diff --git a/evaluation/ablation/test-stream.ipynb b/evaluation/ablation/test-stream.ipynb index e4e3223..52f60b7 100644 --- a/evaluation/ablation/test-stream.ipynb +++ b/evaluation/ablation/test-stream.ipynb @@ -2,12 +2,58 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "569e4726-2856-4e5f-a220-e3bef1c110e1", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wed Oct 25 09:50:13 2023 \n", + "+---------------------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 530.30.02 Driver Version: 530.30.02 CUDA Version: 12.1 |\n", + "|-----------------------------------------+----------------------+----------------------+\n", + "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", + "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", + "| | | MIG M. |\n", + "|=========================================+======================+======================|\n", + "| 0 NVIDIA TITAN Xp On | 00000000:1A:00.0 Off | N/A |\n", + "| 29% 49C P2 60W / 250W| 1916MiB / 12288MiB | 0% Default |\n", + "| | | N/A |\n", + "+-----------------------------------------+----------------------+----------------------+\n", + "| 1 NVIDIA TITAN Xp On | 00000000:1B:00.0 Off | N/A |\n", + "| 23% 28C P8 10W / 250W| 1MiB / 12288MiB | 0% Default |\n", + "| | | N/A |\n", + "+-----------------------------------------+----------------------+----------------------+\n", + "| 2 NVIDIA TITAN Xp On | 00000000:3D:00.0 Off | N/A |\n", + "| 23% 23C P8 9W / 250W| 1MiB / 12288MiB | 0% Default |\n", + "| | | N/A |\n", + "+-----------------------------------------+----------------------+----------------------+\n", + "| 3 NVIDIA TITAN Xp On | 00000000:3E:00.0 Off | N/A |\n", + "| 23% 30C P8 10W / 250W| 1MiB / 12288MiB | 0% Default |\n", + "| | | N/A |\n", + "+-----------------------------------------+----------------------+----------------------+\n", + "| 4 NVIDIA TITAN Xp On | 00000000:88:00.0 Off | N/A |\n", + "| 23% 24C P8 9W / 250W| 1MiB / 12288MiB | 0% Default |\n", + "| | | N/A |\n", + "+-----------------------------------------+----------------------+----------------------+\n", + "| 5 NVIDIA TITAN Xp On | 00000000:89:00.0 Off | N/A |\n", + "| 23% 31C P8 9W / 250W| 1MiB / 12288MiB | 0% Default |\n", + "| | | N/A |\n", + "+-----------------------------------------+----------------------+----------------------+\n", + " \n", + "+---------------------------------------------------------------------------------------+\n", + "| Processes: |\n", + "| GPU GI CI PID Type Process name GPU Memory |\n", + "| ID ID Usage |\n", + "|=======================================================================================|\n", + "+---------------------------------------------------------------------------------------+\n" + ] + } + ], "source": [ "import subprocess\n", "import json\n", @@ -33,12 +79,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "7bcaf7b9-8b3f-4f2f-ad00-08b197795820", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "test freddie\n" + ] + } + ], "source": [ "hostname = socket.gethostname()\n", "test = hostname.split(\"-\")[-1]\n", @@ -47,12 +101,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "df967c40-baae-4473-8ab4-0ddfd630eb59", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/data/chanwutk/projects/spatialyze-ablation\n" + ] + } + ], "source": [ "def is_notebook() -> bool:\n", " try:\n", @@ -82,24 +144,55 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "edb4d993-d0a4-49d5-8b4d-1b3a62f66da4", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mobilitydb_chanwutk\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "process.wait()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "ddcd5926-911d-437d-b67f-6814d5eb7c0b", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CUDA is available.\n", + " > 0: NVIDIA TITAN Xp\n", + " 1: NVIDIA TITAN Xp\n", + " 2: NVIDIA TITAN Xp\n", + " 3: NVIDIA TITAN Xp\n", + " 4: NVIDIA TITAN Xp\n" + ] + } + ], "source": [ "from spatialyze.video_processor.camera_config import camera_config\n", "from spatialyze.video_processor.payload import Payload\n", @@ -110,7 +203,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "863b7bd6-7bc4-4658-bb06-043ba955aef3", "metadata": { "tags": [] @@ -131,7 +224,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "6ee9880e-27d0-47f3-a3dd-36b70d199d8a", "metadata": { "tags": [] @@ -139,7 +232,7 @@ "outputs": [], "source": [ "from spatialyze.video_processor.stages.detection_3d.from_detection_2d_and_road import FromDetection2DAndRoad as StageFromDetection2DAndRoad\n", - "from spatialyze.video_processor.stages.detection_3d.from_detection_2d_and_depth import FromDetection2DAndDepth\n", + "from spatialyze.video_processor.stages.detection_3d.from_detection_2d_and_depth import FromDetection2DAndDepth as StageFromDetection2DAndDepth\n", "\n", "from spatialyze.video_processor.stages.depth_estimation import DepthEstimation\n", "\n", @@ -149,7 +242,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "3c4bddca-a1ae-4806-be85-8cf397d01ec9", "metadata": { "tags": [] @@ -162,7 +255,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "6737b34b-d928-45aa-940e-b23a7a6e5eb0", "metadata": { "tags": [] @@ -181,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "e9087777-3c98-414a-8f00-eda64e8f8ffd", "metadata": {}, "outputs": [], @@ -190,14 +283,18 @@ "from spatialyze.video_processor.stream.decode_frame import DecodeFrame\n", "from spatialyze.video_processor.stream.prune_frames import PruneFrames\n", "from spatialyze.video_processor.stream.road_visibility_pruner import RoadVisibilityPruner\n", + "from spatialyze.video_processor.stream.mono_depth_estimator import MonoDepthEstimator\n", "from spatialyze.video_processor.stream.yolo import Yolo\n", + "from spatialyze.video_processor.stream.object_type_pruner import ObjectTypePruner\n", "from spatialyze.video_processor.stream.from_detection_2d_and_road import FromDetection2DAndRoad\n", + "from spatialyze.video_processor.stream.from_detection_2d_and_depth import FromDetection2DAndDepth\n", + "from spatialyze.video_processor.stream.exit_frame_sampler import ExitFrameSampler\n", "from spatialyze.video_processor.stream.strongsort import StrongSORT" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "59907886", "metadata": { "tags": [] @@ -210,7 +307,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "e650e6ca-9d7c-41e9-98ee-f682b399040f", "metadata": { "tags": [] @@ -224,7 +321,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "264c8190-89ee-472a-bd73-d553eb3e3278", "metadata": { "tags": [] @@ -242,12 +339,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "355b8977", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "/data/apperception-data/processed/nuscenes/full-dataset-v1.0/Trainval\n" + ] + } + ], "source": [ "NUSCENES_PROCESSED_DATA = \"NUSCENES_PROCESSED_DATA\"\n", "print(NUSCENES_PROCESSED_DATA in os.environ)\n", @@ -256,7 +362,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "00c1dd1e", "metadata": { "tags": [] @@ -272,12 +378,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "1f6981ac-60b4-43f4-9c3b-32a4e84e4aa1", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0523 0778 467\n" + ] + } + ], "source": [ "with open('./data/evaluation/video-samples/boston-seaport.txt', 'r') as f:\n", " sampled_scenes = f.read().split('\\n')\n", @@ -286,7 +400,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "fcb3599c-3807-4044-9636-45b2d94fe7e8", "metadata": { "tags": [] @@ -302,7 +416,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "ad97a25a-0356-4d7e-9096-f26c00d2d9d4", "metadata": { "tags": [] @@ -335,7 +449,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "5d4149a3-43b5-4531-90dd-31dd795bdaa1", "metadata": { "tags": [] @@ -357,7 +471,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "275836d5", "metadata": { "tags": [] @@ -421,20 +535,29 @@ " time_input = time.time() - start_input\n", "\n", " t1 = time.time()\n", - " inview = RoadVisibilityPruner(predicate=predicates[0], distance=50)\n", + " # inview = RoadVisibilityPruner(predicate=predicates[0], distance=50)\n", " decode = DecodeFrame()\n", " yolo = Yolo(decode)\n", - " d3ds = FromDetection2DAndRoad(yolo)\n", - " tracks = StrongSORT(d3ds, decode)\n", - " # output1 = tracks.execute(frames)\n", + " dets = ObjectTypePruner(yolo, types=['car'])\n", + " # depths = MonoDepthEstimator(decode)\n", + " # d3ds = FromDetection2DAndDepth(dets, depths)\n", + " d3ds = FromDetection2DAndRoad(dets)\n", + " efp = ExitFrameSampler(d3ds)\n", + " # d3ds = PruneFrames(efp, d3ds)\n", + " # tracks = StrongSORT(d3ds, decode)\n", + " output1 = efp.execute(frames)\n", "\n", " t2 = time.time()\n", "\n", " pipeline = Pipeline()\n", - " pipeline.add_filter(InView(predicate=predicates[0], distance=50))\n", - " # pipeline.add_filter(StageDecodeFrame())\n", - " # pipeline.add_filter(YoloDetection())\n", - " # pipeline.add_filter(StageFromDetection2DAndRoad())\n", + " # pipeline.add_filter(InView(predicate=predicates[0], distance=50))\n", + " pipeline.add_filter(StageDecodeFrame())\n", + " pipeline.add_filter(YoloDetection())\n", + " pipeline.add_filter(ObjectTypeFilter(types=['car']))\n", + " # pipeline.add_filter(DepthEstimation())\n", + " # pipeline.add_filter(StageFromDetection2DAndDepth())\n", + " pipeline.add_filter(StageFromDetection2DAndRoad())\n", + " pipeline.add_filter(DetectionEstimation())\n", " # pipeline.add_filter(StageStrongSORT())\n", " output2 = pipeline.run(Payload(frames))\n", "\n", @@ -442,9 +565,11 @@ "\n", " print('stream', t2 - t1)\n", " print('stage ', t3 - t2)\n", - " for o1, o2 in zip(inview.execute(frames), output2.keep):\n", + " # for o1, o2 in zip(output1, output2[StageFromDetection2DAndRoad]):\n", + " # assert np.array_equal(o1[0].cpu().numpy(), o2[0].cpu().numpy()), (o1[0], o2[0])\n", + " for idx, (o1, o2) in enumerate(zip(output1, output2.keep)):\n", " # print(type(o1), type(o2))\n", - " assert (o1 is True) == (o2 == 1), (o1, o2)\n", + " assert (o1 is True) == (o2 == 1), (o1, o2, idx)\n", "\n", " # for o1, o2 in zip(decode.execute(frames), output2[StageDecodeFrame]):\n", " # assert np.array_equal(o1, o2)\n", @@ -480,6 +605,7 @@ " # with open(p, \"w\") as f:\n", " # json.dump(metadata[name], f, cls=MetadataJSONEncoder,\n", " # indent=1)\n", + " break\n", "\n", "# times_rquery = []\n", "# for i, predicate in enumerate(predicates):\n", @@ -600,7 +726,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "98283938-b68f-4925-a5ef-eee7c6c46c65", "metadata": { "tags": [] @@ -642,10 +768,10 @@ "\n", " # 3D Detection\n", " if geo_depth:\n", - " pipeline.add_filter(FromDetection2DAndRoad())\n", + " pipeline.add_filter(StageFromDetection2DAndRoad())\n", " else:\n", " pipeline.add_filter(DepthEstimation())\n", - " pipeline.add_filter(FromDetection2DAndDepth())\n", + " pipeline.add_filter(StageFromDetection2DAndDepth())\n", "\n", " # Detection Estimation\n", " if detection_estimation:\n", @@ -672,7 +798,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "f4adca3d-7963-4dc6-bde1-d0ce107ae959", "metadata": { "tags": [] @@ -809,7 +935,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "653e586a-a98c-4c15-ac5a-17551b3155db", "metadata": { "tags": [] @@ -822,7 +948,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "48f7c558-dc6e-4b86-b447-a3ffac74c966", "metadata": { "tags": [] @@ -962,12 +1088,1011 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "811c4351-f2a7-478b-b264-69a3e8d75c69", "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------- 0 / 4 --- de -----------\n", + "Pipeline P2:\n", + " - DecodeFrame.ParallelDecodeFrame\n", + " - Detection2D.YoloDetection\n", + " - DepthEstimation\n", + " - Detection3D.FromDetection2DAndDepth\n", + " - DetectionEstimation\n", + " - Tracking2D.StrongSORT\n", + " - Tracking3D.FromTracking2DAndDetection3D\n", + "q2-de\n", + "# of total videos: 5100\n", + "# of filtered videos: 60\n", + "# of sliced videos: 60\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1b18b0a9b793464abbf8a0f2bd4720f7", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/60 [00:00\n", + "Traceback (most recent call last):\n", + " File \"/data/chanwutk/.local/share/mambaforge/envs/spatialyze-benchmark/lib/python3.10/site-packages/tqdm/std.py\", line 1144, in __del__\n", + " def __del__(self):\n", + "KeyboardInterrupt: \n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[25], line 14\u001b[0m\n\u001b[1;32m 11\u001b[0m retry \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m done \u001b[38;5;129;01mand\u001b[39;00m retry \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m5\u001b[39m:\n\u001b[1;32m 13\u001b[0m \u001b[38;5;66;03m# try:\u001b[39;00m\n\u001b[0;32m---> 14\u001b[0m \u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_test\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 15\u001b[0m done \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;66;03m# except Exception as e:\u001b[39;00m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;66;03m# print(type(e))\u001b[39;00m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;66;03m# print(e)\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;66;03m# with open(os.path.join(BENCHMARK_DIR, f'exception--bm{test}-t{_test}-r{retry}'), 'w') as f:\u001b[39;00m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;66;03m# f.write(str(e))\u001b[39;00m\n", + "Cell \u001b[0;32mIn[24], line 118\u001b[0m, in \u001b[0;36mrun\u001b[0;34m(__test)\u001b[0m\n\u001b[1;32m 116\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m p2\u001b[38;5;241m.\u001b[39mstages:\n\u001b[1;32m 117\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m -\u001b[39m\u001b[38;5;124m'\u001b[39m, s)\n\u001b[0;32m--> 118\u001b[0m \u001b[43mrun_benchmark\u001b[49m\u001b[43m(\u001b[49m\u001b[43mp2\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mq2-\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m__test\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mpred2\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpred2_notrack\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mignore_error\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 120\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPipeline P3,P4:\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 121\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m p34\u001b[38;5;241m.\u001b[39mstages:\n", + "Cell \u001b[0;32mIn[20], line 68\u001b[0m, in \u001b[0;36mrun_benchmark\u001b[0;34m(pipeline, filename, predicates, run, ignore_error)\u001b[0m\n\u001b[1;32m 65\u001b[0m efp \u001b[38;5;241m=\u001b[39m ExitFrameSampler(d3ds)\n\u001b[1;32m 66\u001b[0m \u001b[38;5;66;03m# d3ds = PruneFrames(efp, d3ds)\u001b[39;00m\n\u001b[1;32m 67\u001b[0m \u001b[38;5;66;03m# tracks = StrongSORT(d3ds, decode)\u001b[39;00m\n\u001b[0;32m---> 68\u001b[0m output1 \u001b[38;5;241m=\u001b[39m \u001b[43mefp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mframes\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 70\u001b[0m t2 \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m 72\u001b[0m pipeline \u001b[38;5;241m=\u001b[39m Pipeline()\n", + "File \u001b[0;32m/data/chanwutk/projects/spatialyze-ablation/spatialyze/video_processor/stream/stream.py:38\u001b[0m, in \u001b[0;36mStream.execute\u001b[0;34m(self, video)\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_initialize_stream(video)\n\u001b[1;32m 37\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_initialize_stream_progress()\n\u001b[0;32m---> 38\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvideo\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/data/chanwutk/projects/spatialyze-ablation/spatialyze/video_processor/stream/stream.py:48\u001b[0m, in \u001b[0;36mStream.stream\u001b[0;34m(self, video)\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_results) \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stream_progress[idx]:\n\u001b[0;32m---> 48\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_results\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_iter_stream\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 50\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_results[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stream_progress[idx]]\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m/data/chanwutk/projects/spatialyze-ablation/spatialyze/video_processor/stream/exit_frame_sampler.py:70\u001b[0m, in \u001b[0;36mExitFrameSampler._stream\u001b[0;34m(self, video)\u001b[0m\n\u001b[1;32m 68\u001b[0m det, _, dids \u001b[38;5;241m=\u001b[39m detection\n\u001b[1;32m 69\u001b[0m \u001b[38;5;66;03m# if new_car(detections, 5) <= i + 1:\u001b[39;00m\n\u001b[0;32m---> 70\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m--new \u001b[39m\u001b[38;5;124m'\u001b[39m, i, \u001b[43mnew_car\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdetections\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mmin\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mvideo\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m+\u001b[39m i)\n\u001b[1;32m 71\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m new_car(detections, \u001b[38;5;28mmin\u001b[39m(\u001b[38;5;241m5\u001b[39m, \u001b[38;5;28mlen\u001b[39m(video) \u001b[38;5;241m-\u001b[39m i \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m)) \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 72\u001b[0m \u001b[38;5;66;03m# do not map segment if cannot skip in the first place\u001b[39;00m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;66;03m# skipped_frame_num.append(i)\u001b[39;00m\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnew \u001b[39m\u001b[38;5;124m'\u001b[39m, i, new_car(detections, \u001b[38;5;28mmin\u001b[39m(\u001b[38;5;241m5\u001b[39m, \u001b[38;5;28mlen\u001b[39m(video) \u001b[38;5;241m-\u001b[39m i \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m)) \u001b[38;5;241m+\u001b[39m i)\n", + "File \u001b[0;32m/data/chanwutk/projects/spatialyze-ablation/spatialyze/video_processor/stream/exit_frame_sampler.py:181\u001b[0m, in \u001b[0;36mnew_car\u001b[0;34m(detections, nxt)\u001b[0m\n\u001b[1;32m 179\u001b[0m len_det \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(detection[\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m 180\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m1\u001b[39m, nxt \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m):\n\u001b[0;32m--> 181\u001b[0m det \u001b[38;5;241m=\u001b[39m \u001b[43mdetections\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m 182\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(det, Skip)\n\u001b[1;32m 183\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m det \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m/data/chanwutk/projects/spatialyze-ablation/spatialyze/video_processor/stream/exit_frame_sampler.py:158\u001b[0m, in \u001b[0;36mFutureIterator.__getitem__\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m 156\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 157\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmem) \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39midx \u001b[38;5;241m+\u001b[39m item:\n\u001b[0;32m--> 158\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmem\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mit\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 159\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmem[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39midx \u001b[38;5;241m+\u001b[39m item]\n\u001b[1;32m 160\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n", + "File \u001b[0;32m/data/chanwutk/projects/spatialyze-ablation/spatialyze/video_processor/stream/stream.py:48\u001b[0m, in \u001b[0;36mStream.stream\u001b[0;34m(self, video)\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_results) \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stream_progress[idx]:\n\u001b[0;32m---> 48\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_results\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_iter_stream\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 50\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_results[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stream_progress[idx]]\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m/data/chanwutk/projects/spatialyze-ablation/spatialyze/video_processor/stream/from_detection_2d_and_road.py:21\u001b[0m, in \u001b[0;36mFromDetection2DAndRoad._stream\u001b[0;34m(self, video)\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_stream\u001b[39m(\u001b[38;5;28mself\u001b[39m, video: Video):\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mno_grad():\n\u001b[0;32m---> 21\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m d2d, frame \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdetection2ds\u001b[38;5;241m.\u001b[39mstream(video), video\u001b[38;5;241m.\u001b[39mcamera_configs):\n\u001b[1;32m 22\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(d2d, Skip):\n\u001b[1;32m 23\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m skip\n", + "File \u001b[0;32m/data/chanwutk/projects/spatialyze-ablation/spatialyze/video_processor/stream/stream.py:48\u001b[0m, in \u001b[0;36mStream.stream\u001b[0;34m(self, video)\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_results) \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stream_progress[idx]:\n\u001b[0;32m---> 48\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_results\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_iter_stream\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 50\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_results[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stream_progress[idx]]\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m/data/chanwutk/projects/spatialyze-ablation/spatialyze/video_processor/stream/object_type_pruner.py:22\u001b[0m, in \u001b[0;36mObjectTypePruner._stream\u001b[0;34m(self, video)\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_stream\u001b[39m(\u001b[38;5;28mself\u001b[39m, video: Video):\n\u001b[1;32m 20\u001b[0m type_indices_to_keep: \u001b[38;5;28mset\u001b[39m[\u001b[38;5;28mint\u001b[39m] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m---> 22\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m detection_2d \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdetections\u001b[38;5;241m.\u001b[39mstream(video):\n\u001b[1;32m 23\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(detection_2d, Skip):\n\u001b[1;32m 24\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m skip\n", + "File \u001b[0;32m/data/chanwutk/projects/spatialyze-ablation/spatialyze/video_processor/stream/stream.py:48\u001b[0m, in \u001b[0;36mStream.stream\u001b[0;34m(self, video)\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_results) \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stream_progress[idx]:\n\u001b[0;32m---> 48\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_results\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_iter_stream\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 50\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_results[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stream_progress[idx]]\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m/data/chanwutk/projects/spatialyze-ablation/spatialyze/video_processor/stream/yolo.py:74\u001b[0m, in \u001b[0;36mYolo._stream\u001b[0;34m(self, video)\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mimgsz, \u001b[38;5;28mlist\u001b[39m), \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mimgsz)\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39mwarmup(imgsz\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mimgsz)) \u001b[38;5;66;03m# warmup\u001b[39;00m\n\u001b[0;32m---> 74\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m frame_idx, im0 \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mframes\u001b[38;5;241m.\u001b[39mstream(video)):\n\u001b[1;32m 75\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(im0, Skip):\n\u001b[1;32m 76\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m skip\n", + "File \u001b[0;32m/data/chanwutk/projects/spatialyze-ablation/spatialyze/video_processor/stream/stream.py:48\u001b[0m, in \u001b[0;36mStream.stream\u001b[0;34m(self, video)\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_results) \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stream_progress[idx]:\n\u001b[0;32m---> 48\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_results\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_iter_stream\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 50\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_results[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stream_progress[idx]]\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m/data/chanwutk/projects/spatialyze-ablation/spatialyze/video_processor/stream/decode_frame.py:12\u001b[0m, in \u001b[0;36mDecodeFrame._stream\u001b[0;34m(self, video)\u001b[0m\n\u001b[1;32m 10\u001b[0m cap \u001b[38;5;241m=\u001b[39m cv2\u001b[38;5;241m.\u001b[39mVideoCapture(video\u001b[38;5;241m.\u001b[39mvideofile)\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m cap\u001b[38;5;241m.\u001b[39misOpened():\n\u001b[0;32m---> 12\u001b[0m ret, frame \u001b[38;5;241m=\u001b[39m \u001b[43mcap\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m ret:\n\u001b[1;32m 14\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], "source": [ "tests = ['optde', 'de', 'noopt', 'inview', 'objectfilter', 'geo', 'opt']\n", "tests = ['de', 'noopt', 'inview', 'objectfilter']\n", @@ -1031,22 +2156,6 @@ "if not is_notebook():\n", " subprocess.Popen('sudo shutdown -h now', shell=True)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "64ad6fa9-3d74-4dc1-989d-08dfda7f3489", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7ba68010-cbf1-443d-9418-d94ea121392b", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/poetry.lock b/poetry.lock index cbb5818..ccf2161 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,31 +1,5 @@ # This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. 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"21.3.0" -description = "The secure Argon2 password hashing algorithm." +version = "23.1.0" +description = "Argon2 for Python" optional = false -python-versions = ">=3.6" +python-versions = ">=3.7" files = [ - {file = "argon2-cffi-21.3.0.tar.gz", hash = "sha256:d384164d944190a7dd7ef22c6aa3ff197da12962bd04b17f64d4e93d934dba5b"}, - {file = "argon2_cffi-21.3.0-py3-none-any.whl", hash = "sha256:8c976986f2c5c0e5000919e6de187906cfd81fb1c72bf9d88c01177e77da7f80"}, + {file = "argon2_cffi-23.1.0-py3-none-any.whl", hash = "sha256:c670642b78ba29641818ab2e68bd4e6a78ba53b7eff7b4c3815ae16abf91c7ea"}, + {file = "argon2_cffi-23.1.0.tar.gz", hash = "sha256:879c3e79a2729ce768ebb7d36d4609e3a78a4ca2ec3a9f12286ca057e3d0db08"}, ] [package.dependencies] argon2-cffi-bindings = "*" [package.extras] -dev = ["cogapp", "coverage[toml] (>=5.0.2)", "furo", "hypothesis", "pre-commit", "pytest", "sphinx", "sphinx-notfound-page", "tomli"] -docs = ["furo", "sphinx", "sphinx-notfound-page"] -tests = ["coverage[toml] 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--- a/pyproject.toml +++ b/pyproject.toml @@ -26,7 +26,7 @@ matplotlib = "^3.5.2" scipy = "^1.4.1" gdown = "^4.7.1" ultralytics = "^8.0.148" -notebook = "^6.5.5" +notebook = "^7.0.6" [tool.poetry.group.test.dependencies] pytest = "^7.4.0" @@ -41,11 +41,11 @@ flake8 = "^5.0.4" pre-commit = "^3.2.2" # Notebook -jupyterlab = "^3.6.2" -ipywidgets = "^8.0.6" -jupyterlab-lsp = "^4.0.1" +jupyterlab = "^4.0.7" +ipywidgets = "^8.1.1" +jupyterlab-lsp = "^5.0.0" python-lsp-server = {extras = ["all"], version = "^1.7.1"} -jupyterlab-vim = "^0.16.0" +jupyterlab-vim = "^4.1.0" nbdime = "^3.2.1" altair = "^4.2.2" diff --git a/spatialyze/video_processor/stages/detection_estimation/__init__.py b/spatialyze/video_processor/stages/detection_estimation/__init__.py index e137e65..55806fd 100644 --- a/spatialyze/video_processor/stages/detection_estimation/__init__.py +++ b/spatialyze/video_processor/stages/detection_estimation/__init__.py @@ -60,7 +60,7 @@ def _run(self, payload: "Payload"): return keep, {DetectionEstimation.classname(): [[]] * len(keep)} ego_views = get_ego_views(payload.video) - ego_views = [shapely.wkb.loads(view.to_ewkb(), hex=True) for view in ego_views] + # ego_views: list[shapely.geometry.Polygon] = [shapely.wkb.loads(view.to_ewkb(), hex=True) for view in ego_views] skipped_frame_num = [] next_frame_num = 0 @@ -78,14 +78,17 @@ def _run(self, payload: "Payload"): if i != next_frame_num: skipped_frame_num.append(i) metadata.append([]) + print('skip ', i, next_frame_num) continue next_frame_num = i + 1 det, _, dids = dets[i] + print('--new ', i, new_car(dets, i, i + 5)) if new_car(dets, i, i + 5) <= i + 1: # will not map segment if cannot skip in the first place metadata.append([]) + print('new ', i, new_car(dets, i, i + 5)) continue start_detection_time = time.time() @@ -97,13 +100,15 @@ def _run(self, payload: "Payload"): (time.time() - start_detection_time, len(det), len(all_detection_info), times) ) - all_detection_info_pruned, det = prune_detection( - all_detection_info, det, self.predicates - ) + # all_detection_info_pruned, det = prune_detection( + # all_detection_info, det, self.predicates + # ) + all_detection_info_pruned = all_detection_info if len(det) == 0 or len(all_detection_info_pruned) == 0: # skipped_frame_num.append(i) metadata.append([]) + print('0 ', i) continue start_generate_sample_plan = time.time() @@ -114,6 +119,7 @@ def _run(self, payload: "Payload"): next_frame_num = next_sample_plan.get_next_frame_num() next_frame_num = new_car(dets, i, next_frame_num) logger.info(f"founded next_frame_num {next_frame_num}") + print('next ', i) metadata.append(all_detection_info) next_action_type = next_sample_plan.get_action_type() @@ -158,7 +164,7 @@ def new_car(dets: "list[D2DMetadatum]", cur: "int", nxt: "int"): future_det = dets[j][0] if len(future_det) > len_det: return j - return nxt + return min(nxt, len(dets) - 1) def objects_count_change(dets: "list[D2DMetadatum]", cur: "int", nxt: "int"): @@ -173,7 +179,7 @@ def objects_count_change(dets: "list[D2DMetadatum]", cur: "int", nxt: "int"): def get_ego_views(video: "Video") -> "list[postgis.Polygon]": - indices, view_areas = get_views(video, distance=100, skip=False) + indices, view_areas = get_views(video, distance=100) views_raw = database.execute( sql.SQL( """ @@ -220,7 +226,7 @@ def generate_sample_plan_once( next_frame_num: "int", ego_views: "list[postgis.Polygon]", all_detection_info: "list[DetectionInfo] | None" = None, - fps: "int" = 13, + fps: "float" = 13, ) -> "tuple[SamplePlan, None]": assert all_detection_info is not None next_sample_plan = generate_sample_plan( @@ -234,7 +240,7 @@ def construct_estimated_all_detection_info( detection_ids: "list[DetectionId]", ego_config: "CameraConfig", ego_trajectory: "list[trajectory_3d]", -) -> "list[DetectionInfo]": +) -> "tuple[list[DetectionInfo], float]": _times = time.time() all_detections = [] for det, did in zip(detections, detection_ids): diff --git a/spatialyze/video_processor/stages/detection_estimation/detection_estimation.py b/spatialyze/video_processor/stages/detection_estimation/detection_estimation.py index 7812e4a..cc58121 100644 --- a/spatialyze/video_processor/stages/detection_estimation/detection_estimation.py +++ b/spatialyze/video_processor/stages/detection_estimation/detection_estimation.py @@ -152,7 +152,7 @@ class SamplePlan: next_frame_num: int all_detection_info: "list[DetectionInfo]" ego_views: "list[postgis.Polygon]" - fps: int = 12 + fps: float = 12 current_priority: "float | None" = None action: "Action | None" = None @@ -259,7 +259,7 @@ def construct_all_detection_info( ego_config: "CameraConfig", ego_trajectory: "list[trajectory_3d]", all_detections: "list[obj_detection]", -): +) -> tuple[list[DetectionInfo], list[float]]: all_detection_info: "list[DetectionInfo]" = [] if len(all_detections) == 0: return all_detection_info, [] @@ -267,7 +267,7 @@ def construct_all_detection_info( # ego_road_polygon_info = get_largest_polygon_containing_point(ego_config) detections_polygon_mapping, times = get_detection_polygon_mapping(all_detections, ego_config) if len(detections_polygon_mapping) == 0: - return [], times + return all_detection_info, times # assert len(all_detections) == len(detections_polygon_mapping) for detection in all_detections: @@ -298,7 +298,7 @@ def generate_sample_plan( all_detection_info: "list[DetectionInfo]", ego_views: "list[postgis.Polygon]", view_distance: float, - fps: int = 12, + fps: float = 12, ): ### the object detection with higher priority doesn't necessarily get sampled first, # it also based on the sample plan diff --git a/spatialyze/video_processor/stages/detection_estimation/segment_mapping.py b/spatialyze/video_processor/stages/detection_estimation/segment_mapping.py index 187b447..6be4b04 100644 --- a/spatialyze/video_processor/stages/detection_estimation/segment_mapping.py +++ b/spatialyze/video_processor/stages/detection_estimation/segment_mapping.py @@ -186,7 +186,7 @@ def get_detection_polygon_mapping(detections: "list[obj_detection]", ego_config: Given a list of detections, return a list of RoadSegmentWithHeading """ # start_time = time.time() - times = [] + times: list[float] = [] times.append(time.time()) results = map_detections_to_segments(detections, ego_config) times.append(time.time()) @@ -202,13 +202,13 @@ def get_detection_polygon_mapping(detections: "list[obj_detection]", ego_config: times.append(time.time()) mapped_polygons = reformat_return_polygon(mapped_polygons) times.append(time.time()) + mapped_road_polygon_info: "dict[DetectionId, RoadPolygonInfo]" = {} if any(p.road_type == "intersection" for p in mapped_polygons): - return {}, times + return mapped_road_polygon_info, times times.append(time.time()) fov_lines = get_fov_lines(ego_config) times.append(time.time()) - mapped_road_polygon_info: "dict[DetectionId, RoadPolygonInfo]" = {} for order_id, road_polygon in list(zip(order_ids, mapped_polygons)): frame_idx = detections[0].detection_id.frame_idx det_id = DetectionId(frame_idx=frame_idx, obj_order=order_id) @@ -221,7 +221,7 @@ def get_detection_polygon_mapping(detections: "list[obj_detection]", ego_config: # assert all(isinstance(line, sg.LineString) for line in segmentlines) - p = swkb.loads(road_polygon.to_ewkb(), hex=True) + p = swkb.loads(roadpolygon.to_ewkb(), hex=True) assert isinstance(p, sg.Polygon) XYs: "Tuple[array.array[float], array.array[float]]" = p.exterior.xy assert isinstance(XYs, tuple) diff --git a/spatialyze/video_processor/stages/detection_estimation/utils.py b/spatialyze/video_processor/stages/detection_estimation/utils.py index 896a791..fe43f00 100644 --- a/spatialyze/video_processor/stages/detection_estimation/utils.py +++ b/spatialyze/video_processor/stages/detection_estimation/utils.py @@ -317,13 +317,14 @@ def get_car_exits_view_frame_num( start_frame_num = detection_info.detection_id.frame_idx car_speed = max_car_speed(road_type) car_heading += 90 - while detection_info.detection_id.frame_idx + 1 < max_frame_num: - next_frame_num = detection_info.detection_id.frame_idx + 1 + frame_idx = detection_info.detection_id.frame_idx + while frame_idx + 1 < max_frame_num: + next_frame_num = frame_idx + 1 next_ego_view = ego_views[next_frame_num] next_ego_view = shapely.wkb.loads(next_ego_view.to_ewkb(), hex=True) duration = (next_frame_num - start_frame_num) / fps next_car_loc = car_move(car_loc, car_heading, car_speed, duration) if not next_ego_view.contains(shapely.geometry.Point(next_car_loc[:2])): - return max(detection_info.detection_id.frame_idx, start_frame_num + 1) - detection_info.detection_id.frame_idx = next_frame_num + return max(frame_idx, start_frame_num + 1) + frame_idx = next_frame_num return max_frame_num diff --git a/spatialyze/video_processor/stream/exit_frame_sampler.py b/spatialyze/video_processor/stream/exit_frame_sampler.py index eb9045b..f9e56e5 100644 --- a/spatialyze/video_processor/stream/exit_frame_sampler.py +++ b/spatialyze/video_processor/stream/exit_frame_sampler.py @@ -36,7 +36,7 @@ def _stream(self, video: Video): return self.detections.stream(video) ego_views = get_ego_views(video) - ego_views = [shapely.wkb.loads(view.to_ewkb(), hex=True) for view in ego_views] + # ego_views = [shapely.wkb.loads(view.to_ewkb(), hex=True) for view in ego_views] skipped_frame_num = [] next_frame_num = 0 @@ -51,17 +51,16 @@ def _stream(self, video: Video): yield True continue - if i != next_frame_num: - yield skip + if i != next_frame_num or isinstance(detection, Skip): + yield False continue next_frame_num = i + 1 det, _, dids = detection - if new_car(detections, 5) <= i + 1: + if new_car(detections, min(5, len(video) - i - 1)) <= 1: # do not map segment if cannot skip in the first place - skipped_frame_num.append(i) - yield skip + yield True continue start_detection_time = time.time() @@ -79,9 +78,7 @@ def _stream(self, video: Video): all_detection_info_pruned = all_detection_info if len(det) == 0 or len(all_detection_info_pruned) == 0: - skipped_frame_num.append(i) - # metadata.append([]) - yield skip + yield True continue start_generate_sample_plan = time.time() @@ -90,9 +87,9 @@ def _stream(self, video: Video): ) total_sample_plan_time.append(time.time() - start_generate_sample_plan) next_frame_num = next_sample_plan.get_next_frame_num() - next_frame_num = new_car(detections, next_frame_num - i) + next_frame_num = i + new_car(detections, min(next_frame_num, len(video) - 1) - i) logger.info(f"founded next_frame_num {next_frame_num}") - yield detection + yield True next_action_type = next_sample_plan.get_action_type() if next_action_type not in action_type_counts: @@ -128,7 +125,7 @@ class FutureIterator(Generic[T], Iterator[T]): def __init__(self, it: Iterable[T]): self.it = iter(it) self.idx = -1 - self.mem = [] + self.mem: list[T | None] = [] def __next__(self): if self.idx >= 0: @@ -144,13 +141,19 @@ def __getitem__(self, item: int): self.mem.append(next(self.it)) return self.mem[self.idx + item] except StopIteration: + if item == 0: + raise StopIteration return None def new_car(detections: FutureIterator[Detection3D | Skip], nxt: int): - len_det = len(detections[0][0]) + detection = detections[0] + assert detection is not None + assert not isinstance(detection, Skip) + len_det = len(detection[0]) for i in range(1, nxt + 1): det = detections[i] + assert not isinstance(det, Skip) if det is None: return i - 1 future_det = det[0] diff --git a/spatialyze/video_processor/stream/object_type_pruner.py b/spatialyze/video_processor/stream/object_type_pruner.py index 6f9a50a..bbea7dd 100644 --- a/spatialyze/video_processor/stream/object_type_pruner.py +++ b/spatialyze/video_processor/stream/object_type_pruner.py @@ -1,7 +1,7 @@ from ...predicate import PredicateNode from ..stages.detection_2d.object_type_filter import ObjectTypeFilter from ..video import Video -from .data_types import Detection2D, Skip +from .data_types import Detection2D, Skip, skip from .stream import Stream @@ -21,7 +21,7 @@ def _stream(self, video: Video): for detection_2d in self.detections.stream(video): if isinstance(detection_2d, Skip): - yield Skip() + yield skip continue det, class_mapping, ids = detection_2d