如何使用deepFace批量创建有创意的GIF图片表情包？
是否你也想知道这样有意思的表情包在哪里下载的？
聊天的时候找啊找，感觉找不到有用的表情包来表达你此刻的心情？
今天我们用python deepFace库来实现一个读取视频，并且根据提供的面部特征来批量生成有意思的表情包的工具。。。。。。
依赖库
numpy==1.22.3 pandas==2.0.3 Pillow==9.0.0 opencv-python==4.9.0.80 tensorflow==2.13.1 keras==2.13.1 requests==2.27.1 gdown==4.2.0 tqdm==4.66.1 Flask==2.0.2 Werkzeug==2.0.2 flask_cors==4.0.1 mtcnn==0.1.1 retina-face==0.0.17 fire==0.4.0 gunicorn==20.1.0 lightphe==0.0.19
这里可能会有numpy 的兼容问题，
我们修改代码 numpy 库的 __init__.py 中的def __getattr__(attr) 方法：
def __getattr__(attr): # Warn for expired attributes, and return a dummy function # that always raises an exception. print(f"attrattrattrattrattrattr:{attr}") import warnings try: msg = __expired_functions__[attr] except KeyError: pass else: warnings.warn(msg, DeprecationWarning, stacklevel=2) def _expired(*args, **kwds): raise RuntimeError(msg) return _expired # Emit warnings for deprecated attributes try: val, msg = __deprecated_attrs__[attr] except KeyError: pass else: warnings.warn(msg, DeprecationWarning, stacklevel=2) return val if attr in __future_scalars__: # And future warnings for those that will change, but also give # the AttributeError warnings.warn( f"In the future `np.{attr}` will be defined as the " "corresponding NumPy scalar.", FutureWarning, stacklevel=2) if attr in __former_attrs__: raise AttributeError(__former_attrs__[attr]) # Importing Tester requires importing all of UnitTest which is not a # cheap import Since it is mainly used in test suits, we lazy import it # here to save on the order of 10 ms of import time for most users # # The previous way Tester was imported also had a side effect of adding # the full `numpy.testing` namespace if attr == 'testing': import numpy.testing as testing return testing elif attr == 'Tester': from .testing import Tester return Tester elif attr == 'typeDict': import numpy as np # Note: we use dtype objects instead of dtype classes. The spec does not # require any behavior on dtypes other than equality. int8 = np.dtype("int8") int16 = np.dtype("int16") int32 = np.dtype("int32") int64 = np.dtype("int64") uint8 = np.dtype("uint8") uint16 = np.dtype("uint16") uint32 = np.dtype("uint32") uint64 = np.dtype("uint64") float32 = np.dtype("float32") float64 = np.dtype("float64") # Note: This name is changed bool = np.dtype("bool") typeDict = { # 整数类型 'int8': np.int8, 'int16': np.int16, 'int32': np.int32, 'int64': np.int64, 'uint8': np.uint8, 'uint16': np.uint16, 'uint32': np.uint32, 'uint64': np.uint64, # 浮点类型 'float16': np.float16, 'float32': np.float32, 'float64': np.float64, # 其他常用类型 'bool': np.bool_, 'str': np.str_, 'object': np.object_, } return typeDict raise AttributeError("module {!r} has no attribute " "{!r}".format(__name__, attr))
蓝色部分是新增的代码，兼容以前的库没有typeDict属性，导致报错的问题。
核心文件来了：
import cv2
import imageio
import numpy as np
import os
import time
import gc
from deepface import DeepFace
from PIL import Image, ImageDraw, ImageFont
# ===================== 兼容DeepFace版本的核心修复 =====================
# 兼容DeepFace新旧版本的距离计算导入
try:
# 新版本（≥0.0.79）
from deepface.commons.distance_utils import findCosineDistance
except ImportError:
try:
# 旧版本（<0.0.79）
from deepface.commons import distance as dst
findCosineDistance = dst.findCosineDistance
except ImportError:
# 终极兜底：手动实现余弦距离计算
def findCosineDistance(vector1, vector2):
vec1 = np.array(vector1) / np.linalg.norm(vector1)
vec2 = np.array(vector2) / np.linalg.norm(vector2)
return 1 - np.dot(vec1, vec2)
class TargetFaceGIFGenerator:
def __init__(self, video_path, target_face_path,
save_dir="./face_gifs",
similarity_threshold=0.65, # 人脸匹配阈值（0~1）
detect_backend="mtcnn", # 检测后端：mtcnn/ssd/opencv
gif_cache_seconds=3, # 检测到人脸后缓存多少秒的帧生成GIF
gif_fps=3, # GIF帧率（降低至3以延长显示时间）
gif_size=(400, 400), # GIF尺寸
cool_down_seconds=5): # 冷却时间（避免重复生成）
"""
初始化特定人脸GIF生成器
:param video_path: 待分析视频路径
:param target_face_path: 目标人脸图片路径（单/多人脸）
:param save_dir: GIF保存目录
:param similarity_threshold: 人脸匹配余弦相似度阈值
:param gif_cache_seconds: 检测到人脸后缓存的帧时长（秒）
:param cool_down_seconds: 生成GIF后冷却时间（避免短时间重复生成）
"""
# 视频初始化
self.cap = cv2.VideoCapture(video_path)
if not self.cap.isOpened():
raise ValueError(f"❌ 无法读取视频：{video_path}")
# 视频基础参数
self.fps = self.cap.get(cv2.CAP_PROP_FPS)
self.total_frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
self.total_duration = self.total_frames / self.fps
self.current_frame_idx = 0
self.current_sec = 0
# 人脸匹配配置
self.target_face_path = target_face_path
self.sim_threshold = similarity_threshold
self.detect_backend = detect_backend
self.target_embeddings = [] # 目标人脸特征库
self._load_target_face() # 加载目标人脸特征
# 修复：移除旧的Emotion模型加载，改用DeepFace.analyze直接分析表情
self.emotion_labels = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']
# 文言文风格表情文字映射表
self.emotion_text_map = {
'angry': '嗯？！',
'disgust': '哼！！',
'fear': 'Oh~ NO!',
'happy': '哈哈哈哈~~~',
'sad': 'SADDLY...',
'surprise': 'What??',
'neutral': 'enn hn ? little case !'
}
# GIF生成配置
self.gif_cache_frames = int(gif_cache_seconds * self.fps) # 缓存帧数
self.gif_fps = gif_fps
self.gif_size = gif_size
self.cool_down_frames = int(cool_down_seconds * self.fps) # 冷却帧数
self.last_generate_frame = -self.cool_down_frames # 上一次生成GIF的帧数
# 缓存与保存配置
self.save_dir = save_dir
self._create_save_dir()
self.frame_buffer = [] # 帧缓存队列（用于生成GIF）
self.generated_gif_count = 0
self.generated_gif_paths = []
def _create_save_dir(self):
"""创建GIF保存目录"""
if not os.path.exists(self.save_dir):
os.makedirs(self.save_dir)
print(f"📁 自动创建GIF目录：{self.save_dir}")
else:
print(f"📁 GIF保存目录：{self.save_dir}")
# def _load_target_face(self):
# """加载目标人脸并提取特征（支持多人脸）"""
# print(f"🔍 正在加载目标人脸：{self.target_face_path}")
# try:
# # 提取目标人脸的特征向量（Facenet模型轻量且精准）
# target_objs = DeepFace.represent(
# img_path=self.target_face_path,
# model_name="Facenet",
# detector_backend=self.detect_backend,
# enforce_detection=True
# )
# self.target_embeddings = [obj["embedding"] for obj in target_objs]
# print(f"✅ 成功加载 {len(self.target_embeddings)} 个人脸特征")
# if len(self.target_embeddings) == 0:
# raise ValueError("❌ 目标图片中未检测到人脸！")
# except Exception as e:
# raise RuntimeError(f"❌ 加载目标人脸失败：{str(e)}")
def _calc_cosine_similarity(self, embedding1, embedding2):
"""计算余弦相似度（值越大越相似，范围0~1）"""
embedding1 = np.array(embedding1)
embedding2 = np.array(embedding2)
# 修复：直接调用兼容后的findCosineDistance
return 1 - findCosineDistance(embedding1, embedding2)
def _analyze_face_emotion(self, face_img):
"""
修复版表情分析：使用DeepFace.analyze直接获取表情（兼容新版本）
:param face_img: 人脸区域图片（BGR格式）
:return: 最可能的表情标签
"""
try:
# 使用analyze接口直接分析表情，关闭其他分析项提升速度
analysis = DeepFace.analyze(
img_path=face_img,
actions=['emotion'], # 只分析表情，跳过年龄/性别/种族
detector_backend=self.detect_backend,
enforce_detection=False, # 小人脸也尝试分析
silent=True # 关闭冗余日志
)
# 兼容新版本返回格式（单人脸返回字典，多人脸返回列表）
if isinstance(analysis, list):
emotion = analysis[0]['dominant_emotion'] if analysis else 'neutral'
else:
emotion = analysis['dominant_emotion']
return emotion
except Exception as e:
print(f"⚠️ 表情分析失败（降级为平静）：{str(e)}")
return 'neutral'
def _match_face(self, frame):
"""
匹配当前帧中的人脸（修复表情分析逻辑）
:return: (is_matched, max_similarity, face_regions, emotions)
is_matched: 是否匹配目标人脸
max_similarity: 最高相似度值
face_regions: 匹配的人脸区域坐标 [(x1,y1,x2,y2), ...]
emotions: 匹配人脸的表情列表
"""
try:
# 提取当前帧的所有人脸特征
frame_faces = DeepFace.represent(
img_path=frame,
model_name="Facenet",
detector_backend=self.detect_backend,
enforce_detection=False # 无脸时不报错
)
if len(frame_faces) == 0:
return False, 0.0, [], []
# 逐一匹配目标人脸
max_sim = 0.0
matched_regions = []
matched_emotions = []
for face_obj in frame_faces:
frame_emb = face_obj["embedding"]
# 计算与所有目标人脸的相似度
sims = [self._calc_cosine_similarity(frame_emb, t_emb) for t_emb in self.target_embeddings]
current_sim = max(sims) # 取最高相似度
# 超过阈值则判定为匹配
if current_sim >= self.sim_threshold:
max_sim = max(max_sim, current_sim)
# 获取人脸区域坐标
facial_area = face_obj["facial_area"]
x1, y1 = facial_area["x"], facial_area["y"]
x2, y2 = x1 + facial_area["w"], y1 + facial_area["h"]
matched_regions.append((x1, y1, x2, y2))
# 修复：调用新的表情分析方法
face_img = frame[y1:y2, x1:x2]
emotion = self._analyze_face_emotion(face_img)
matched_emotions.append(emotion)
return max_sim >= self.sim_threshold, max_sim, matched_regions, matched_emotions
except Exception as e:
print(f"⚠️ 人脸匹配失败：{str(e)}")
return False, 0.0, [], []
def _draw_face_info_on_frame(self, frame, face_regions, similarity, emotions):
"""在单帧上绘制人脸框、相似度、时间戳和表情文字"""
# 转换为PIL（支持中文绘制）
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
pil_img = Image.fromarray(frame_rgb)
draw = ImageDraw.Draw(pil_img)
# 加载字体（Windows默认黑体，Mac/Linux替换为"/System/Library/Fonts/PingFang.ttc"）
try:
font_large = ImageFont.truetype("C:/Windows/Fonts/simhei.ttf", 24)
font_small = ImageFont.truetype("C:/Windows/Fonts/simhei.ttf", 20)
except:
font_large = ImageFont.load_default()
font_small = ImageFont.load_default()
print("⚠️ 无法加载中文字体，使用默认字体")
# 选择传入的主表情（如果有）
if emotions:
main_emotion = emotions[0]
biaoqing_text = self.emotion_text_map.get(main_emotion, "")
else:
biaoqing_text = ""
# 获取画面宽高（用于计算文字居中位置）
frame_width, frame_height = pil_img.size
# 计算文字尺寸和位置（底部居中）
text_bbox = draw.textbbox((0,0), biaoqing_text, font=font_small)
text_w = text_bbox[2] - text_bbox[0]
text_h = text_bbox[3] - text_bbox[1]
time_x = (frame_width - text_w) / 2 # 水平居中
time_y = frame_height - text_h - 10 # 底部向上10像素
text_color = (254,254, 254)
# 绘制表情文字
draw.text((time_x, time_y), biaoqing_text, fill=text_color, font=font_small)
# 转换回OpenCV格式
frame_marked = cv2.cvtColor(np.array(pil_img), cv2.COLOR_RGB2BGR)
return frame_marked
def _generate_gif_from_buffer(self, similarity):
"""从帧缓存生成GIF并保存（修复文字一闪而过问题）"""
# 确保至少有5帧才生成GIF，避免动画过短
if len(self.frame_buffer) < 5:
print("⚠️ 缓存帧数不足（至少需要5帧），跳过GIF生成")
self.frame_buffer.clear()
return
# 统计缓存帧中所有表情，取出现次数最多的作为主表情（稳定文字）
all_emotions = []
for _, _, emotions in self.frame_buffer:
if emotions: # 只收集有效表情
all_emotions.extend(emotions)
# 确定主表情（如果没有有效表情则用中性）
if all_emotions:
main_emotion = max(set(all_emotions), key=all_emotions.count)
else:
main_emotion = 'neutral'
main_emotion_text = self.emotion_text_map.get(main_emotion, "")
# 1. 帧预处理（优化采样逻辑，确保足够帧数量）
processed_frames = []
# 重新计算采样步长：确保最终有8-15帧（平衡流畅度和体积）
max_frames_needed = 15
sample_step = max(1, len(self.frame_buffer) // max_frames_needed)
sample_step = min(sample_step, 3) # 限制最大步长为3，保证帧变化
for idx, (frame, face_regions, _) in enumerate(self.frame_buffer): # 忽略单帧表情
if idx % sample_step != 0:
continue
# 缩放+填充（保持比例，黑边填充）
h, w = frame.shape[:2]
scale = min(self.gif_size[0]/w, self.gif_size[1]/h)
new_w, new_h = int(w*scale), int(h*scale)
frame_resized = cv2.resize(frame, (new_w, new_h), cv2.INTER_AREA)
pad_l = (self.gif_size[0] - new_w) // 2
pad_r = self.gif_size[0] - new_w - pad_l
pad_t = (self.gif_size[1] - new_h) // 2
pad_b = self.gif_size[1] - new_h - pad_t
frame_padded = cv2.copyMakeBorder(
frame_resized, pad_t, pad_b, pad_l, pad_r,
cv2.BORDER_CONSTANT, value=(0, 0, 0)
)
# 使用主表情文字，而非单帧表情
frame_marked = self._draw_face_info_on_frame(
frame_padded,
face_regions,
similarity,
[main_emotion] # 传入主表情，确保文字稳定
)
# 转换为RGB（GIF要求）
frame_rgb = cv2.cvtColor(frame_marked, cv2.COLOR_BGR2RGB)
processed_frames.append(frame_rgb)
# 确保至少8帧，不足则补充最后几帧
if len(processed_frames) < 8:
processed_frames += processed_frames[-3:] * (8 - len(processed_frames))
# 2. 生成GIF文件名（使用格式化时间戳：YYYYMMDD_HHMMSS_ms）
timestamp = time.strftime("%Y%m%d_%H%M%S", time.localtime())
ms = int(time.time() * 1000) % 1000 # 毫秒部分保证唯一性
gif_filename = f"face_gif_{timestamp}_{ms}_sim{similarity:.2f}.gif"
gif_path = os.path.join(self.save_dir, gif_filename)
# 3. 生成GIF（降低帧率，延长单帧显示时间）
frame_interval = 1.0 / self.gif_fps # 延长帧间隔（至少0.2秒）
imageio.mimsave(
gif_path, processed_frames,
duration=frame_interval, # 明确帧间隔（秒）
loop=0, # 无限循环
fps=self.gif_fps, # 强制帧率
format='GIF',
palettesize=256, # 增加色彩数量，避免失真
subrectangles=True # 只更新变化区域，优化体积
)
# 4. 统计与日志
file_size = os.path.getsize(gif_path) / 1024
self.generated_gif_count += 1
self.generated_gif_paths.append(gif_path)
self.last_generate_frame = self.current_frame_idx # 更新最后生成帧数
print(f"\n✅ 生成目标人脸GIF：")
print(f" 📍 时间：{self.current_sec:.1f}秒 | 相似度：{similarity:.3f} | 主表情：{main_emotion_text}")
print(f" 📄 文件：{gif_path} | 帧数：{len(processed_frames)} | 大小：{file_size:.2f}KB")
# 清空缓存
self.frame_buffer.clear()
def _draw_progress(self):
"""绘制处理进度条"""
progress = (self.current_frame_idx / self.total_frames) * 100
bar_length = 50
filled = int(bar_length * progress / 100)
bar = "█" * filled + "-" * (bar_length - filled)
print(f"\r处理进度：|{bar}| {progress:.1f}% | 已生成GIF：{self.generated_gif_count}个", end="")
def run_face_gif_detection(self, skip_frames=3):
"""
主函数：运行人脸检测，匹配到目标则生成GIF
:param skip_frames: 跳帧检测（减少计算量，默认每3帧检测一次）
"""
print(f"\n🚀 开始检测目标人脸并生成GIF | 视频总时长：{self.total_duration:.1f}秒")
print(f"🔧 匹配阈值：{self.sim_threshold} | 缓存时长：{self.gif_cache_frames/self.fps:.1f}秒 | 跳帧检测：每{skip_frames}帧一次")
finish_flag = False
while not finish_flag:
# 读取帧
ret, frame = self.cap.read()
if not ret:
finish_flag = True
break
# 初始化当前帧的人脸匹配结果
is_matched = False
max_sim = 0.0
matched_regions = []
matched_emotions = []
# 跳帧检测（提升效率）+ 冷却时间内不检测
if (self.current_frame_idx % skip_frames == 0) and \
(self.current_frame_idx - self.last_generate_frame > self.cool_down_frames):
# 匹配人脸（包含表情分析）
is_matched, max_sim, matched_regions, matched_emotions = self._match_face(frame)
# 缓存帧逻辑：
# 1. 检测到匹配人脸 → 开始缓存帧（直到缓存满）
# 2. 缓存满后 → 生成GIF
if is_matched or (len(self.frame_buffer) > 0 and len(self.frame_buffer) < self.gif_cache_frames):
self.frame_buffer.append((frame.copy(), matched_regions, matched_emotions))
# 缓存满则生成GIF
if len(self.frame_buffer) >= self.gif_cache_frames:
self._generate_gif_from_buffer(max_sim)
# 更新进度
self.current_frame_idx += 1
self.current_sec = self.current_frame_idx / self.fps
self._draw_progress()
# 内存清理
gc.collect()
# 处理剩余缓存帧（若有）
if len(self.frame_buffer) >= 5: # 剩余帧至少5帧才生成
print(f"\n📌 处理剩余缓存帧，生成最后一个GIF")
self._generate_gif_from_buffer(max_sim)
# 最终统计
self.cap.release()
print(f"\n\n📋 检测完成！")
print(f" 🎯 共生成 {self.generated_gif_count} 个目标人脸GIF")
print(f" 📂 GIF保存路径：{self.save_dir}")
if self.generated_gif_paths:
print(f" 📄 GIF列表：")
for idx, path in enumerate(self.generated_gif_paths, 1):
print(f" {idx}. {path}")
return self.generated_gif_paths
def _load_target_face(self):
"""加载目标人脸并提取特征（支持多图片+单图片多个人脸）"""
print(f"🔍 正在加载目标人脸：{self.target_face_path}")
try:
self.target_embeddings = []
# 处理多图片路径（如果是列表）
if isinstance(self.target_face_path, list):
img_paths = self.target_face_path
else:
img_paths = [self.target_face_path]
# 逐个加载图片并提取特征
for img_path in img_paths:
target_objs = DeepFace.represent(
img_path=img_path,
model_name="Facenet",
detector_backend=self.detect_backend,
enforce_detection=True
)
self.target_embeddings.extend([obj["embedding"] for obj in target_objs])
print(f"✅ 成功加载 {len(self.target_embeddings)} 个人脸特征")
if len(self.target_embeddings) == 0:
raise ValueError("❌ 目标图片中未检测到人脸！")
except Exception as e:
raise RuntimeError(f"❌ 加载目标人脸失败：{str(e)}")
# ------------------------------
# 示例调用
# ------------------------------
if __name__ == "__main__":
# 配置参数（替换为你的实际路径）
VIDEO_PATH = r"D:\BaiduNetdiskDownload\火烧赤壁2.mp4" # 待分析视频
TARGET_FACE_PATH = [
f"F:\\OpenCV\\pm\\deepface\\tests\\liubei.png",
f"F:\\OpenCV\\pm\\deepface\\tests\\caocao.png",
] # 目标人脸图片
SAVE_DIR = f"F:\\OpenCV\\video_gifs" # GIF保存目录
SIM_THRESHOLD = 0.7 # 匹配阈值（建议0.65~0.8）
# 初始化生成器
face_gif_generator = TargetFaceGIFGenerator(
video_path=VIDEO_PATH,
target_face_path=TARGET_FACE_PATH,
save_dir=SAVE_DIR,
similarity_threshold=SIM_THRESHOLD,
detect_backend="mtcnn", # mtcnn检测精度最高（推荐）
gif_cache_seconds=3, # 检测到人脸后缓存3秒帧
gif_fps=5, # 降低帧率至3（每帧显示约0.33秒）
gif_size=(400, 400), # GIF尺寸
cool_down_seconds=5 # 5秒冷却（避免重复生成）
)
# 开始检测并生成GIF（跳帧建议30-60，过大会导致帧变化不明显）
face_gif_generator.run_face_gif_detection(skip_frames=60)c次
此文件会逐帧的读取视频文件（.mp4）中的图片， 并和传入的特征人物图片比对， 如果相似度到达阈值，就会将一定数量的帧合并起来， 生成GIF图片。并根据识别出来的情绪特征，打上对应的文字。
F:\OpenCV\pm\deepface> & C:/Users/yigepingfanren/AppData/Local/Programs/Python/Python38/python.exe f:/OpenCV/pm/deepface/tests/cc.py attrattrattrattrattrattr:float128 attrattrattrattrattrattr:complex256 attrattrattrattrattrattr:flatten attrattrattrattrattrattr:random.seed attrattrattrattrattrattr:random.randn attrattrattrattrattrattr:random.standard_normal attrattrattrattrattrattr:random.uniform attrattrattrattrattrattr:random.poisson attrattrattrattrattrattr:random.random attrattrattrattrattrattr:random.rand attrattrattrattrattrattr:random.randint attrattrattrattrattrattr:typeDict 🔍 正在加载目标人脸：['F:\\OpenCV\\pm\\deepface\\tests\\liubei.png', 'F:\\OpenCV\\pm\\deepface\\tests\\caocao.png'] attrattrattrattrattrattr:__module__ 1/1 [==============================] - 1s 524ms/step 1/1 [==============================] - 0s 131ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 35ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 25ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 23ms/step 1/1 [==============================] - 0s 28ms/step 1/1 [==============================] - 0s 14ms/step 1/1 [==============================] - 0s 26ms/step 1/1 [==============================] - 0s 27ms/step 2/2 [==============================] - 0s 0s/step 1/1 [==============================] - 0s 107ms/step 1/1 [==============================] - 0s 128ms/step 1/1 [==============================] - 0s 72ms/step 1/1 [==============================] - 0s 54ms/step 1/1 [==============================] - 0s 34ms/step 1/1 [==============================] - 0s 35ms/step 1/1 [==============================] - 0s 36ms/step 1/1 [==============================] - 0s 28ms/step 1/1 [==============================] - 0s 25ms/step 1/1 [==============================] - 0s 25ms/step 1/1 [==============================] - 0s 28ms/step 1/1 [==============================] - 0s 28ms/step 1/1 [==============================] - 0s 27ms/step 14/14 [==============================] - 0s 7ms/step 1/1 [==============================] - 0s 37ms/step ✅ 成功加载 2 个人脸特征 📁 GIF保存目录：F:\OpenCV\video_gifs 🚀 开始检测目标人脸并生成GIF | 视频总时长：1680.0秒 🔧 匹配阈值：0.7 | 缓存时长：3.0秒 | 跳帧检测：每60帧一次 处理进度：|--------------------------------------------------| 0.1% | 已生成GIF：0个
执行效果
可以根据你想要的视频，和你想要的文字，来生成不同的表情包哦！~~~~
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