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