add face attribute parsing functionality and example JSON data

main/ex.json

@@ -0,0 +1,89 @@
+[
+    {
+        "faceId": "866db6af-66ee-4828-b565-994f42571d2f",
+        "faceRectangle": {
+            "top": 42,
+            "left": 32,
+            "width": 63,
+            "height": 63
+        },
+        "faceAttributes": {
+            "smile": 0.003,
+            "headPose": {
+                "pitch": 5.4,
+                "roll": 1.5,
+                "yaw": 15.3
+            },
+            "gender": "female",
+            "age": 0.0,
+            "facialHair": {
+                "moustache": 0.0,
+                "beard": 0.0,
+                "sideburns": 0.0
+            },
+            "glasses": "NoGlasses",
+            "emotion": {
+                "anger": 0.0,
+                "contempt": 0.0,
+                "disgust": 0.0,
+                "fear": 0.0,
+                "happiness": 0.003,
+                "neutral": 0.993,
+                "sadness": 0.003,
+                "surprise": 0.0
+            },
+            "blur": {
+                "blurLevel": "medium",
+                "value": 0.55
+            },
+            "exposure": {
+                "exposureLevel": "goodExposure",
+                "value": 0.6
+            },
+            "noise": {
+                "noiseLevel": "medium",
+                "value": 0.44
+            },
+            "makeup": {
+                "eyeMakeup": false,
+                "lipMakeup": false
+            },
+            "accessories": [],
+            "occlusion": {
+                "foreheadOccluded": false,
+                "eyeOccluded": false,
+                "mouthOccluded": false
+            },
+            "hair": {
+                "bald": 0.07,
+                "invisible": false,
+                "hairColor": [
+                    {
+                        "color": "brown",
+                        "confidence": 0.93
+                    },
+                    {
+                        "color": "blond",
+                        "confidence": 0.8
+                    },
+                    {
+                        "color": "black",
+                        "confidence": 0.41
+                    },
+                    {
+                        "color": "red",
+                        "confidence": 0.28
+                    },
+                    {
+                        "color": "gray",
+                        "confidence": 0.22
+                    },
+                    {
+                        "color": "other",
+                        "confidence": 0.2
+                    }
+                ]
+            }
+        }
+    }
+]

main/main.py

@@ -1 +1,158 @@
-import os
+import json
+import os
+import sys
+
+def parse_face_attributes(json_file):
+    with open(json_file, 'r', encoding='utf-8') as file:
+        data = json.load(file)
+    
+    data = data[0]
+
+    # 取出 faceAttributes 部分
+    face_attr = data.get('faceAttributes', {})
+    
+    result = {}
+    
+    # 1. 處理笑容：根據 smile 值轉換為類別
+    # 若 smile 大於 0.1 則視為「smiling」，否則視為「no smile」
+    smile_value = face_attr.get("smile", 0)
+    result['smile'] = "smiling" if smile_value > 0.1 else "no smile"
+    
+    # 2. 處理頭部姿勢 (headPose)
+    head_pose = face_attr.get("headPose", {})
+    pitch = head_pose.get("pitch", 0)
+    roll = head_pose.get("roll", 0)
+    yaw = head_pose.get("yaw", 0)
+    
+    # pitch：若大於 5 則「upward」，小於 -5 則「downward」，否則「neutral」
+    if pitch > 5:
+        pitch_cat = "upward"
+    elif pitch < -5:
+        pitch_cat = "downward"
+    else:
+        pitch_cat = "neutral"
+    
+    # roll：若絕對值大於 5 則「tilted」，否則「neutral」
+    roll_cat = "tilted" if abs(roll) > 5 else "neutral"
+    
+    # yaw：若大於 15 則「turned right」，小於 -15 則「turned left」，否則「frontal」
+    if yaw > 15:
+        yaw_cat = "turned right"
+    elif yaw < -15:
+        yaw_cat = "turned left"
+    else:
+        yaw_cat = "frontal"
+    
+    result['headPose'] = {"pitch": pitch_cat, "roll": roll_cat, "yaw": yaw_cat}
+    
+    # 3. 性別 (gender) 為類別型資料，直接取用
+    result["gender"] = face_attr.get("gender", "unknown")
+    
+    # 4. 年齡 (age)：將數值轉換為類別
+    # 定義閾值：小於 2 歲視為 "baby"，2-10 歲視為 "child"，10-20 歲視為 "teenager"，20-60 歲視為 "adult"，60 歲以上視為 "senior"
+    age = face_attr.get("age", 0)
+    if age < 2:
+        age_cat = "baby"
+    elif age < 10:
+        age_cat = "child"
+    elif age < 20:
+        age_cat = "teenager"
+    elif age < 60:
+        age_cat = "adult"
+    else:
+        age_cat = "senior"
+    result["age"] = age_cat
+    
+    # 5. 面部毛髮 (facialHair)：檢查各項是否大於 0.1，若皆不超過則視為 "none"
+    facial_hair = face_attr.get("facialHair", {})
+    hair_types = []
+    for key, value in facial_hair.items():
+        if value > 0.1:  # 可調整閾值
+            hair_types.append(key)
+    result["facialHair"] = "none" if not hair_types else ", ".join(hair_types)
+    
+    # 6. 眼鏡狀態 (glasses) 直接取用原始資料
+    result["glasses"] = face_attr.get("glasses", "NoGlasses")
+    
+    # 7. 情緒 (emotion)：取出分數最高的情緒作為類別
+    emotion = face_attr.get("emotion", {})
+    if emotion:
+        emotion_category = max(emotion, key=emotion.get)
+    else:
+        emotion_category = "unknown"
+    result["emotion"] = emotion_category
+    
+    # 8. 模糊度 (blur)：採用 blurLevel 作為類別
+    blur = face_attr.get("blur", {})
+    result["blur"] = blur.get("blurLevel", "unknown")
+    
+    # 9. 曝光 (exposure)：採用 exposureLevel 作為類別
+    exposure = face_attr.get("exposure", {})
+    result["exposure"] = exposure.get("exposureLevel", "unknown")
+    
+    # 10. 噪音 (noise)：採用 noiseLevel 作為類別
+    noise = face_attr.get("noise", {})
+    result["noise"] = noise.get("noiseLevel", "unknown")
+    
+    # 11. 妝容 (makeup)：根據 eyeMakeup 與 lipMakeup 判斷
+    makeup = face_attr.get("makeup", {})
+    makeup_categories = []
+    if makeup.get("eyeMakeup", False):
+        makeup_categories.append("eye makeup")
+    if makeup.get("lipMakeup", False):
+        makeup_categories.append("lip makeup")
+    result["makeup"] = "no makeup" if not makeup_categories else ", ".join(makeup_categories)
+    
+    # 12. 配件 (accessories)：若空則回傳 "none"
+    accessories = face_attr.get("accessories", [])
+    result["accessories"] = "none" if not accessories else ", ".join([acc.get("type", "unknown") for acc in accessories])
+    
+    # 13. 遮擋 (occlusion)：若皆為 False 則 "none"，否則列出被遮擋的部位
+    occlusion = face_attr.get("occlusion", {})
+    occlusion_list = [k for k, v in occlusion.items() if v]
+    result["occlusion"] = "none" if not occlusion_list else ", ".join(occlusion_list)
+    
+    # 14. 頭髮 (hair)：若 bald 大於 0.5 則視為 bald，否則取 hairColor 中信心最高的顏色
+    hair = face_attr.get("hair", {})
+    if hair:
+        if hair.get("bald", 0) > 0.5:
+            hair_cat = "bald"
+        else:
+            hair_colors = hair.get("hairColor", [])
+            if hair_colors:
+                dominant_color = max(hair_colors, key=lambda x: x.get("confidence", 0))["color"]
+            else:
+                dominant_color = "unknown"
+            hair_cat = dominant_color
+    else:
+        hair_cat = "unknown"
+    result["hair"] = hair_cat
+    
+    return result
+
+def test_json(file_path):
+    with open(file_path, 'r', encoding='utf-8') as file:
+        data = json.load(file)
+    return len(data)
+
+# 範例使用方式：
+if __name__ == "__main__":
+    from pprint import pprint
+    # 假設 JSON 檔案名稱為 "face_data.json"
+    parsed_data = parse_face_attributes("/root/siglip-recursion-ffhq-thumbnails/main/temp_data/ffhq-features-dataset/json/01111.json")
+    pprint(parsed_data)
+    # file_list = os.listdir("/root/siglip-recursion-ffhq-thumbnails/main/temp_data/ffhq-features-dataset/json")
+    # cnt = {}
+    # outlier = {}
+    # for file in file_list:
+    #     file_path = os.path.join("/root/siglip-recursion-ffhq-thumbnails/main/temp_data/ffhq-features-dataset/json", file)
+    #     length = test_json(file_path)
+    #     if length in cnt:
+    #         cnt[length] += 1
+    #     else:
+    #         cnt[length] = 1
+    #     if length > 1:
+    #         outlier[file] = length
+    # print(outlier)
+    # print(cnt)
+    # print(f"total: {sum(cnt.values())}")