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feat: Update detection module output to datalasses

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yakhyo
2025-12-22 19:25:38 +09:00
parent 96306a0910
commit 9bcbfa65c2
18 changed files with 165 additions and 189 deletions
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33
QUICKSTART.md
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@@ -39,9 +39,9 @@ faces = detector.detect(image)
# Print results
for i, face in enumerate(faces):
print(f"Face {i+1}:")
print(f" Confidence: {face['confidence']:.2f}")
print(f" BBox: {face['bbox']}")
print(f" Landmarks: {len(face['landmarks'])} points")
print(f" Confidence: {face.confidence:.2f}")
print(f" BBox: {face.bbox}")
print(f" Landmarks: {len(face.landmarks)} points")
```
**Output:**
@@ -70,9 +70,9 @@ image = cv2.imread("photo.jpg")
faces = detector.detect(image)
# Extract visualization data
bboxes = [f['bbox'] for f in faces]
scores = [f['confidence'] for f in faces]
landmarks = [f['landmarks'] for f in faces]
bboxes = [f.bbox for f in faces]
scores = [f.confidence for f in faces]
landmarks = [f.landmarks for f in faces]
# Draw on image
draw_detections(
@@ -113,8 +113,8 @@ faces2 = detector.detect(image2)
if faces1 and faces2:
# Extract embeddings
emb1 = recognizer.get_normalized_embedding(image1, faces1[0]['landmarks'])
emb2 = recognizer.get_normalized_embedding(image2, faces2[0]['landmarks'])
emb1 = recognizer.get_normalized_embedding(image1, faces1[0].landmarks)
emb2 = recognizer.get_normalized_embedding(image2, faces2[0].landmarks)
# Compute similarity (cosine similarity)
similarity = np.dot(emb1, emb2.T)[0][0]
@@ -159,9 +159,9 @@ while True:
faces = detector.detect(frame)
# Draw results
bboxes = [f['bbox'] for f in faces]
scores = [f['confidence'] for f in faces]
landmarks = [f['landmarks'] for f in faces]
bboxes = [f.bbox for f in faces]
scores = [f.confidence for f in faces]
landmarks = [f.landmarks for f in faces]
draw_detections(
image=frame,
bboxes=bboxes,
@@ -199,7 +199,7 @@ faces = detector.detect(image)
# Predict attributes
for i, face in enumerate(faces):
gender, age = age_gender.predict(image, face['bbox'])
gender, age = age_gender.predict(image, face.bbox)
gender_str = 'Female' if gender == 0 else 'Male'
print(f"Face {i+1}: {gender_str}, {age} years old")
```
@@ -230,7 +230,7 @@ image = cv2.imread("photo.jpg")
faces = detector.detect(image)
if faces:
landmarks = landmarker.get_landmarks(image, faces[0]['bbox'])
landmarks = landmarker.get_landmarks(image, faces[0].bbox)
print(f"Detected {len(landmarks)} landmarks")
# Draw landmarks
@@ -262,8 +262,7 @@ faces = detector.detect(image)
# Estimate gaze for each face
for i, face in enumerate(faces):
bbox = face['bbox']
x1, y1, x2, y2 = map(int, bbox[:4])
x1, y1, x2, y2 = map(int, face.bbox[:4])
face_crop = image[y1:y2, x1:x2]
if face_crop.size > 0:
@@ -271,7 +270,7 @@ for i, face in enumerate(faces):
print(f"Face {i+1}: pitch={np.degrees(pitch):.1f}°, yaw={np.degrees(yaw):.1f}°")
# Draw gaze direction
draw_gaze(image, bbox, pitch, yaw)
draw_gaze(image, face.bbox, pitch, yaw)
cv2.imwrite("gaze_output.jpg", image)
```
@@ -435,7 +434,7 @@ image = cv2.imread("photo.jpg")
faces = detector.detect(image)
for i, face in enumerate(faces):
label_idx, score = spoofer.predict(image, face['bbox'])
label_idx, score = spoofer.predict(image, face.bbox)
# label_idx: 0 = Fake, 1 = Real
label = 'Real' if label_idx == 1 else 'Fake'
print(f"Face {i+1}: {label} ({score:.1%})")