents/ActualTest_QuanCheng/camera_ex_params_1_2025_4_20/camera_params/5604/re_extrinsic.parquet\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "8225ee33",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       " ...,\n",
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+       " {rvec: [[-2.21], [0.054], [-2.2]], tvec: [[0.157], ...]},\n",
+       " {rvec: [[-2.19], [0.0169], [-2.25]], tvec: [[0.151], ...]},\n",
+       " {rvec: [[-2.2], [0.0719], [-2.19]], tvec: [[0.157], ...]},\n",
+       " {rvec: [[-2.22], [0.0726], [-2.18]], tvec: [[0.161], ...]},\n",
+       " {rvec: [[-2.2], [0.0742], [-2.19]], tvec: [[0.158], ...]},\n",
+       " {rvec: [[-2.2], [0.0998], [-2.17]], tvec: [[0.158], ...]},\n",
+       " {rvec: [[-2.2], [0.0998], [-2.17]], tvec: [[0.158], ...]},\n",
+       " {rvec: [[-2.3], [0.0733], [-2.1]], tvec: [[0.175], ...]}]\n",
+       "---------------------------------------------------------------------------\n",
+       "backend: cpu\n",
+       "nbytes: 3.4 kB\n",
+       "type: 30 * {\n",
+       "    rvec: var * var * float64,\n",
+       "    tvec: var * var * float64\n",
+       "}</pre>"
+      ],
+      "text/plain": [
+       "<Array [{rvec: [...], tvec: [...]}, ..., {...}] type='30 * {rvec: var * var...'>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "temp_data = ak.from_parquet(\"/home/admin/Documents/ActualTest_QuanCheng/camera_ex_params_1_2025_4_20/camera_params/5604/re_extrinsic.parquet\")\n",
+    "display(temp_data)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}