今天是第23天我們可以寫一個lstm結合yolo去分析這個人在社群平台的po文狀況進而去推薦可以遊玩的景點,以下是程式碼
確保安裝了以下庫:
pip install tensorflow opencv-python numpy yolov5 scikit-learn pandas
使用YOLOv5進行高效的人臉檢測,並將檢測到的人臉提取為圖像。
from yolov5 import YOLOv5
# 加載YOLO模型
yolo = YOLOv5('yolov5x.pt') # 使用較高精度的模型
# 人臉檢測函數
def detect_faces(image):
results = yolo.predict(image)
faces = []
for result in results.xyxy[0]: # x1, y1, x2, y2 for bounding boxes
x_min, y_min, x_max, y_max = map(int, result[:4])
face = image[y_min:y_max, x_min:x_max]
faces.append(face)
return faces
這裡使用多層LSTM模型來進行情感分析,並考慮時間序列的情感變化。
from tensorflow.keras.models import load_model
# 加載LSTM情感分析模型
lstm_model = load_model('/path/to/lstm_model.h5')
# 將臉部圖像轉換為適合LSTM輸入的數據
def preprocess_face(face):
face = cv2.resize(face, (64, 64)) # 增大輸入尺寸以捕捉更多特徵
face = face.astype('float32') / 255.0
face = np.expand_dims(face, axis=0)
return face
# 多階段情感分析
def analyze_emotion(faces):
emotions = []
for face in faces:
face_data = preprocess_face(face)
emotion = lstm_model.predict(face_data)
emotions.append(emotion)
return emotions
使用加權推薦系統,結合用戶歷史數據和情感變化來推薦景點。
import pandas as pd
from sklearn.preprocessing import StandardScaler
# 定義情感到景點的映射和權重
emotion_to_places = {
'happy': ['海灘', '遊樂園', '山區'],
'sad': ['博物館', '藝術畫廊', '圖書館'],
'angry': ['健身房', '武術中心'],
'neutral': ['公園', '咖啡廳', '商場']
}
# 根據情感分數調整權重
def recommend_places(emotions, user_history):
emotion_weights = {
'happy': 1.0,
'sad': 0.5,
'angry': 0.7,
'neutral': 0.6
}
places_score = {}
# 用戶歷史數據加權
history_weights = user_history.mean()
for emotion in emotions:
emotion_label = np.argmax(emotion) # 假設模型輸出one-hot編碼
if emotion_label in emotion_to_places:
for place in emotion_to_places[emotion_label]:
if place not in places_score:
places_score[place] = 0
places_score[place] += emotion_weights.get(emotion_label, 0) * history_weights.get(place, 1)
sorted_places = sorted(places_score.items(), key=lambda x: x[1], reverse=True)
return [place for place, score in sorted_places]
使用LSTM來分析情感時間序列,並根據情感變化調整推薦。
from tensorflow.keras.preprocessing.sequence import pad_sequences
def analyze_emotion_sequence(emotion_sequences):
# 假設 emotion_sequences 是一個時間序列的情感數據
# 進行填充和預處理
padded_sequences = pad_sequences(emotion_sequences, maxlen=100)
emotion_sequence_analysis = lstm_model.predict(padded_sequences)
return emotion_sequence_analysis
def dynamic_recommendation(emotion_sequences, user_history):
emotion_sequence_analysis = analyze_emotion_sequence(emotion_sequences)
recommended_places = recommend_places(emotion_sequence_analysis, user_history)
return recommended_places
根據用戶的反饋來調整模型和推薦系統。
# 假設用戶反饋數據
feedback_data = pd.DataFrame({
'place': ['海灘', '博物館', '健身房'],
'feedback': [1, 0, 1] # 1 表示喜歡,0 表示不喜歡
})
def update_recommendation_weights(feedback_data):
global emotion_to_places
for index, row in feedback_data.iterrows():
place = row['place']
feedback = row['feedback']
if feedback == 1:
# 提升推薦權重
for emotion in emotion_to_places:
if place in emotion_to_places[emotion]:
emotion_weights[emotion] += 0.1
else:
# 降低推薦權重
for emotion in emotion_to_places:
if place in emotion_to_places[emotion]:
emotion_weights[emotion] -= 0.1
update_recommendation_weights(feedback_data)
def analyze_and_recommend(image, emotion_sequences, user_history):
faces = detect_faces(image)
if faces:
emotions = analyze_emotion(faces)
recommended_places = dynamic_recommendation(emotion_sequences, user_history)
return recommended_places
else:
return ["未檢測到人臉"]
# 示例輸入圖像
image = cv2.imread('input.jpg')
user_history = pd.DataFrame({
'place': ['海灘', '博物館', '健身房'],
'frequency': [5, 3, 2]
})
# 模擬情感序列
emotion_sequences = [np.random.rand(10, 48, 48, 3) for _ in range(5)] # 假設有5段情感序列
# 獲取推薦的景點
places = analyze_and_recommend(image, emotion_sequences, user_history)
print("推薦的景點:", places)
這部分程式碼負責加載YOLO模型並檢測圖像中的人臉。
from yolov5 import YOLOv5
# 加載YOLO模型
yolo = YOLOv5('yolov5x.pt') # 使用較高精度的模型
# 人臉檢測函數
def detect_faces(image):
results = yolo.predict(image)
faces = []
for result in results.xyxy[0]: # x1, y1, x2, y2 for bounding boxes
x_min, y_min, x_max, y_max = map(int, result[:4])
face = image[y_min:y_max, x_min:x_max]
faces.append(face)
return faces
YOLOv5('yolov5x.pt')
:加載YOLOv5模型,yolov5x.pt
是精度較高的模型檔案。detect_faces(image)
:這個函數接收一張圖像,通過YOLO模型檢測人臉。results.xyxy[0]
返回檢測到的邊界框(bounding boxes),result[:4]
提取邊界框的座標。根據這些座標裁剪出人臉區域並儲存到faces
列表中。這部分程式碼負責加載LSTM模型,處理臉部圖像,並進行情感分析。
from tensorflow.keras.models import load_model
# 加載LSTM情感分析模型
lstm_model = load_model('/path/to/lstm_model.h5')
# 將臉部圖像轉換為適合LSTM輸入的數據
def preprocess_face(face):
face = cv2.resize(face, (64, 64)) # 增大輸入尺寸以捕捉更多特徵
face = face.astype('float32') / 255.0
face = np.expand_dims(face, axis=0)
return face
# 多階段情感分析
def analyze_emotion(faces):
emotions = []
for face in faces:
face_data = preprocess_face(face)
emotion = lstm_model.predict(face_data)
emotions.append(emotion)
return emotions
load_model('/path/to/lstm_model.h5')
:加載LSTM情感分析模型。preprocess_face(face)
:將臉部圖像調整為LSTM模型需要的尺寸(64x64),並將像素值標準化(0到1之間)。np.expand_dims(face, axis=0)
添加批次維度,將其轉換為模型可接受的形狀。analyze_emotion(faces)
:這個函數遍歷所有檢測到的臉部圖像,對每個臉部圖像進行預處理,然後用LSTM模型進行情感預測。結果保存在emotions
列表中。這部分程式碼根據情感分析結果和用戶歷史數據來推薦景點。
import pandas as pd
from sklearn.preprocessing import StandardScaler
# 定義情感到景點的映射和權重
emotion_to_places = {
'happy': ['海灘', '遊樂園', '山區'],
'sad': ['博物館', '藝術畫廊', '圖書館'],
'angry': ['健身房', '武術中心'],
'neutral': ['公園', '咖啡廳', '商場']
}
# 根據情感分數調整權重
def recommend_places(emotions, user_history):
emotion_weights = {
'happy': 1.0,
'sad': 0.5,
'angry': 0.7,
'neutral': 0.6
}
places_score = {}
# 用戶歷史數據加權
history_weights = user_history.mean()
for emotion in emotions:
emotion_label = np.argmax(emotion) # 假設模型輸出one-hot編碼
if emotion_label in emotion_to_places:
for place in emotion_to_places[emotion_label]:
if place not in places_score:
places_score[place] = 0
places_score[place] += emotion_weights.get(emotion_label, 0) * history_weights.get(place, 1)
sorted_places = sorted(places_score.items(), key=lambda x: x[1], reverse=True)
return [place for place, score in sorted_places]
emotion_to_places
:定義情感和景點的映射關係。recommend_places(emotions, user_history)
:這個函數根據情感分析結果和用戶歷史數據來計算每個景點的分數。history_weights
是用戶歷史數據的平均值,用來調整每個景點的分數。根據情感分數和用戶歷史,計算每個景點的最終分數並排序。這部分程式碼使用LSTM來分析情感時間序列,並根據分析結果動態調整推薦。
from tensorflow.keras.preprocessing.sequence import pad_sequences
def analyze_emotion_sequence(emotion_sequences):
# 假設 emotion_sequences 是一個時間序列的情感數據
# 進行填充和預處理
padded_sequences = pad_sequences(emotion_sequences, maxlen=100)
emotion_sequence_analysis = lstm_model.predict(padded_sequences)
return emotion_sequence_analysis
def dynamic_recommendation(emotion_sequences, user_history):
emotion_sequence_analysis = analyze_emotion_sequence(emotion_sequences)
recommended_places = recommend_places(emotion_sequence_analysis, user_history)
return recommended_places
analyze_emotion_sequence(emotion_sequences)
:這個函數對情感序列進行填充(pad_sequences
)和預處理,以適應LSTM模型的輸入。maxlen=100
是序列的最大長度。dynamic_recommendation(emotion_sequences, user_history)
:分析情感序列,然後使用分析結果來推薦景點。這部分程式碼根據用戶反饋來調整推薦系統的權重。
# 假設用戶反饋數據
feedback_data = pd.DataFrame({
'place': ['海灘', '博物館', '健身房'],
'feedback': [1, 0, 1] # 1 表示喜歡,0 表示不喜歡
})
def update_recommendation_weights(feedback_data):
global emotion_to_places
for index, row in feedback_data.iterrows():
place = row['place']
feedback = row['feedback']
if feedback == 1:
# 提升推薦權重
for emotion in emotion_to_places:
if place in emotion_to_places[emotion]:
emotion_weights[emotion] += 0.1
else:
# 降低推薦權重
for emotion in emotion_to_places:
if place in emotion_to_places[emotion]:
emotion_weights[emotion] -= 0.1
update_recommendation_weights(feedback_data)
feedback_data
:用戶的反饋數據,1表示喜歡,0表示不喜歡。update_recommendation_weights(feedback_data)
:根據用戶反饋調整情感對應的景點推薦權重。對於喜歡的景點,增加推薦權重;對於不喜歡的景點,減少推薦權重。將所有功能整合到一個系統中。
def analyze_and_recommend(image, emotion_sequences, user_history):
faces = detect_faces(image)
if faces:
emotions = analyze_emotion(faces)
recommended_places = dynamic_recommendation(emotion_sequences, user_history)
return recommended_places
else:
return ["未檢測到人臉"]
# 示例輸入圖像
image = cv2.imread('input.jpg')
user_history = pd.DataFrame({
'place': ['海灘', '博物館', '健身房'],
'frequency': [5, 3, 2]
})
# 模擬情感序列
emotion_sequences = [np.random.rand(10, 64, 64, 3) for _ in range(5)] # 假設有5段情感序列
# 獲取推薦的景點
places = analyze_and_recommend(image, emotion_sequences, user_history)
print("推薦的景點:", places)
將前面定義的功能整合到一個系統中,並使用實際的數據進行推薦。
def analyze_and_recommend(image, emotion_sequences, user_history):
# 檢測圖像中的人臉
faces = detect_faces(image)
if faces:
# 對檢測到的人臉進行情感分析
emotions = analyze_emotion(faces)
# 根據情感序列和用戶歷史數據進行動態推薦
recommended_places = dynamic_recommendation(emotion_sequences, user_history)
return recommended_places
else:
# 若未檢測到人臉,返回預設消息
return ["未檢測到人臉"]
# 示例輸入圖像
image = cv2.imread('input.jpg')
# 模擬用戶歷史數據
user_history = pd.DataFrame({
'place': ['海灘', '博物館', '健身房'],
'frequency': [5, 3, 2] # 用戶去過這些地方的頻率
})
# 模擬情感序列
emotion_sequences = [np.random.rand(10, 64, 64, 3) for _ in range(5)] # 假設有5段情感序列
# 獲取推薦的景點
places = analyze_and_recommend(image, emotion_sequences, user_history)
print("推薦的景點:", places)
analyze_and_recommend(image, emotion_sequences, user_history)
:
detect_faces(image)
:檢測圖像中的人臉,返回人臉圖像列表。analyze_emotion(faces)
:對檢測到的每張人臉進行情感分析,返回情感預測結果。dynamic_recommendation(emotion_sequences, user_history)
:基於情感序列分析結果和用戶歷史數據,計算推薦景點並返回結果。image
:輸入圖像,這裡假設為input.jpg
。
user_history
:模擬的用戶歷史數據,記錄用戶去過的景點及頻率。
emotion_sequences
:模擬的情感序列數據,用於測試情感時間序列分析的功能。
places
:返回的推薦景點列表,並打印出來。
多模態數據融合:
深度學習模型:
即時反饋系統:
使用這個系統我們就能做人類情感分析去做推薦系統。