add:covers_analyser

covers_analyser.py

@@ -5,40 +5,17 @@ import requests
 from io import BytesIO
 from PIL import Image
 import os
-from colorthief import ColorThief
-import pytesseract
 from multiprocessing import Pool
-from cnsenti import Sentiment
+from sklearn.cluster import MiniBatchKMeans
-import pynlpir
+from tqdm import tqdm
-from collections import defaultdict
 import warnings
 
 warnings.filterwarnings('ignore')
 
-#设置OCR路径
-pytesseract.pytesseract.tesseract_cmd = r'D:Program files\Tesseract-OCR\tesseract.exe'
-
 # ------------------图像处理-初始化配置 ---------------------
 # 人脸检测模型初始化
 face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
 
-# 图像情感模型系数（基于IAPS数据集校准）
-VALENCE_WEIGHTS = {
-    'warm_ratio': 0.35,
-    'brightness': 0.15,
-    'symmetry': 0.20,
-    'colorfulness': 0.30
-}
-
-AROUSAL_WEIGHTS = {
-    'contrast': 0.40,
-    'edge_density': 0.35,
-    'saturation_std': 0.25
-}
-
-# 暖色调定义（HSV色相范围）
-WARM_HUE_RANGE = (0, 60)  # 红色到黄色
-
 # ------------------处理图像 ---------------------
 def get_image(url):
     """从URL获取图像并预处理"""
@@ -51,56 +28,72 @@ def get_image(url):
         return None
 
 
-def analyze_image(img):
+def extract_color_palette(img_rgb, color_count=5):
-    """分析图像特征"""
+    """提取前N种主色及比例"""
-    if img is None:
+    # 使用k-means聚类提取主色
-        return {}
+    pixels = img_rgb.reshape(-1, 3)
+    kmeans = MiniBatchKMeans(n_clusters=color_count, random_state=0)
+    labels = kmeans.fit_predict(pixels)
 
-    # 转换为HSV颜色空间
+    # 计算每种颜色的比例
-    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+    counts = np.bincount(labels)
-    h, s, v = cv2.split(hsv)
+    total = counts.sum()
+    palette = []
+    for i in range(color_count):
+        ratio = counts[i] / total
+        color = kmeans.cluster_centers_[i].astype(int)
+        palette.append((color, ratio))
 
-    # 计算基础特征
+    return sorted(palette, key=lambda x: -x[1])  # 按比例降序排列
-    features = {
-        'brightness': np.mean(v),
-        'contrast': np.max(v) - np.min(v),
-        'saturation_std': np.std(s),
-        'colorfulness': np.std(h) + np.std(s) + np.std(v)
-    }
-
-    # 暖色比例计算
-    hue_mask = cv2.inRange(h, WARM_HUE_RANGE[0], WARM_HUE_RANGE[1])
-    features['warm_ratio'] = np.count_nonzero(hue_mask) / (img.shape[0] * img.shape[1])
-
-    # 对称性计算
-    mid = img.shape[1] // 2
-    left = img[:, :mid]
-    right = cv2.flip(img[:, mid:], 1)
-    features['symmetry'] = cv2.matchTemplate(left, right, cv2.TM_CCOEFF_NORMED)[0][0]
-
-    # 边缘密度
-    edges = cv2.Canny(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), 100, 200)
-    features['edge_density'] = np.mean(edges)
-
-    return features
 
 
-def calculate_affect(features):
+def classify_hsv_color(rgb_color):
-    """计算情感效价和唤醒度"""
+    """将RGB颜色分类为暖色/冷色/中性色"""
-    poslm = sum(features[k] * VALENCE_WEIGHTS[k] for k in VALENCE_WEIGHTS)
+    try:
-    actlm = sum(features[k] * AROUSAL_WEIGHTS[k] for k in AROUSAL_WEIGHTS)
+        # 确保输入是有效的RGB颜色值
+        rgb_color = np.clip(rgb_color, 0, 255)
+        hsv = cv2.cvtColor(np.uint8([[rgb_color]]), cv2.COLOR_RGB2HSV)[0][0]
+        h, s, v = hsv[0], hsv[1] / 255.0, hsv[2] / 255.0  # 归一化
 
-    # Sigmoid归一化
+        # 中性色判断（根据Palmer标准）
-    return {
+        if s < 0.2 or v < 0.2:
-        'Poslm': 2 / (1 + np.exp(-poslm)) - 1,
+            return 'neutral'
-        'Actlm': 2 / (1 + np.exp(-actlm)) - 1
+
-    }
+        # 色相分类
+        if (0 <= h < 90) or (270 <= h <= 360):
+            return 'warm'
+        return 'cool'
+    except Exception as e:
+        print(f"颜色越界: {str(e)}")
+        return 'neutral'  # 出错时默认返回中性色
+
+def determine_warm_tone(img_rgb):
+    """返回（暖色标签, 暖色比例, 冷色比例）"""
+    palette = extract_color_palette(img_rgb)
+
+    warm_ratio, cool_ratio, neutral_ratio = 0.0, 0.0, 0.0
+    for color, ratio in palette:
+        category = classify_hsv_color(color)
+        if category == 'warm':
+            warm_ratio += ratio
+        elif category == 'cool':
+            cool_ratio += ratio
+        else:
+            neutral_ratio += ratio
+
+    return warm_ratio, cool_ratio, neutral_ratio
 
 
 def detect_human(img):
-    """检测人像"""
     gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-    faces = face_cascade.detectMultiScale(gray, 1.05, 3)
+    # 优化参数组合
+    faces = face_cascade.detectMultiScale(
+        gray,
+        scaleFactor=1.02,    # 减小缩放步长，增加检测粒度
+        minNeighbors=5,      # 提高邻居数要求，减少误检
+        minSize=(50, 50),    # 适配B站封面最小人脸尺寸
+        flags=cv2.CASCADE_FIND_BIGGEST_OBJECT  # 优先检测最大人脸
+    )
     return len(faces) > 0
 
 
@@ -110,24 +103,18 @@ def process_url(url):
         if img is None:
             return None
 
-        features = analyze_image(img)
+        # 颜色分析
-        affect = calculate_affect(features)
+        img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-
+        warm_ratio, cool_ratio, neutral_ratio = determine_warm_tone(img_rgb)
-        # 主色分析
-        color_thief = ColorThief(BytesIO(requests.get(url).content))
-        dominant_color = color_thief.get_color(quality=1)
-        hsv_color = cv2.cvtColor(np.uint8([[dominant_color]]), cv2.COLOR_RGB2HSV)[0][0]
-        warm = 1 if WARM_HUE_RANGE[0] <= hsv_color[0] <= WARM_HUE_RANGE[1] else 0
-
-        # 人像检测
-        has_human = detect_human(img)
 
         return {
             'url': url,
-            **affect,
+            'Portrait': int(detect_human(img)),
-            'Warm': warm,
+            'WarmRatio': round(warm_ratio, 3),
-            'Portrait': int(has_human)
+            'CoolRatio': round(cool_ratio, 3),
+            'NeutralRatio': round(neutral_ratio, 3)
         }
+
     except Exception as e:
         print(f"Error processing {url}: {str(e)}")
         return None
@@ -136,41 +123,27 @@ def process_url(url):
 # 批量处理
 def batch_process(urls, workers=4):
     with Pool(workers) as pool:
-        results = [res for res in pool.imap(process_url, urls) if res is not None]
+        results = [res for res in tqdm(pool.imap(process_url, urls),
+                                       total=len(urls),
+                                       desc="处理进度") if res is not None]
     return pd.DataFrame(results)
 
 
 # 使用示例
 if __name__ == "__main__":
     # 读取URL列表
-    input_csv = "data_all.csv"
+    input_csv = "data_all_first_ver.csv"
     #输出路径
     os.makedirs('./result', exist_ok=True)
     output_csv = "result/analysis_results.csv"
 
-    ##完整运行
+    #完整运行
-    # df = pd.read_csv(input_csv)
+    df = pd.read_csv(input_csv)
-    # urls = df['视频封面'].tolist()
+    urls = df['视频封面URL'].tolist()
-    #
-    # # 执行分析
-    # result_df = batch_process(urls)
-    #
-    # # 合并原始数据
-    # final_df = df.merge(result_df, left_on='视频封面')
-    # final_df.drop('url', axis=1).to_csv(output_csv, index=False)
 
-    # 示例URL列表
+    # 执行分析
-    #小批量实验
-    urls = [
-        'http://i0.hdslb.com/bfs/archive/393a8e961b704d43256fe7e6c89fee04df966e17.jpg',
-        'http://i0.hdslb.com/bfs/archive/072e16a1237040941f15b1ed67a8d1ebe6f2e041.jpg',
-        'http://i2.hdslb.com/bfs/archive/1c56b5bec767c604175983cc5926f5832baa9bb8.jpg',
-        'http://i0.hdslb.com/bfs/archive/66384e53a15345a539ccbb2989442f1d960b9235.jpg',
-        'http://i2.hdslb.com/bfs/archive/836b762456f0b4d65dd2c40fc4cd120107e46b88.jpg',
-    ]
     result_df = batch_process(urls)
-    result_df.to_csv("result/analysis_results.csv", index=False)
+    result_df.to_csv(output_csv, index=False, encoding='utf-8-sig')
+
     print(f"成功处理 {len(result_df)}/{len(urls)} 张图片")
-
-
     print("分析完成！结果已保存至", output_csv)