first phrase

2025-12-23 00:02:36 +08:00
commit 6e882d2aa4
14 changed files with 721 additions and 0 deletions
--- a/reproject/new_emotion_test.py
+++ b/reproject/new_emotion_test.py
@@ -0,0 +1,109 @@
+import cv2
+import numpy as np
+import math
+from hsemotion_onnx.facial_emotions import HSEmotionRecognizer
+
+EMOTION_VA_MAP = {
+    'happy':      (0.85,  0.60),
+    'sad':        (-0.75, -0.60),
+    'angry':      (-0.70,  0.80),
+    'fear':       (-0.65,  0.75),
+    'surprise':   (0.20,   0.85),
+    'disgust':    (-0.80,  0.40),
+    'neutral':    (0.00,   0.00),
+    'contempt':   (-0.60,  0.50),
+}
+
+EMOTION_HANDLE = {
+    'happiness': 'happy',
+    'sadness': 'sad',
+    'anger': 'angry',
+    'fear': 'fear',
+    'surprise': 'surprise',
+    'disgust': 'disgust',
+    'neutral': 'neutral',
+    'contempt': 'contempt',
+}
+
+def get_fine_grained_emotion(valence, arousal):
+    radius = math.sqrt(valence**2 + arousal**2)
+    angle = math.degrees(math.atan2(arousal, valence))
+    
+    if radius < 0.25:
+        return "neutral"
+
+    if 0 <= angle < 90:
+        if angle > 60: return "excited"
+        elif angle > 30: return "happy"
+        else: return "pleased"
+    elif 90 <= angle <= 180:
+        if angle > 150: return "nervous"
+        elif angle > 120: return "angry"
+        else: return "annoying"
+    elif -180 <= angle < -90:
+        if angle < -150: return "sad"
+        elif angle < -120: return "bored"
+        else: return "sleepy"
+    elif -90 <= angle < 0:
+        if angle < -60: return "calm"
+        elif angle < -30: return "peaceful"
+        else: return "relaxed"
+            
+    return "neutral"
+
+class EmotionAnalyzer:
+    def __init__(self):
+        print("正在加载 HSEmotion-ONNX 模型...")
+        self.fer = HSEmotionRecognizer(model_name='enet_b0_8_best_vgaf')
+        print("HSEmotion-ONNX 模型加载完成")
+
+    def calculate_va_score(self, emotion_prob):
+        valence_sum = 0.0
+        arousal_sum = 0.0
+        total_prob = 0.0
+        
+        for emotion, prob in emotion_prob.items():
+            key = EMOTION_HANDLE.get(emotion.lower(), emotion.lower())
+            if key in EMOTION_VA_MAP:
+                v, a = EMOTION_VA_MAP[key]
+                valence_sum += v * prob
+                arousal_sum += a * prob
+                total_prob += prob
+            
+        if total_prob == 0:
+            return 0.0, 0.0
+        return valence_sum, arousal_sum
+
+    def analyze(self, face_img_bgr):
+        if face_img_bgr is None or face_img_bgr.size == 0:
+            return []
+
+        face_img_rgb = cv2.cvtColor(face_img_bgr, cv2.COLOR_BGR2RGB)
+
+        # predict_emotions 返回主要情绪标签和概率数组
+        emotion_raw, scores = self.fer.predict_emotions(face_img_rgb, logits=False)
+        
+        probabilities = {}
+        for idx, score in enumerate(scores):
+            raw_label = self.fer.idx_to_class[idx]
+            key = EMOTION_HANDLE.get(raw_label.lower(), raw_label.lower()) 
+            
+            probabilities[key] = float(score)
+
+        valence, arousal = self.calculate_va_score(probabilities)
+        fine_grained_label = get_fine_grained_emotion(valence, arousal)
+
+        result = {
+            "box": {}, 
+            "vaVal": (round(valence, 4), round(arousal, 4)),
+            "probabilities": probabilities,
+            "dominant_emotion": EMOTION_HANDLE.get(emotion_raw.lower(), emotion_raw.lower()),
+            "emotion": fine_grained_label
+        }
+        
+        return [result]
+
+analyzer_instance = EmotionAnalyzer()
+
+def analyze_emotion_with_hsemotion(face_crop_bgr):
+    return analyzer_instance.analyze(face_crop_bgr)