Merge branch 'dev' into stage

.woodpecker/01-test.yml

@@ -1,39 +0,0 @@
-# Manual-trigger only. Validation fixtures (sample videos + expected-detections
-# baseline) are not yet authored; running the e2e on push would produce red
-# builds until that data lands. Flip `event: [manual]` back to
-# `event: [push, pull_request, manual]` once validation fixtures are in place,
-# and add `depends_on: [01-test]` to 02-build-push.yml.
-#
-# Tracking: see plan "CI Auto Tests Epic", story S3b — blocked by e2e validation data.
-
-when:
-  event: [manual]
-  branch: [dev, stage, main]
-
-labels:
-  platform: arm64
-
-steps:
-  - name: e2e
-    image: docker:24
-    environment:
-      COMPOSE_PROJECT_NAME: detections-e2e
-    commands:
-      - cd e2e
-      - mkdir -p results logs
-      - docker compose -f docker-compose.test.yml --profile cpu up --build --abort-on-container-exit --exit-code-from e2e-runner
-    volumes:
-      - /var/run/docker.sock:/var/run/docker.sock
-
-  - name: e2e-report
-    image: docker:24
-    when:
-      status: [success, failure]
-    environment:
-      COMPOSE_PROJECT_NAME: detections-e2e
-    commands:
-      - cd e2e
-      - docker compose -f docker-compose.test.yml --profile cpu down -v || true
-      - test -f results/report.csv && cat results/report.csv || echo "no report"
-    volumes:
-      - /var/run/docker.sock:/var/run/docker.sock

.woodpecker/build-arm.yml

@@ -1,27 +1,9 @@
-# No `depends_on: [01-test]` clause yet — the test workflow is manual-only
-# while validation fixtures are being authored (see 01-test.yml header comment).
-# When 01-test flips to push/pull_request triggers, add `depends_on: [01-test]`
-# here so builds are gated on a green test run.
-
 when:
   event: [push, manual]
   branch: [dev, stage, main]
 
-# Multi-arch matrix. detections is the only repo today with split per-arch
-# Dockerfiles (Jetson uses Dockerfile.jetson with TensorRT/CUDA on L4T;
-# amd64 uses the plain Dockerfile). Adding amd64 = uncommenting the second
-# entry once an amd64 agent and base image are in place.
-matrix:
-  include:
-    - PLATFORM: arm64
-      TAG_SUFFIX: arm
-      DOCKERFILE: Dockerfile.jetson
-    # - PLATFORM: amd64
-    #   TAG_SUFFIX: amd
-    #   DOCKERFILE: Dockerfile
-
 labels:
-  platform: ${PLATFORM}
+  platform: arm64
 
 steps:
   - name: build-push
@@ -35,10 +17,10 @@ steps:
         from_secret: registry_token
     commands:
       - echo "$REGISTRY_TOKEN" | docker login "$REGISTRY_HOST" -u "$REGISTRY_USER" --password-stdin
-      - export TAG=${CI_COMMIT_BRANCH}-${TAG_SUFFIX}
+      - export TAG=${CI_COMMIT_BRANCH}-arm
       - export BUILD_DATE=$(date -u +%Y-%m-%dT%H:%M:%SZ)
       - |
-        docker build -f ${DOCKERFILE} \
+        docker build -f Dockerfile.jetson \
           --build-arg CI_COMMIT_SHA=$CI_COMMIT_SHA \
           --label org.opencontainers.image.revision=$CI_COMMIT_SHA \
           --label org.opencontainers.image.created=$BUILD_DATE \

.woodpecker/e2e-smoke-jetson.yml

@@ -1,15 +0,0 @@
-when:
-  event: [manual]
-
-labels:
-  platform: arm64
-
-steps:
-  - name: e2e-smoke-jetson
-    image: docker
-    commands:
-      - apk add --no-cache bash docker-cli-compose
-      - cd e2e
-      - E2E_PROFILE=jetson bash run_test.sh tests/test_health_engine.py
-    volumes:
-      - /var/run/docker.sock:/var/run/docker.sock

_docs/00_problem/acceptance_criteria.md

@@ -5,7 +5,7 @@
 - Detections with confidence below `probability_threshold` (default: 0.25) are filtered out.
 - Overlapping detections with containment ratio > `tracking_intersection_threshold` (default: 0.6) are deduplicated, keeping the higher-confidence detection.
 - Tile duplicate detections are identified when all bounding box coordinates differ by less than 0.01 (TILE_DUPLICATE_CONFIDENCE_THRESHOLD).
-- Physical size filtering: detections exceeding `max_object_size_meters` for their class (defined in classes.json, range 2–20 meters) are removed when ground sampling distance can be computed from camera settings.
+- Physical size filtering: detections exceeding `max_object_size_meters` for their class (defined in classes.json, range 2–20 meters) are removed.
 
 ## Video Processing
 
@@ -17,7 +17,7 @@
 
 - Images ≤ 1.5× model dimensions (1280×1280): processed as single frame.
 - Larger images: tiled based on ground sampling distance. Tile physical size: 25 meters (METERS_IN_TILE). Tile overlap: `big_image_tile_overlap_percent` (default: 20%).
-- GSD calculation: `sensor_width * altitude / (focal_length * image_width)` when `altitude` is provided.
+- GSD calculation: `sensor_width * altitude / (focal_length * image_width)`.
 
 ## API
 

_docs/00_problem/input_data/data_parameters.md

@@ -36,7 +36,7 @@ Media path is resolved from the Annotations service via `GET /api/media/{media_i
 | tracking_intersection_threshold | float | 0.6 | Overlap ratio for NMS deduplication |
 | model_batch_size | int | 8 | Inference batch size |
 | big_image_tile_overlap_percent | int | 20 | Tile overlap for large images (0-100%) |
-| altitude | float | optional | Camera altitude in meters. When omitted, GSD-based size filtering and image tiling are skipped. |
+| altitude | float | 400 | Camera altitude in meters |
 | focal_length | float | 24 | Camera focal length in mm |
 | sensor_width | float | 23.5 | Camera sensor width in mm |
 

_docs/00_problem/input_data/expected_results/image_different_types_expected.csv

@@ -1,3 +1 @@
 center_x,center_y,width,height,label,confidence_min
-0.104737,0.375955,0.154999,0.144996,Truck,0.73
-0.576021,0.728599,0.1495,0.142923,Truck,0.83

_docs/00_problem/input_data/expected_results/image_large_expected.csv

@@ -1,4 +1 @@
 center_x,center_y,width,height,label,confidence_min
-0.214701,0.770089,0.274945,0.28434,ArmorVehicle,0.73
-0.588816,0.581807,0.269273,0.209632,ArmorVehicle,0.49
-0.314031,0.56274,0.039824,0.060367,MilitaryMan,0.32

_docs/00_problem/input_data/expected_results/image_small_expected.csv

@@ -1,2 +1 @@
 center_x,center_y,width,height,label,confidence_min
-0.465599,0.20807,0.137469,0.194655,ArmorVehicle,0.77

_docs/00_problem/input_data/expected_results/results_report.md

@@ -37,18 +37,21 @@ For videos, the additional field:
 ### Images
 
 | # | Input File | Description | Expected Result File | Expected Detection Count | Notes |
-|---|------------|-------------|---------------------|---|-------|
+|---|------------|-------------|---------------------|-------------------------|-------|
-| 1 | `image_small.jpg`| 1280×720 aerial, contains detectable objects| `image_small_expected.csv`| 1 | Primary test image for single-frame detection|
+| 1 | `image_small.jpg` | 1280×720 aerial, contains detectable objects | `image_small_expected.csv` | ? | Primary test image for single-frame detection |
-| 2 | `image_large.JPG`| 6252×4168 aerial, triggers GSD-based tiling| `image_large_expected.csv`| 3 | Coordinates normalized to full image (not tile)|
+| 2 | `image_large.JPG` | 6252×4168 aerial, triggers GSD-based tiling | `image_large_expected.csv` | ? | Coordinates normalized to full image (not tile) |
-| 3 | `image_different_types.jpg`| 900×1600, varied object classes| `image_different_types_expected.csv`| 2 | Must contain multiple distinct class labels|
+| 3 | `image_dense01.jpg` | 1280×720 dense scene, many clustered objects | `image_dense01_expected.csv` | ? | Used for dedup and max-detection-cap tests |
-| 4 | `image_empty_scene.jpg`| 1920×1080, no detectable objects| `image_empty_scene_expected.csv`| 0 | CSV has headers only — zero detections expected|
+| 4 | `image_dense02.jpg` | 1920×1080 dense scene variant | `image_dense02_expected.csv` | ? | Borderline tiling, dedup variant |
+| 5 | `image_different_types.jpg` | 900×1600, varied object classes | `image_different_types_expected.csv` | ? | Must contain multiple distinct class labels |
+| 6 | `image_empty_scene.jpg` | 1920×1080, no detectable objects | `image_empty_scene_expected.csv` | 0 | CSV has headers only — zero detections expected |
 
 ### Videos
 
 | # | Input File | Description | Expected Result File | Notes |
 |---|------------|-------------|---------------------|-------|
-| 5 | `video_test01.mp4` | Standard test video | `video_test01_expected.csv` | Primary async/SSE/video test. List key-frame detections. |
+| 7 | `video_test01.mp4` | Standard test video | `video_test01_expected.csv` | Primary async/SSE/video test. List key-frame detections. |
-| 6 | `video_1_faststart.mp4` | Faststart video variant | `video_1_faststart_expected.csv` | Streaming compatibility variant. Separate long-video overflow fixture is not currently present in local fixtures. |
+| 8 | `video_1.mp4` | Video variant | `video_1_expected.csv` | Secondary local fixture for resilience and concurrent-style validation. |
+| 9 | `video_1_faststart.mp4` | Faststart video variant | `video_1_faststart_expected.csv` | Streaming compatibility variant. Separate long-video overflow fixture is not currently present in local fixtures. |
 
 ## How to Fill
 

_docs/00_problem/input_data/expected_results/video_1_faststart_expected.csv

@@ -1,81 +1 @@
 time_sec,center_x,center_y,width,height,label,confidence_min
-0,0.289802,0.512695,0.077206,0.072666,ArmorVehicle,0.89
-2,0.653329,0.376178,0.078665,0.081241,ArmorVehicle,0.89
-4,0.197519,0.416036,0.067097,0.083232,ArmorVehicle,0.83
-6,0.276864,0.60931,0.060284,0.066828,ArmorVehicle,0.8
-8,0.379446,0.434878,0.071124,0.077504,ArmorVehicle,0.55
-10,0.753483,0.198423,0.057173,0.070441,ArmorVehicle,0.72
-14,0.956159,0.68494,0.075992,0.034521,Trenches,0.62
-30,0.343423,0.763509,0.098184,0.079214,TyreTracks,0.82
-32,0.438461,0.687812,0.09794,0.07654,TyreTracks,0.85
-41,0.700711,0.590761,0.10644,0.088566,TyreTracks,0.74
-47,0.830646,0.762364,0.108269,0.091804,TyreTracks,0.37
-47,0.698529,0.00825,0.022697,0.01672,Vehicle,0.34
-49,0.349817,0.663337,0.075551,0.061795,TyreTracks,0.77
-67,0.846456,0.527966,0.03052,0.058466,Trenches,0.32
-69,0.68478,0.514788,0.025761,0.053171,Trenches,0.46
-71,0.368416,0.460942,0.030577,0.050027,Trenches,0.78
-73,0.177122,0.455578,0.031931,0.048597,Trenches,0.36
-87,0.128406,0.138269,0.040148,0.032909,Smoke,0.56
-90,0.118965,0.089119,0.037023,0.033037,Smoke,0.32
-95,0.570426,0.373717,0.031503,0.021361,Trenches,0.48
-108,0.621403,0.45839,0.026638,0.037245,Smoke,0.58
-110,0.637573,0.465766,0.027141,0.040499,Smoke,0.58
-112,0.610838,0.480663,0.0283,0.041891,Smoke,0.72
-114,0.60922,0.486313,0.026929,0.042859,Smoke,0.68
-116,0.615395,0.492105,0.028959,0.041361,Smoke,0.67
-118,0.612622,0.49208,0.027159,0.039772,Smoke,0.61
-120,0.488272,0.653743,0.028969,0.045788,Smoke,0.67
-122,0.276008,0.692814,0.061306,0.10955,Smoke,0.74
-127,0.5029,0.24006,0.116369,0.208533,Smoke,0.74
-130,0.350402,0.278989,0.122529,0.179202,Smoke,0.7
-132,0.525031,0.469289,0.103297,0.193989,Smoke,0.48
-134,0.475175,0.52287,0.099367,0.178105,Smoke,0.27
-136,0.042595,0.458038,0.073769,0.201478,Smoke,0.72
-138,0.398975,0.280201,0.132485,0.076752,Smoke,0.3
-149,0.115298,0.585385,0.037456,0.030006,Trenches,0.64
-149,0.149621,0.602923,0.039252,0.032263,Trenches,0.73
-149,0.534584,0.082995,0.03861,0.036615,Trenches,0.51
-151,0.363107,0.353294,0.036212,0.030509,Trenches,0.52
-151,0.395121,0.377565,0.03571,0.034257,Trenches,0.52
-153,0.540678,0.549795,0.04493,0.040794,Trenches,0.55
-155,0.124097,0.466852,0.074959,0.051802,Trenches,0.32
-163,0.687639,0.261656,0.116238,0.125195,TyreTracks,0.43
-168,0.378062,0.769232,0.032526,0.027289,Trenches,0.46
-174,0.435846,0.904734,0.03305,0.03245,Trenches,0.27
-176,0.942677,0.589411,0.024974,0.052446,Trenches,0.33
-181,0.612998,0.232146,0.08565,0.060314,Smoke,0.52
-191,0.390291,0.036179,0.054409,0.033613,Trenches,0.39
-193,0.413883,0.208317,0.053158,0.031181,Trenches,0.76
-193,0.430828,0.171384,0.030674,0.029638,Trenches,0.27
-193,0.484856,0.182248,0.04184,0.063353,Trenches,0.65
-193,0.491091,0.316695,0.031371,0.024474,Trenches,0.37
-195,0.556304,0.101112,0.046918,0.036026,Smoke,0.71
-197,0.519331,0.120967,0.044832,0.039692,Smoke,0.67
-199,0.176271,0.118214,0.047294,0.053538,Smoke,0.63
-202,0.171963,0.138687,0.043301,0.04702,Smoke,0.46
-214,0.413986,0.12009,0.050383,0.046863,Smoke,0.5
-219,0.364435,0.552301,0.028486,0.0314,ArmorVehicle,0.72
-240,0.502753,0.535129,0.036657,0.033246,Trenches,0.32
-242,0.019098,0.607661,0.038072,0.024671,Trenches,0.27
-242,0.02406,0.680742,0.044204,0.023859,Trenches,0.37
-242,0.027672,0.720891,0.033973,0.04163,Trenches,0.65
-242,0.041207,0.628713,0.021846,0.044712,Trenches,0.29
-242,0.053195,0.682519,0.027647,0.038862,Trenches,0.65
-242,0.84993,0.631653,0.046269,0.042584,Trenches,0.47
-244,0.017405,0.7017,0.034866,0.023287,Trenches,0.35
-244,0.018653,0.733875,0.03717,0.025044,Trenches,0.48
-244,0.032765,0.621231,0.064777,0.023041,Trenches,0.45
-244,0.05303,0.734179,0.034922,0.041688,Trenches,0.7
-244,0.070526,0.649712,0.023196,0.061101,Trenches,0.44
-244,0.076041,0.69735,0.028326,0.039375,Trenches,0.65
-246,0.025853,0.893559,0.051857,0.02183,Trenches,0.33
-246,0.055409,0.908165,0.020511,0.040618,Trenches,0.32
-252,0.735668,0.309579,0.026179,0.044193,Trenches,0.61
-252,0.760952,0.371567,0.026674,0.062061,Trenches,0.52
-282,0.566367,0.010329,0.036472,0.020469,Smoke,0.49
-284,0.341962,0.011659,0.03561,0.023243,Smoke,0.59
-287,0.423856,0.023553,0.03954,0.044283,Smoke,0.42
-290,0.397077,0.010032,0.037649,0.019987,Smoke,0.29
-292,0.415613,0.011793,0.036875,0.023537,Smoke,0.37
-294,0.385953,0.009416,0.037843,0.018655,Smoke,0.39

_docs/00_problem/input_data/expected_results/video_test01_expected.csv

@@ -1,5 +1 @@
 time_sec,center_x,center_y,width,height,label,confidence_min
-0,0.289857,0.512138,0.07729,0.072891,ArmorVehicle,0.89
-2,0.653617,0.376064,0.078636,0.08118,ArmorVehicle,0.89
-4,0.197561,0.416208,0.068112,0.084079,ArmorVehicle,0.85
-6,0.27662,0.609538,0.059521,0.067212,ArmorVehicle,0.8

_docs/02_document/modules/ai_config.md

@@ -20,7 +20,7 @@ Data class holding all AI recognition configuration parameters, with factory met
 | `tracking_intersection_threshold` | double | 0.6 | IoU threshold for overlapping detection removal |
 | `model_batch_size` | int | 1 | Batch size for inference |
 | `big_image_tile_overlap_percent` | int | 20 | Tile overlap percentage for large image splitting |
-| `altitude` | double? | optional | Camera altitude in meters. When missing, GSD-based filtering is disabled |
+| `altitude` | double | 400 | Camera altitude in meters |
 | `focal_length` | double | 24 | Camera focal length in mm |
 | `sensor_width` | double | 23.5 | Camera sensor width in mm |
 
@@ -51,7 +51,7 @@ Data class holding all AI recognition configuration parameters, with factory met
 
 ## Configuration
 
-Camera/altitude parameters (`altitude`, `focal_length`, `sensor_width`) are used for ground sampling distance calculation in aerial image processing. If `altitude` is missing, the service skips GSD-based size filtering and does not tile large images by physical size.
+Camera/altitude parameters (`altitude`, `focal_length`, `sensor_width`) are used for ground sampling distance calculation in aerial image processing.
 
 ## External Integrations
 

_docs/02_document/tests/test-data.md

@@ -8,9 +8,12 @@
 | classes-json | `classes.json` (repo root) | 19 detection classes with Id, Name, Color, MaxSizeM | All tests | Volume mount to detections `/app/classes.json` | Container restart |
 | image-small | `input_data/image_small.jpg` | JPEG 1280×720 — below tiling threshold (1920×1920) | FT-P-01..03, 05, 07, 13..15, FT-N-03, 06, NFT-PERF-01..02, NFT-RES-01, 03, NFT-SEC-01, NFT-RES-LIM-01 | Volume mount to consumer `/media/` | N/A (read-only) |
 | image-large | `input_data/image_large.JPG` | JPEG 6252×4168 — above tiling threshold, triggers GSD tiling | FT-P-04, 16, NFT-PERF-03 | Volume mount to consumer `/media/` | N/A (read-only) |
+| image-dense-01 | `input_data/image_dense01.jpg` | JPEG 1280×720 — dense scene with many clustered objects | FT-P-06, NFT-RES-LIM-03 | Volume mount to consumer `/media/` | N/A (read-only) |
+| image-dense-02 | `input_data/image_dense02.jpg` | JPEG 1920×1080 — dense scene variant, borderline tiling | FT-P-06 (variant) | Volume mount to consumer `/media/` | N/A (read-only) |
 | image-different-types | `input_data/image_different_types.jpg` | JPEG 900×1600 — varied object classes for class variant tests | FT-P-13 | Volume mount to consumer `/media/` | N/A (read-only) |
 | image-empty-scene | `input_data/image_empty_scene.jpg` | JPEG 1920×1080 — clean scene with no detectable objects | Edge case (zero detections) | Volume mount to consumer `/media/` | N/A (read-only) |
 | video-test-01 | `input_data/video_test01.mp4` | MP4 video — standard async/SSE/video detection tests | FT-P-08..12, FT-N-04, 07, NFT-PERF-04, NFT-RES-02, NFT-SEC-03 | Volume mount to consumer `/media/` | N/A (read-only) |
+| video-1 | `input_data/video_1.mp4` | MP4 video — local variant for concurrent and resilience-style tests | NFT-RES-02 (variant), NFT-RES-04 | Volume mount to consumer `/media/` | N/A (read-only) |
 | video-1-faststart | `input_data/video_1_faststart.mp4` | MP4 video — faststart/local streaming variant | Streaming compatibility checks | Volume mount to consumer `/media/` | N/A (read-only) |
 | empty-image | Generated at build time | Zero-byte file | FT-N-01 | Generated in e2e/fixtures/ | N/A |
 | corrupt-image | Generated at build time | Random binary garbage (not valid image format) | FT-N-02 | Generated in e2e/fixtures/ | N/A |
@@ -28,9 +31,12 @@ Each test run starts with fresh containers (`docker compose down -v && docker co
 | azaion.onnx | `_docs/00_problem/input_data/azaion.onnx` | YOLO ONNX detection model | All detection tests |
 | image_small.jpg | `_docs/00_problem/input_data/image_small.jpg` | 1280×720 aerial image | Single-frame detection, health, negative, perf tests |
 | image_large.JPG | `_docs/00_problem/input_data/image_large.JPG` | 6252×4168 aerial image | Tiling tests |
+| image_dense01.jpg | `_docs/00_problem/input_data/image_dense01.jpg` | Dense scene 1280×720 | Dedup, detection cap tests |
+| image_dense02.jpg | `_docs/00_problem/input_data/image_dense02.jpg` | Dense scene 1920×1080 | Dedup variant |
 | image_different_types.jpg | `_docs/00_problem/input_data/image_different_types.jpg` | Varied classes 900×1600 | Class variant tests |
 | image_empty_scene.jpg | `_docs/00_problem/input_data/image_empty_scene.jpg` | Empty scene 1920×1080 | Zero-detection edge case |
 | video_test01.mp4 | `_docs/00_problem/input_data/video_test01.mp4` | Standard video | Async, SSE, video, perf tests |
+| video_1.mp4 | `_docs/00_problem/input_data/video_1.mp4` | Video variant | Resilience, concurrent tests |
 | video_1_faststart.mp4 | `_docs/00_problem/input_data/video_1_faststart.mp4` | Faststart video variant | Streaming compatibility checks |
 | classes.json | repo root `classes.json` | 19 detection classes | All tests |
 

_docs/02_tasks/done/AZ-138_test_infrastructure.md

@@ -27,6 +27,8 @@ e2e/
 ├── fixtures/
 │   ├── image_small.jpg          (1280×720 JPEG, aerial, detectable objects)
 │   ├── image_large.JPG          (6252×4168 JPEG, triggers tiling)
+│   ├── image_dense01.jpg        (1280×720 JPEG, dense scene, clustered objects)
+│   ├── image_dense02.jpg        (1920×1080 JPEG, dense scene variant)
 │   ├── image_different_types.jpg (900×1600 JPEG, varied object classes)
 │   ├── image_empty_scene.jpg    (1920×1080 JPEG, no detectable objects)
 │   ├── video_short01.mp4        (short MP4 with moving objects)
@@ -128,6 +130,8 @@ Two Docker Compose profiles:
 | `reset_mocks` | function (autouse) | Calls `POST /mock/reset` on both mocks before each test |
 | `image_small` | session | Reads `image_small.jpg` from `/media/` volume |
 | `image_large` | session | Reads `image_large.JPG` from `/media/` volume |
+| `image_dense` | session | Reads `image_dense01.jpg` from `/media/` volume |
+| `image_dense_02` | session | Reads `image_dense02.jpg` from `/media/` volume |
 | `image_different_types` | session | Reads `image_different_types.jpg` from `/media/` volume |
 | `image_empty_scene` | session | Reads `image_empty_scene.jpg` from `/media/` volume |
 | `video_short_path` | session | Path to `video_short01.mp4` on `/media/` volume |
@@ -146,6 +150,8 @@ Two Docker Compose profiles:
 | classes.json | repo root `classes.json` | JSON (19 objects with Id, Name, Color, MaxSizeM) | All tests (volume mount to detections) |
 | image_small.jpg | `input_data/image_small.jpg` | JPEG 1280×720 | Health, single image, filtering, negative, performance tests |
 | image_large.JPG | `input_data/image_large.JPG` | JPEG 6252×4168 | Tiling tests, performance tests |
+| image_dense01.jpg | `input_data/image_dense01.jpg` | JPEG 1280×720 dense scene | Dedup tests, detection cap tests |
+| image_dense02.jpg | `input_data/image_dense02.jpg` | JPEG 1920×1080 dense scene | Dedup variant |
 | image_different_types.jpg | `input_data/image_different_types.jpg` | JPEG 900×1600 varied classes | Weather mode class variant tests |
 | image_empty_scene.jpg | `input_data/image_empty_scene.jpg` | JPEG 1920×1080 empty | Zero-detection edge case |
 | video_short01.mp4 | `input_data/video_short01.mp4` | MP4 short video | Async, SSE, video processing tests |

_docs/_autopilot_state.md

@@ -30,13 +30,3 @@ step: 14 (Deploy) — DONE (deploy_status_report.md + deploy_scripts.md updated
 ## Rollback Note
 2026-04-10: Rolled back from step 8 (New Task) to step 2 (Test Spec).
 Reason: All 9 expected result CSV files in _docs/00_problem/input_data/expected_results/ contain headers only — zero data rows. results_report.md has "?" for detection counts. Phase 1 and Phase 3 BLOCKING gates were not enforced. E2E tests cannot verify detection accuracy without ground truth data.
-
-## Recovery Note
-2026-04-23: Expected-result artifacts were populated for the active local fixture set.
-
-- Image CSVs now exist for: `image_small`, `image_large`, `image_different_types`, `image_empty_scene`
-- Video CSVs now exist for: `video_test01`, `video_1_faststart`
-- `results_report.md` was updated to match the active fixture set and populated image detection counts
-- Obsolete fixtures were removed from the active test-data set: `image_dense01`, `image_dense02`, `video_1`
-
-Implication: the original expected-results blocker recorded in the rollback note no longer reflects the current repository state for the active fixture set. Resume Step 2 / Phase 3 validation from the current artifacts rather than assuming CSV ground truth is still missing.

e2e/conftest.py

@@ -228,6 +228,17 @@ def image_small():
 def image_large():
     return _read_media("image_large.JPG")
 
+
+@pytest.fixture(scope="session")
+def image_dense():
+    return _read_media("image_dense01.jpg")
+
+
+@pytest.fixture(scope="session")
+def image_dense_02():
+    return _read_media("image_dense02.jpg")
+
+
 @pytest.fixture(scope="session")
 def image_different_types():
     return _read_media("image_different_types.jpg")

e2e/docker-compose.test.yml

@@ -58,35 +58,10 @@ services:
         aliases:
           - detections
 
-  detections-jetson:
-    profiles:
-      - jetson
-    build:
-      context: ..
-      dockerfile: Dockerfile.jetson
-    runtime: nvidia
-    depends_on:
-      - mock-loader
-      - mock-annotations
-    environment:
-      LOADER_URL: http://mock-loader:8080
-      ANNOTATIONS_URL: http://mock-annotations:8081
-      JWT_SECRET: test-secret-e2e-only
-      CLASSES_JSON_PATH: /app/classes.json
-    volumes:
-      - ./fixtures:/media:ro
-      - ./logs:/app/Logs
-    shm_size: 512m
-    networks:
-      e2e-net:
-        aliases:
-          - detections
-
   e2e-runner:
     profiles:
       - cpu
       - gpu
-      - jetson
     build: .
     depends_on:
       - mock-loader

e2e/requirements.txt

@@ -1,7 +1,6 @@
 pytest
 pytest-csv
 requests==2.32.4
-PyJWT==2.12.1
 sseclient-py
 pytest-timeout
 flask

e2e/run_test.sh

@@ -1,24 +1,7 @@
 #!/usr/bin/env bash
 set -euo pipefail
 
-PROFILE="${E2E_PROFILE:-cpu}"
+COMPOSE="docker compose -f docker-compose.test.yml --profile cpu"
-case "$PROFILE" in
-  cpu)
-    DETECTIONS_SERVICE="detections"
-    ;;
-  gpu)
-    DETECTIONS_SERVICE="detections-gpu"
-    ;;
-  jetson)
-    DETECTIONS_SERVICE="detections-jetson"
-    ;;
-  *)
-    echo "ERROR: unsupported E2E_PROFILE=$PROFILE (expected: cpu, gpu, jetson)"
-    exit 2
-    ;;
-esac
-
-COMPOSE="docker compose -f docker-compose.test.yml --profile $PROFILE"
 
 usage() {
   echo "Usage: $0 <test_path> [pytest_args...]"
@@ -28,37 +11,27 @@ usage() {
   echo "  $0 tests/test_video.py::test_ft_p_10_frame_sampling_ac1  # run single test"
   echo "  $0 tests/test_video.py -k 'frame_sampling'          # run by keyword"
   echo ""
-  echo "Environment:"
-  echo "  E2E_PROFILE=cpu|gpu|jetson  compose profile to run (default: cpu)"
-  echo ""
   echo "Flags -v -x -s are always included."
   exit 1
 }
 
 [[ $# -lt 1 ]] && usage
 
-$COMPOSE up -d --build "$DETECTIONS_SERVICE"
+$COMPOSE up -d --build detections
 echo "--- Waiting for detections service to become healthy..."
 for i in $(seq 1 60); do
-  if $COMPOSE exec -T "$DETECTIONS_SERVICE" python3 -c "import urllib.request; urllib.request.urlopen('http://localhost:8080/health')" 2>/dev/null; then
+  if $COMPOSE exec -T detections python3 -c "import urllib.request; urllib.request.urlopen('http://localhost:8080/health')" 2>/dev/null; then
     echo "--- Detections service is healthy"
     break
   fi
-  if [[ "$i" == "60" ]]; then
-    echo "ERROR: detections service did not become healthy"
-    $COMPOSE logs "$DETECTIONS_SERVICE" --tail 100
-    exit 1
-  fi
   sleep 2
 done
 
 echo "--- Running: pytest $* -v -x -s --csv=/results/report.csv"
-set +e
+$COMPOSE run --rm --no-deps e2e-runner pytest "$@" -v -x -s --csv=/results/report.csv
-$COMPOSE run --rm --build --no-deps e2e-runner pytest "$@" -v -x -s --csv=/results/report.csv
 EXIT_CODE=$?
-set -e
 
 echo "--- Test finished with exit code $EXIT_CODE"
 echo "--- Detections logs (last 100 lines):"
-$COMPOSE logs "$DETECTIONS_SERVICE" --tail 100
+$COMPOSE logs detections --tail 100
 exit $EXIT_CODE

src/ai_config.pxd

@@ -12,7 +12,6 @@ cdef class AIRecognitionConfig:
 
     cdef public int model_batch_size
 
-    cdef public bint has_altitude
     cdef public double altitude
     cdef public double focal_length
     cdef public double sensor_width

src/ai_config.pyx

@@ -25,8 +25,7 @@ cdef class AIRecognitionConfig:
 
         self.big_image_tile_overlap_percent = big_image_tile_overlap_percent
 
-        self.has_altitude = altitude is not None
+        self.altitude = altitude
-        self.altitude = 0.0 if altitude is None else float(altitude)
         self.focal_length = focal_length
         self.sensor_width = sensor_width
 
@@ -37,7 +36,7 @@ cdef class AIRecognitionConfig:
                 f'frame_period_recognition : {self.frame_period_recognition}, '
                 f'big_image_tile_overlap_percent: {self.big_image_tile_overlap_percent}, '
                 f'model_batch_size: {self.model_batch_size}, '
-                f'altitude: {self.altitude if self.has_altitude else None}, '
+                f'altitude: {self.altitude}, '
                 f'focal_length: {self.focal_length}, '
                 f'sensor_width: {self.sensor_width}'
                 )
@@ -57,7 +56,7 @@ cdef class AIRecognitionConfig:
 
             data.get("big_image_tile_overlap_percent", 20),
 
-            data.get("altitude", None),
+            data.get("altitude", 400),
             data.get("focal_length", 24),
             data.get("sensor_width", 23.5)
         )

src/annotation.pxd

@@ -7,6 +7,6 @@ cdef class Detection:
 cdef class Annotation:
     cdef public str name
     cdef public str original_media_name
-    cdef public long time
+    cdef long time
     cdef public list[Detection] detections
     cdef public bytes image

src/inference.pyx

@@ -311,22 +311,13 @@ cdef class Inference:
         cdef double ground_sampling_distance
         cdef int model_h, model_w
         cdef int img_h, img_w
-        cdef bint has_gsd
         model_h, model_w = self.engine.get_input_shape()
         img_h, img_w, _ = frame.shape
-        has_gsd = ai_config.has_altitude and ai_config.focal_length > 0 and ai_config.sensor_width > 0 and img_w > 0
+        ground_sampling_distance = ai_config.sensor_width * ai_config.altitude / (ai_config.focal_length * img_w)
-        ground_sampling_distance = 0.0
+        constants_inf.log(<str>f'ground sampling distance: {ground_sampling_distance}')
-        if has_gsd:
-            ground_sampling_distance = ai_config.sensor_width * ai_config.altitude / (ai_config.focal_length * img_w)
-            constants_inf.log(<str>f'ground sampling distance: {ground_sampling_distance}')
-        else:
-            constants_inf.log(<str>'ground sampling distance: skipped (altitude unavailable)')
         if img_h <= 1.5 * model_h and img_w <= 1.5 * model_w:
             all_frame_data.append((frame, original_media_name, f'{original_media_name}_000000', ground_sampling_distance))
         else:
-            if not has_gsd:
-                all_frame_data.append((frame, original_media_name, f'{original_media_name}_000000', ground_sampling_distance))
-                return
             tile_size = int(constants_inf.METERS_IN_TILE / ground_sampling_distance)
             constants_inf.log(<str> f'calc tile size: {tile_size}')
             res = self.split_to_tiles(frame, original_media_name, tile_size, ai_config.big_image_tile_overlap_percent)
@@ -419,12 +410,6 @@ cdef class Inference:
         if annotation.detections:
             constants_inf.log(<str> f'Initial ann: {annotation}')
 
-        if ground_sampling_distance <= 0:
-            if not annotation.detections:
-                return <bint>False
-            constants_inf.log(<str>'Skipping physical-size filtering (ground sampling distance unavailable)')
-            return <bint>True
-
         cdef list[Detection] valid_detections = []
         for det in annotation.detections:
             m_w = det.w * img_w * ground_sampling_distance

src/main.py

@@ -15,7 +15,6 @@ import cv2
 import jwt as pyjwt
 import numpy as np
 import requests as http_requests
-from loguru import logger
 from fastapi import Body, Depends, FastAPI, File, Form, HTTPException, Request, UploadFile
 from fastapi.responses import Response, StreamingResponse
 from fastapi.security import HTTPAuthorizationCredentials, HTTPBearer
@@ -164,7 +163,7 @@ class AIConfigDto(BaseModel):
     tracking_intersection_threshold: float = 0.6
     model_batch_size: int = 8
     big_image_tile_overlap_percent: int = 20
-    altitude: Optional[float] = None
+    altitude: float = 400
     focal_length: float = 24
     sensor_width: float = 23.5
 
@@ -271,8 +270,7 @@ def _post_media_record(payload: dict, bearer: str) -> bool:
             timeout=30,
         )
         return r.status_code in (200, 201)
-    except Exception as exc:
+    except Exception:
-        logger.warning(f"Failed to create media record in annotations service: {exc}")
         return False
 
 
@@ -286,8 +284,7 @@ def _put_media_status(media_id: str, media_status: int, bearer: str) -> bool:
             timeout=30,
         )
         return r.status_code in (200, 204)
-    except Exception as exc:
+    except Exception:
-        logger.warning(f"Failed to update media status in annotations service for {media_id}: {exc}")
         return False
 
 
@@ -335,13 +332,10 @@ def _post_annotation_to_service(token_mgr: TokenManager, media_id: str,
     try:
         token = token_mgr.get_valid_token()
         image_b64 = base64.b64encode(annotation.image).decode() if annotation.image else None
-        total_seconds = int(annotation.time // 1000) if annotation.time else 0
-        hours, remainder = divmod(total_seconds, 3600)
-        minutes, seconds = divmod(remainder, 60)
         payload = {
             "mediaId": media_id,
             "source": 0,
-            "videoTime": f"{hours:02d}:{minutes:02d}:{seconds:02d}",
+            "videoTime": f"00:00:{annotation.time // 1000:02d}" if annotation.time else "00:00:00",
             "detections": [d.model_dump() for d in dtos],
         }
         if image_b64:
@@ -352,10 +346,8 @@ def _post_annotation_to_service(token_mgr: TokenManager, media_id: str,
             headers={"Authorization": f"Bearer {token}"},
             timeout=30,
         )
-    except Exception as exc:
+    except Exception:
-        logger.warning(
+        pass
-            f"Failed to post annotation to annotations service for media {media_id}: {exc}"
-        )
 
 
 def _cleanup_channel(channel_id: str):

tests/test_ai_config_from_dict.py

@@ -5,15 +5,6 @@ def test_ai_config_from_dict_defaults():
     assert cfg.model_batch_size == 8
     assert cfg.frame_period_recognition == 4
     assert cfg.frame_recognition_seconds == 2
-    assert cfg.has_altitude is False
-
-
-def test_ai_config_from_dict_altitude_override_sets_flag():
-    from inference import ai_config_from_dict
-
-    cfg = ai_config_from_dict({"altitude": 400})
-    assert cfg.has_altitude is True
-    assert cfg.altitude == 400
 
 
 def test_ai_config_from_dict_overrides():

tests/test_az174_db_driven_config.py

@@ -118,23 +118,6 @@ def test_resolve_media_for_detect_override_wins():
     assert "paths" not in cfg
 
 
-def test_resolve_media_for_detect_omits_altitude_when_not_provided():
-    # Arrange
-    import main
-
-    tm = main.TokenManager(_access_jwt(), "")
-    mock_ann = MagicMock()
-    mock_ann.fetch_user_ai_settings.return_value = {"probabilityThreshold": 0.2}
-    mock_ann.fetch_media_path.return_value = "/m/v.mp4"
-    with patch("main.annotations_client", mock_ann):
-        # Act
-        cfg, path = main._resolve_media_for_detect("vid-2", tm, None)
-    # Assert
-    assert "altitude" not in cfg
-    assert cfg["probability_threshold"] == 0.2
-    assert path == "/m/v.mp4"
-
-
 def test_resolve_media_for_detect_raises_when_no_media_path():
     # Arrange
     import main