CI: Apply Go more linter recommendations to "ai/face" package #5330

Signed-off-by: Michael Mayer <michael@photoprism.app>

internal/ai/face/detector.go

@@ -3,7 +3,7 @@ package face
 import (
 	_ "embed"
 	"fmt"
-	_ "image/jpeg"
+	_ "image/jpeg" // register JPEG decoder for pigo image decoding
 	"os"
 	"path/filepath"
 	"runtime/debug"
@@ -146,7 +146,7 @@ func (d *pigoDetector) Detect(fileName string) (faces []pigo.Detection, params p
 		d.angles = append([]float64(nil), DetectionAngles...)
 	}
 
-	file, err := os.Open(fileName)
+	file, err := os.Open(fileName) //nolint:gosec // fileName comes from caller; reading local images is expected
 
 	if err != nil {
 		return faces, params, err
@@ -169,11 +169,12 @@ func (d *pigoDetector) Detect(fileName string) (faces []pigo.Detection, params p
 
 	var maxSize int
 
-	if cols < 20 || rows < 20 || cols < d.minSize || rows < d.minSize {
+	switch {
+	case cols < 20 || rows < 20 || cols < d.minSize || rows < d.minSize:
 		return faces, params, fmt.Errorf("image size %dx%d is too small", cols, rows)
-	} else if cols < rows {
+	case cols < rows:
 		maxSize = cols - 4
-	} else {
+	default:
 		maxSize = rows - 4
 	}
 
@@ -370,8 +371,8 @@ func (d *pigoDetector) Faces(det []pigo.Detection, params pigo.CascadeParams, fi
 
 		// Create face.
 		f := Face{
-			Rows:      params.ImageParams.Rows,
-			Cols:      params.ImageParams.Cols,
+			Rows:      params.Rows,
+			Cols:      params.Cols,
 			Score:     int(face.Q),
 			Area:      faceCoord,
 			Eyes:      eyesCoords,

internal/ai/face/embedding.go

@@ -10,6 +10,7 @@ import (
 // Embedding represents a face embedding.
 type Embedding []float64
 
+// NullEmbedding is a zero-value placeholder embedding used when no data is available.
 var NullEmbedding = make(Embedding, 512)
 
 // NewEmbedding creates a new embedding from an inference result.

internal/ai/face/embeddings_random.go

@@ -4,12 +4,17 @@ import (
 	"math/rand/v2"
 )
 
+// Kind identifies the type of embedding.
 type Kind int
 
 const (
+	// RegularFace represents a standard face embedding.
 	RegularFace Kind = iota + 1
+	// ChildrenFace represents a child face embedding.
 	ChildrenFace
+	// BackgroundFace represents non-face/background embeddings.
 	BackgroundFace
+	// AmbiguousFace represents embeddings that should be treated as uncertain.
 	AmbiguousFace
 )
 
@@ -20,7 +25,7 @@ func RandomDist() float64 {
 
 // RandomFloat64 adds a random distance offset to a float64.
 func RandomFloat64(f, d float64) float64 {
-	return f + (rand.Float64()-0.5)*d
+	return f + (rand.Float64()-0.5)*d //nolint:gosec // pseudo-random is sufficient for test fixtures
 }
 
 // RandomEmbeddings returns random embeddings for testing.
@@ -76,7 +81,7 @@ func RandomChildrenEmbedding() (result Embedding) {
 	}
 
 	d := 0.1 / 512.0
-	n := rand.IntN(len(Children))
+	n := rand.IntN(len(Children)) //nolint:gosec // deterministic seeding not required for synthetic embeddings
 	e := Children[n].Embedding
 
 	for i := range result {
@@ -97,7 +102,7 @@ func RandomBackgroundEmbedding() (result Embedding) {
 	}
 
 	d := 0.1 / 512.0
-	n := rand.IntN(len(Background))
+	n := rand.IntN(len(Background)) //nolint:gosec // deterministic seeding not required for synthetic embeddings
 	e := Background[n].Embedding
 
 	for i := range result {

internal/ai/face/engine.go

@@ -7,12 +7,17 @@ import (
 	"sync"
 )
 
+// EngineName identifies a face detection engine implementation.
 type EngineName = string
 
 const (
+	// EngineAuto selects the default engine based on availability.
 	EngineAuto EngineName = "auto"
+	// EnginePigo enables the built-in Pigo cascade detector.
 	EnginePigo EngineName = "pigo"
+	// EngineONNX enables the ONNX runtime-powered SCRFD detector.
 	EngineONNX EngineName = "onnx"
+	// EngineNone disables face detection.
 	EngineNone EngineName = "none"
 )
 

internal/ai/face/engine_onnx.go

@@ -4,7 +4,7 @@ import (
 	"errors"
 	"fmt"
 	"image"
-	_ "image/jpeg"
+	_ "image/jpeg" // register JPEG decoder for ONNX engine input
 	"math"
 	"os"
 	"path/filepath"
@@ -17,7 +17,7 @@ import (
 	onnxruntime "github.com/yalue/onnxruntime_go"
 )
 
-// ONNXOptions configures how the ONNX runtime-backed detector is initialised.
+// ONNXOptions configures how the ONNX runtime-backed detector is initialized.
 type ONNXOptions struct {
 	ModelPath      string
 	LibraryPath    string
@@ -27,6 +27,7 @@ type ONNXOptions struct {
 }
 
 const (
+	// DefaultONNXModelFilename is the bundled ONNX model name used when none is provided.
 	DefaultONNXModelFilename  = "scrfd.onnx"
 	onnxDefaultScoreThreshold = 0.50
 	onnxDefaultNMSThreshold   = 0.40
@@ -307,7 +308,7 @@ func (o *onnxEngine) Close() error {
 
 // Detect identifies faces in the provided image using the ONNX runtime session.
 func (o *onnxEngine) Detect(fileName string, findLandmarks bool, minSize int) (Faces, error) {
-	file, err := os.Open(fileName)
+	file, err := os.Open(fileName) //nolint:gosec // fileName provided by caller; reading local images is required for detection
 	if err != nil {
 		return Faces{}, err
 	}
@@ -437,9 +438,9 @@ func (o *onnxEngine) buildBlob(img image.Image) ([]float32, float32, error) {
 			var r, g, b float32
 			if x < newWidth && y < newHeight {
 				cr, cg, cb, _ := resized.At(x, y).RGBA()
-				r = float32(uint8(cr >> 8))
-				g = float32(uint8(cg >> 8))
-				b = float32(uint8(cb >> 8))
+				r = float32((cr >> 8) & 0xff)
+				g = float32((cg >> 8) & 0xff)
+				b = float32((cb >> 8) & 0xff)
 			}
 
 			blob[idx] = (r - onnxInputMean) / onnxInputStd

internal/ai/face/model.go

@@ -86,7 +86,7 @@ func (m *Model) Detect(fileName string, minSize int, cacheCrop bool, expected in
 	return faces, nil
 }
 
-// Init initialises tensorflow models if not disabled
+// Init initializes tensorflow models if not disabled.
 func (m *Model) Init() (err error) {
 	if m.disabled {
 		return nil
@@ -139,6 +139,10 @@ func (m *Model) Run(img image.Image) Embeddings {
 	// TODO: pre-whiten image as in facenet
 
 	trainPhaseBoolTensor, err := tf.NewTensor(false)
+	if err != nil {
+		log.Errorf("faces: failed to create phase_train tensor: %s", err)
+		return nil
+	}
 
 	output, err := m.model.Session.Run(
 		map[tf.Output]*tf.Tensor{

internal/ai/face/model_test.go

@@ -123,11 +123,11 @@ func TestNet(t *testing.T) {
 			t.Logf("Dist for %d %d (faces are %d %d) is %f", i, j, faceIndexToPersonID[i], faceIndexToPersonID[j], dist)
 			if faceIndexToPersonID[i] == faceIndexToPersonID[j] {
 				if dist < 1.21 {
-					correct += 1
+					correct++
 				}
 			} else {
 				if dist >= 1.21 {
-					correct += 1
+					correct++
 				}
 			}
 		}