chore: version v2.2.3

* fix(mobile): ignore patch releases for app version alerts

* chore: make difference type nullable to indicate when versions match

* chore: add error handling for semver parsing

* chore: tests

cli/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@immich/cli",
-  "version": "2.2.100",
+  "version": "2.2.101",
   "description": "Command Line Interface (CLI) for Immich",
   "type": "module",
   "exports": "./dist/index.js",

docs/static/archived-versions.json

@@ -1,4 +1,8 @@
 [
+  {
+    "label": "v2.2.3",
+    "url": "https://docs.v2.2.3.archive.immich.app"
+  },
   {
     "label": "v2.2.2",
     "url": "https://docs.v2.2.2.archive.immich.app"

e2e/package.json

@@ -1,6 +1,6 @@
 {
   "name": "immich-e2e",
-  "version": "2.2.2",
+  "version": "2.2.3",
   "description": "",
   "main": "index.js",
   "type": "module",

e2e/src/api/specs/asset.e2e-spec.ts

@@ -1140,6 +1140,16 @@ describe('/asset', () => {
           },
         },
       },
+      {
+        input: 'metadata/gps-position/empty_gps.jpg',
+        expected: {
+          type: AssetTypeEnum.Image,
+          exifInfo: {
+            latitude: null,
+            longitude: null,
+          },
+        },
+      },
     ];
 
     it.each(tests)(`should upload and generate a thumbnail for different file types`, async ({ input, expected }) => {

machine-learning/immich_ml/models/ocr/detection.py

@@ -1,8 +1,10 @@
 from typing import Any
 
+import cv2
 import numpy as np
+from numpy.typing import NDArray
 from PIL import Image
-from rapidocr.ch_ppocr_det import TextDetector as RapidTextDetector
+from rapidocr.ch_ppocr_det.utils import DBPostProcess
 from rapidocr.inference_engine.base import FileInfo, InferSession
 from rapidocr.utils import DownloadFile, DownloadFileInput
 from rapidocr.utils.typings import EngineType, LangDet, OCRVersion, TaskType
@@ -10,11 +12,10 @@ from rapidocr.utils.typings import ModelType as RapidModelType
 
 from immich_ml.config import log
 from immich_ml.models.base import InferenceModel
-from immich_ml.models.transforms import decode_cv2
 from immich_ml.schemas import ModelFormat, ModelSession, ModelTask, ModelType
 from immich_ml.sessions.ort import OrtSession
 
-from .schemas import OcrOptions, TextDetectionOutput
+from .schemas import TextDetectionOutput
 
 
 class TextDetector(InferenceModel):
@@ -24,13 +25,20 @@ class TextDetector(InferenceModel):
     def __init__(self, model_name: str, **model_kwargs: Any) -> None:
         super().__init__(model_name, **model_kwargs, model_format=ModelFormat.ONNX)
         self.max_resolution = 736
-        self.min_score = 0.5
-        self.score_mode = "fast"
+        self.mean = np.array([0.5, 0.5, 0.5], dtype=np.float32)
+        self.std_inv = np.float32(1.0) / (np.array([0.5, 0.5, 0.5], dtype=np.float32) * 255.0)
         self._empty: TextDetectionOutput = {
-            "image": np.empty(0, dtype=np.float32),
             "boxes": np.empty(0, dtype=np.float32),
             "scores": np.empty(0, dtype=np.float32),
         }
+        self.postprocess = DBPostProcess(
+            thresh=0.3,
+            box_thresh=model_kwargs.get("minScore", 0.5),
+            max_candidates=1000,
+            unclip_ratio=1.6,
+            use_dilation=True,
+            score_mode="fast",
+        )
 
     def _download(self) -> None:
         model_info = InferSession.get_model_url(
@@ -52,35 +60,65 @@ class TextDetector(InferenceModel):
 
     def _load(self) -> ModelSession:
         # TODO: support other runtime sessions
-        session = OrtSession(self.model_path)
-        self.model = RapidTextDetector(
-            OcrOptions(
-                session=session.session,
-                limit_side_len=self.max_resolution,
-                limit_type="min",
-                box_thresh=self.min_score,
-                score_mode=self.score_mode,
-            )
-        )
-        return session
+        return OrtSession(self.model_path)
 
-    def _predict(self, inputs: bytes | Image.Image) -> TextDetectionOutput:
-        results = self.model(decode_cv2(inputs))
-        if results.boxes is None or results.scores is None or results.img is None:
+    # partly adapted from RapidOCR
+    def _predict(self, inputs: Image.Image) -> TextDetectionOutput:
+        w, h = inputs.size
+        if w < 32 or h < 32:
+            return self._empty
+        out = self.session.run(None, {"x": self._transform(inputs)})[0]
+        boxes, scores = self.postprocess(out, (h, w))
+        if len(boxes) == 0:
             return self._empty
         return {
-            "image": results.img,
-            "boxes": np.array(results.boxes, dtype=np.float32),
-            "scores": np.array(results.scores, dtype=np.float32),
+            "boxes": self.sorted_boxes(boxes),
+            "scores": np.array(scores, dtype=np.float32),
         }
 
+    # adapted from RapidOCR
+    def _transform(self, img: Image.Image) -> NDArray[np.float32]:
+        if img.height < img.width:
+            ratio = float(self.max_resolution) / img.height
+        else:
+            ratio = float(self.max_resolution) / img.width
+
+        resize_h = int(img.height * ratio)
+        resize_w = int(img.width * ratio)
+
+        resize_h = int(round(resize_h / 32) * 32)
+        resize_w = int(round(resize_w / 32) * 32)
+        resized_img = img.resize((int(resize_w), int(resize_h)), resample=Image.Resampling.LANCZOS)
+
+        img_np: NDArray[np.float32] = cv2.cvtColor(np.array(resized_img, dtype=np.float32), cv2.COLOR_RGB2BGR)  # type: ignore
+        img_np -= self.mean
+        img_np *= self.std_inv
+        img_np = np.transpose(img_np, (2, 0, 1))
+        return np.expand_dims(img_np, axis=0)
+
+    def sorted_boxes(self, dt_boxes: NDArray[np.float32]) -> NDArray[np.float32]:
+        if len(dt_boxes) == 0:
+            return dt_boxes
+
+        # Sort by y, then identify lines, then sort by (line, x)
+        y_order = np.argsort(dt_boxes[:, 0, 1], kind="stable")
+        sorted_y = dt_boxes[y_order, 0, 1]
+
+        line_ids = np.empty(len(dt_boxes), dtype=np.int32)
+        line_ids[0] = 0
+        np.cumsum(np.abs(np.diff(sorted_y)) >= 10, out=line_ids[1:])
+
+        # Create composite sort key for final ordering
+        # Shift line_ids by large factor, add x for tie-breaking
+        sort_key = line_ids[y_order] * 1e6 + dt_boxes[y_order, 0, 0]
+        final_order = np.argsort(sort_key, kind="stable")
+        sorted_boxes: NDArray[np.float32] = dt_boxes[y_order[final_order]]
+        return sorted_boxes
+
     def configure(self, **kwargs: Any) -> None:
         if (max_resolution := kwargs.get("maxResolution")) is not None:
             self.max_resolution = max_resolution
-            self.model.limit_side_len = max_resolution
         if (min_score := kwargs.get("minScore")) is not None:
-            self.min_score = min_score
-            self.model.postprocess_op.box_thresh = min_score
+            self.postprocess.box_thresh = min_score
         if (score_mode := kwargs.get("scoreMode")) is not None:
-            self.score_mode = score_mode
-            self.model.postprocess_op.score_mode = score_mode
+            self.postprocess.score_mode = score_mode

machine-learning/immich_ml/models/ocr/recognition.py

@@ -1,9 +1,8 @@
 from typing import Any
 
-import cv2
 import numpy as np
 from numpy.typing import NDArray
-from PIL.Image import Image
+from PIL import Image
 from rapidocr.ch_ppocr_rec import TextRecInput
 from rapidocr.ch_ppocr_rec import TextRecognizer as RapidTextRecognizer
 from rapidocr.inference_engine.base import FileInfo, InferSession
@@ -14,6 +13,7 @@ from rapidocr.utils.vis_res import VisRes
 
 from immich_ml.config import log, settings
 from immich_ml.models.base import InferenceModel
+from immich_ml.models.transforms import pil_to_cv2
 from immich_ml.schemas import ModelFormat, ModelSession, ModelTask, ModelType
 from immich_ml.sessions.ort import OrtSession
 
@@ -65,17 +65,16 @@ class TextRecognizer(InferenceModel):
         )
         return session
 
-    def _predict(self, _: Image, texts: TextDetectionOutput) -> TextRecognitionOutput:
-        boxes, img, box_scores = texts["boxes"], texts["image"], texts["scores"]
+    def _predict(self, img: Image.Image, texts: TextDetectionOutput) -> TextRecognitionOutput:
+        boxes, box_scores = texts["boxes"], texts["scores"]
         if boxes.shape[0] == 0:
             return self._empty
         rec = self.model(TextRecInput(img=self.get_crop_img_list(img, boxes)))
         if rec.txts is None:
             return self._empty
 
-        height, width = img.shape[0:2]
-        boxes[:, :, 0] /= width
-        boxes[:, :, 1] /= height
+        boxes[:, :, 0] /= img.width
+        boxes[:, :, 1] /= img.height
 
         text_scores = np.array(rec.scores)
         valid_text_score_idx = text_scores > self.min_score
@@ -87,7 +86,7 @@ class TextRecognizer(InferenceModel):
             "textScore": text_scores[valid_text_score_idx],
         }
 
-    def get_crop_img_list(self, img: NDArray[np.float32], boxes: NDArray[np.float32]) -> list[NDArray[np.float32]]:
+    def get_crop_img_list(self, img: Image.Image, boxes: NDArray[np.float32]) -> list[NDArray[np.uint8]]:
         img_crop_width = np.maximum(
             np.linalg.norm(boxes[:, 1] - boxes[:, 0], axis=1), np.linalg.norm(boxes[:, 2] - boxes[:, 3], axis=1)
         ).astype(np.int32)
@@ -98,22 +97,55 @@ class TextRecognizer(InferenceModel):
         pts_std[:, 1:3, 0] = img_crop_width[:, None]
         pts_std[:, 2:4, 1] = img_crop_height[:, None]
 
-        img_crop_sizes = np.stack([img_crop_width, img_crop_height], axis=1).tolist()
-        imgs: list[NDArray[np.float32]] = []
-        for box, pts_std, dst_size in zip(list(boxes), list(pts_std), img_crop_sizes):
-            M = cv2.getPerspectiveTransform(box, pts_std)
-            dst_img: NDArray[np.float32] = cv2.warpPerspective(
-                img,
-                M,
-                dst_size,
-                borderMode=cv2.BORDER_REPLICATE,
-                flags=cv2.INTER_CUBIC,
-            )  # type: ignore
-            dst_height, dst_width = dst_img.shape[0:2]
+        img_crop_sizes = np.stack([img_crop_width, img_crop_height], axis=1)
+        all_coeffs = self._get_perspective_transform(pts_std, boxes)
+        imgs: list[NDArray[np.uint8]] = []
+        for coeffs, dst_size in zip(all_coeffs, img_crop_sizes):
+            dst_img = img.transform(
+                size=tuple(dst_size),
+                method=Image.Transform.PERSPECTIVE,
+                data=tuple(coeffs),
+                resample=Image.Resampling.BICUBIC,
+            )
+
+            dst_width, dst_height = dst_img.size
             if dst_height * 1.0 / dst_width >= 1.5:
-                dst_img = np.rot90(dst_img)
-            imgs.append(dst_img)
+                dst_img = dst_img.rotate(90, expand=True)
+            imgs.append(pil_to_cv2(dst_img))
+
         return imgs
 
+    def _get_perspective_transform(self, src: NDArray[np.float32], dst: NDArray[np.float32]) -> NDArray[np.float32]:
+        N = src.shape[0]
+        x, y = src[:, :, 0], src[:, :, 1]
+        u, v = dst[:, :, 0], dst[:, :, 1]
+        A = np.zeros((N, 8, 9), dtype=np.float32)
+
+        # Fill even rows (0, 2, 4, 6): [x, y, 1, 0, 0, 0, -u*x, -u*y, -u]
+        A[:, ::2, 0] = x
+        A[:, ::2, 1] = y
+        A[:, ::2, 2] = 1
+        A[:, ::2, 6] = -u * x
+        A[:, ::2, 7] = -u * y
+        A[:, ::2, 8] = -u
+
+        # Fill odd rows (1, 3, 5, 7): [0, 0, 0, x, y, 1, -v*x, -v*y, -v]
+        A[:, 1::2, 3] = x
+        A[:, 1::2, 4] = y
+        A[:, 1::2, 5] = 1
+        A[:, 1::2, 6] = -v * x
+        A[:, 1::2, 7] = -v * y
+        A[:, 1::2, 8] = -v
+
+        # Solve using SVD for all matrices at once
+        _, _, Vt = np.linalg.svd(A)
+        H = Vt[:, -1, :].reshape(N, 3, 3)
+        H = H / H[:, 2:3, 2:3]
+
+        # Extract the 8 coefficients for each transformation
+        return np.column_stack(
+            [H[:, 0, 0], H[:, 0, 1], H[:, 0, 2], H[:, 1, 0], H[:, 1, 1], H[:, 1, 2], H[:, 2, 0], H[:, 2, 1]]
+        )  # pyright: ignore[reportReturnType]
+
     def configure(self, **kwargs: Any) -> None:
         self.min_score = kwargs.get("minScore", self.min_score)

machine-learning/immich_ml/models/ocr/schemas.py

@@ -7,7 +7,6 @@ from typing_extensions import TypedDict
 
 
 class TextDetectionOutput(TypedDict):
-    image: npt.NDArray[np.float32]
     boxes: npt.NDArray[np.float32]
     scores: npt.NDArray[np.float32]
 

machine-learning/pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "immich-ml"
-version = "2.2.2"
+version = "2.2.3"
 description = ""
 authors = [{ name = "Hau Tran", email = "alex.tran1502@gmail.com" }]
 requires-python = ">=3.10,<4.0"

misc/release/pump-version.sh

@@ -88,7 +88,6 @@ if [ "$CURRENT_MOBILE" != "$NEXT_MOBILE" ]; then
 fi
 
 sed -i "s/\"android\.injected\.version\.name\" => \"$CURRENT_SERVER\",/\"android\.injected\.version\.name\" => \"$NEXT_SERVER\",/" mobile/android/fastlane/Fastfile
-sed -i "s/version_number: \"$CURRENT_SERVER\"$/version_number: \"$NEXT_SERVER\"/" mobile/ios/fastlane/Fastfile
 sed -i "s/\"android\.injected\.version\.code\" => $CURRENT_MOBILE,/\"android\.injected\.version\.code\" => $NEXT_MOBILE,/" mobile/android/fastlane/Fastfile
 sed -i "s/^version: $CURRENT_SERVER+$CURRENT_MOBILE$/version: $NEXT_SERVER+$NEXT_MOBILE/" mobile/pubspec.yaml
 

mobile/android/fastlane/Fastfile

@@ -35,8 +35,8 @@ platform :android do
       task: 'bundle', 
       build_type: 'Release',
       properties: {
-        "android.injected.version.code" => 3025,
-        "android.injected.version.name" => "2.2.2",
+        "android.injected.version.code" => 3026,
+        "android.injected.version.name" => "2.2.3",
       }
     )
     upload_to_play_store(skip_upload_apk: true, skip_upload_images: true, skip_upload_screenshots: true, aab: '../build/app/outputs/bundle/release/app-release.aab')

mobile/ios/fastlane/Fastfile

@@ -32,6 +32,17 @@ platform :ios do
     )
   end
   
+  # Helper method to get version from pubspec.yaml
+def get_version_from_pubspec
+  require 'yaml'
+  
+  pubspec_path = File.join(Dir.pwd, "../..", "pubspec.yaml")
+  pubspec = YAML.load_file(pubspec_path)
+  
+  version_string = pubspec['version']
+  version_string ? version_string.split('+').first : nil
+end
+  
   # Helper method to configure code signing for all targets
   def configure_code_signing(bundle_id_suffix: "")
     bundle_suffix = bundle_id_suffix.empty? ? "" : ".#{bundle_id_suffix}"
@@ -158,7 +169,8 @@ platform :ios do
     # Build and upload with version number
     build_and_upload(
       api_key: api_key,
-      version_number: "2.1.0"
+      version_number: get_version_from_pubspec,
+      distribute_external: false,
     )
   end
 
@@ -168,8 +180,9 @@ platform :ios do
       path: "./Runner.xcodeproj",
       targets: ["Runner", "ShareExtension", "WidgetExtension"]
     )
+
     increment_version_number(
-      version_number: "2.2.2"
+      version_number: get_version_from_pubspec
     )
     increment_build_number(
       build_number: latest_testflight_build_number + 1,

mobile/lib/providers/server_info.provider.dart

@@ -67,7 +67,7 @@ class ServerInfoNotifier extends StateNotifier<ServerInfo> {
       return;
     }
 
-    if (clientVersion < serverVersion) {
+    if (clientVersion < serverVersion && clientVersion.differenceType(serverVersion) != SemVerType.patch) {
       state = state.copyWith(versionStatus: VersionStatus.clientOutOfDate);
       return;
     }

mobile/lib/repositories/asset_media.repository.dart

@@ -89,9 +89,16 @@ class AssetMediaRepository {
       return null;
     }
 
-    // titleAsync gets the correct original filename for some assets on iOS
-    // otherwise using the `entity.title` would return a random GUID
-    return await entity.titleAsync;
+    try {
+      // titleAsync gets the correct original filename for some assets on iOS
+      // otherwise using the `entity.title` would return a random GUID
+      final originalFilename = await entity.titleAsync;
+      // treat empty filename as missing
+      return originalFilename.isNotEmpty ? originalFilename : null;
+    } catch (e) {
+      _log.warning("Failed to get original filename for asset: $id. Error: $e");
+      return null;
+    }
   }
 
   // TODO: make this more efficient

mobile/lib/utils/semver.dart

@@ -1,3 +1,5 @@
+enum SemVerType { major, minor, patch }
+
 class SemVer {
   final int major;
   final int minor;
@@ -15,8 +17,20 @@ class SemVer {
   }
 
   factory SemVer.fromString(String version) {
+    if (version.toLowerCase().startsWith("v")) {
+      version = version.substring(1);
+    }
+
     final parts = version.split("-")[0].split('.');
-    return SemVer(major: int.parse(parts[0]), minor: int.parse(parts[1]), patch: int.parse(parts[2]));
+    if (parts.length != 3) {
+      throw FormatException('Invalid semantic version string: $version');
+    }
+
+    try {
+      return SemVer(major: int.parse(parts[0]), minor: int.parse(parts[1]), patch: int.parse(parts[2]));
+    } catch (e) {
+      throw FormatException('Invalid semantic version string: $version');
+    }
   }
 
   bool operator >(SemVer other) {
@@ -54,6 +68,20 @@ class SemVer {
     return other is SemVer && other.major == major && other.minor == minor && other.patch == patch;
   }
 
+  SemVerType? differenceType(SemVer other) {
+    if (major != other.major) {
+      return SemVerType.major;
+    }
+    if (minor != other.minor) {
+      return SemVerType.minor;
+    }
+    if (patch != other.patch) {
+      return SemVerType.patch;
+    }
+
+    return null;
+  }
+
   @override
   int get hashCode => major.hashCode ^ minor.hashCode ^ patch.hashCode;
 }

mobile/openapi/README.md

@@ -3,7 +3,7 @@ Immich API
 
 This Dart package is automatically generated by the [OpenAPI Generator](https://openapi-generator.tech) project:
 
-- API version: 2.2.2
+- API version: 2.2.3
 - Generator version: 7.8.0
 - Build package: org.openapitools.codegen.languages.DartClientCodegen
 

mobile/pubspec.yaml

@@ -2,7 +2,7 @@ name: immich_mobile
 description: Immich - selfhosted backup media file on mobile phone
 
 publish_to: 'none'
-version: 2.2.2+3025
+version: 2.2.3+3026
 
 environment:
   sdk: '>=3.8.0 <4.0.0'

mobile/scripts/fdroid_build_isar.sh

@@ -8,11 +8,11 @@ bash tool/build_android.sh x64
 bash tool/build_android.sh armv7
 bash tool/build_android.sh arm64
 mv libisar_android_arm64.so libisar.so
-mv libisar.so ../.pub-cache/hosted/pub.isar-community.dev/isar_flutter_libs-*/android/src/main/jniLibs/arm64-v8a/
+mv libisar.so ../.pub-cache/hosted/pub.dev/isar_community_flutter_libs-*/android/src/main/jniLibs/arm64-v8a/
 mv libisar_android_armv7.so libisar.so
-mv libisar.so ../.pub-cache/hosted/pub.isar-community.dev/isar_flutter_libs-*/android/src/main/jniLibs/armeabi-v7a/
+mv libisar.so ../.pub-cache/hosted/pub.dev/isar_community_flutter_libs-*/android/src/main/jniLibs/armeabi-v7a/
 mv libisar_android_x64.so libisar.so
-mv libisar.so ../.pub-cache/hosted/pub.isar-community.dev/isar_flutter_libs-*/android/src/main/jniLibs/x86_64/
+mv libisar.so ../.pub-cache/hosted/pub.dev/isar_community_flutter_libs-*/android/src/main/jniLibs/x86_64/
 mv libisar_android_x86.so libisar.so
-mv libisar.so ../.pub-cache/hosted/pub.isar-community.dev/isar_flutter_libs-*/android/src/main/jniLibs/x86/
-)
+mv libisar.so ../.pub-cache/hosted/pub.dev/isar_community_flutter_libs-*/android/src/main/jniLibs/x86/
+)

mobile/test/utils/semver_test.dart

@@ -0,0 +1,92 @@
+import 'package:flutter_test/flutter_test.dart';
+import 'package:immich_mobile/utils/semver.dart';
+
+void main() {
+  group('SemVer', () {
+    test('Parses valid semantic version strings correctly', () {
+      final version = SemVer.fromString('1.2.3');
+      expect(version.major, 1);
+      expect(version.minor, 2);
+      expect(version.patch, 3);
+    });
+
+    test('Throws FormatException for invalid version strings', () {
+      expect(() => SemVer.fromString('1.2'), throwsFormatException);
+      expect(() => SemVer.fromString('a.b.c'), throwsFormatException);
+      expect(() => SemVer.fromString('1.2.3.4'), throwsFormatException);
+    });
+
+    test('Compares equal versons correctly', () {
+      final v1 = SemVer.fromString('1.2.3');
+      final v2 = SemVer.fromString('1.2.3');
+      expect(v1 == v2, isTrue);
+      expect(v1 > v2, isFalse);
+      expect(v1 < v2, isFalse);
+    });
+
+    test('Compares major version correctly', () {
+      final v1 = SemVer.fromString('2.0.0');
+      final v2 = SemVer.fromString('1.9.9');
+      expect(v1 == v2, isFalse);
+      expect(v1 > v2, isTrue);
+      expect(v1 < v2, isFalse);
+    });
+
+    test('Compares minor version correctly', () {
+      final v1 = SemVer.fromString('1.3.0');
+      final v2 = SemVer.fromString('1.2.9');
+      expect(v1 == v2, isFalse);
+      expect(v1 > v2, isTrue);
+      expect(v1 < v2, isFalse);
+    });
+
+    test('Compares patch version correctly', () {
+      final v1 = SemVer.fromString('1.2.4');
+      final v2 = SemVer.fromString('1.2.3');
+      expect(v1 == v2, isFalse);
+      expect(v1 > v2, isTrue);
+      expect(v1 < v2, isFalse);
+    });
+
+    test('Gives correct major difference type', () {
+      final v1 = SemVer.fromString('2.0.0');
+      final v2 = SemVer.fromString('1.9.9');
+      expect(v1.differenceType(v2), SemVerType.major);
+    });
+
+    test('Gives correct minor difference type', () {
+      final v1 = SemVer.fromString('1.3.0');
+      final v2 = SemVer.fromString('1.2.9');
+      expect(v1.differenceType(v2), SemVerType.minor);
+    });
+
+    test('Gives correct patch difference type', () {
+      final v1 = SemVer.fromString('1.2.4');
+      final v2 = SemVer.fromString('1.2.3');
+      expect(v1.differenceType(v2), SemVerType.patch);
+    });
+
+    test('Gives null difference type for equal versions', () {
+      final v1 = SemVer.fromString('1.2.3');
+      final v2 = SemVer.fromString('1.2.3');
+      expect(v1.differenceType(v2), isNull);
+    });
+
+    test('toString returns correct format', () {
+      final version = SemVer.fromString('1.2.3');
+      expect(version.toString(), '1.2.3');
+    });
+
+    test('Parses versions with leading v correctly', () {
+      final version1 = SemVer.fromString('v1.2.3');
+      expect(version1.major, 1);
+      expect(version1.minor, 2);
+      expect(version1.patch, 3);
+
+      final version2 = SemVer.fromString('V1.2.3');
+      expect(version2.major, 1);
+      expect(version2.minor, 2);
+      expect(version2.patch, 3);
+    });
+  });
+}

open-api/immich-openapi-specs.json

@@ -10006,7 +10006,7 @@
   "info": {
     "title": "Immich",
     "description": "Immich API",
-    "version": "2.2.2",
+    "version": "2.2.3",
     "contact": {}
   },
   "tags": [],

open-api/typescript-sdk/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@immich/sdk",
-  "version": "2.2.2",
+  "version": "2.2.3",
   "description": "Auto-generated TypeScript SDK for the Immich API",
   "type": "module",
   "main": "./build/index.js",

open-api/typescript-sdk/src/fetch-client.ts

@@ -1,6 +1,6 @@
 /**
  * Immich
- * 2.2.2
+ * 2.2.3
  * DO NOT MODIFY - This file has been generated using oazapfts.
  * See https://www.npmjs.com/package/oazapfts
  */

server/package.json

@@ -1,6 +1,6 @@
 {
   "name": "immich",
-  "version": "2.2.2",
+  "version": "2.2.3",
   "description": "",
   "author": "",
   "private": true,

server/src/services/metadata.service.ts

@@ -236,8 +236,8 @@ export class MetadataService extends BaseService {
       latitude: number | null = null,
       longitude: number | null = null;
     if (this.hasGeo(exifTags)) {
-      latitude = exifTags.GPSLatitude;
-      longitude = exifTags.GPSLongitude;
+      latitude = Number(exifTags.GPSLatitude);
+      longitude = Number(exifTags.GPSLongitude);
       if (reverseGeocoding.enabled) {
         geo = await this.mapRepository.reverseGeocode({ latitude, longitude });
       }
@@ -894,12 +894,10 @@ export class MetadataService extends BaseService {
     };
   }
 
-  private hasGeo(tags: ImmichTags): tags is ImmichTags & { GPSLatitude: number; GPSLongitude: number } {
-    return (
-      tags.GPSLatitude !== undefined &&
-      tags.GPSLongitude !== undefined &&
-      (tags.GPSLatitude !== 0 || tags.GPSLatitude !== 0)
-    );
+  private hasGeo(tags: ImmichTags) {
+    const lat = Number(tags.GPSLatitude);
+    const lng = Number(tags.GPSLongitude);
+    return !Number.isNaN(lat) && !Number.isNaN(lng) && (lat !== 0 || lng !== 0);
   }
 
   private getAutoStackId(tags: ImmichTags | null): string | null {

web/package.json

@@ -1,6 +1,6 @@
 {
   "name": "immich-web",
-  "version": "2.2.2",
+  "version": "2.2.3",
   "license": "GNU Affero General Public License version 3",
   "type": "module",
   "scripts": {

web/src/lib/components/shared-components/search-bar/search-bar.svelte

@@ -30,10 +30,10 @@
   let showSuggestions = $state(false);
   let isSearchSuggestions = $state(false);
   let selectedId: string | undefined = $state();
-  let isFocus = $state(false);
   let close: (() => Promise<void>) | undefined;
 
   const listboxId = generateId();
+  const searchTypeId = generateId();
 
   onDestroy(() => {
     searchStore.isSearchEnabled = false;
@@ -161,12 +161,10 @@
 
   const openDropdown = () => {
     showSuggestions = true;
-    isFocus = true;
   };
 
   const closeDropdown = () => {
     showSuggestions = false;
-    isFocus = false;
     searchHistoryBox?.clearSelection();
   };
 
@@ -251,6 +249,7 @@
         aria-activedescendant={selectedId ?? ''}
         aria-expanded={showSuggestions && isSearchSuggestions}
         aria-autocomplete="list"
+        aria-describedby={searchTypeId}
         use:shortcuts={[
           { shortcut: { key: 'Escape' }, onShortcut: onEscape },
           { shortcut: { ctrl: true, shift: true, key: 'k' }, onShortcut: onFilterClick },
@@ -287,12 +286,12 @@
       />
     </div>
 
-    {#if isFocus}
+    {#if searchStore.isSearchEnabled}
       <div
-        class="absolute inset-y-0 flex items-center"
+        id={searchTypeId}
+        class="absolute inset-y-0 flex items-center end-16"
         class:max-md:hidden={value}
-        class:end-16={isFocus}
-        class:end-28={isFocus && value.length > 0}
+        class:end-28={value.length > 0}
       >
         <p
           class="bg-immich-primary text-white dark:bg-immich-dark-primary/90 dark:text-black/75 rounded-full px-3 py-1 text-xs"