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add data processing scripts and ffhq dataset; implement image-label mapping and visualization
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import json
def json_element_cnt(json_file:str):
with open(json_file, 'r', encoding='utf-8') as file:
data = json.load(file)
return len(data)
def parse_face_attributes(json_file:str):
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. 笑容:若 smile 大於 0.1 則視為 "smiling",否則 "no smile"
smile_value = face_attr.get("smile", 0)
result['smile'] = "smiling" if smile_value > 0.1 else "no smile"
# 2. 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:大於 5 為 "upward",小於 -5 為 "downward",否則 "neutral"
if pitch > 5:
pitch_cat = "upward"
elif pitch < -5:
pitch_cat = "downward"
else:
pitch_cat = "neutral"
# roll:絕對值大於 5 為 "tilted",否則 "neutral"
roll_cat = "tilted" if abs(roll) > 5 else "neutral"
# yaw:大於 15 為 "turned right",小於 -15 為 "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)
result["gender"] = face_attr.get("gender", "unknown")
# 4. 年齡 (age):依年齡數值分類
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):若各項均不超過 0.1 則為 "none"
facial_hair = face_attr.get("facialHair", {})
hair_types = []
for key, value in facial_hair.items():
if value > 0.1:
hair_types.append(key)
result["facialHair"] = "none" if not hair_types else ", ".join(hair_types)
# 6. 眼鏡狀態 (glasses)
result["glasses"] = face_attr.get("glasses", "NoGlasses")
# 7. 情緒 (emotion):取分數最高的情緒
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):以 blurLevel 為類別
blur = face_attr.get("blur", {})
result["blur"] = blur.get("blurLevel", "unknown")
# 9. 曝光 (exposure):以 exposureLevel 為類別
exposure = face_attr.get("exposure", {})
result["exposure"] = exposure.get("exposureLevel", "unknown")
# 10. 噪音 (noise):以 noiseLevel 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):若無配件則回傳 "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 則為 "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