滑动窗口策略 ：在图像上滑动固定大小的窗口，对每个窗口进行分类多维特征提取 ：结合统计特征、纹理特征、边缘特征、形状特征等随机森林分类 ：训练二分类器判断窗口是否包含目标后处理优化 ：使用非极大值抑制减少重复检测LBP纹理特征 ：捕捉局部纹理模式灰度共生矩阵 ：描述纹理的统计特性边缘密度 ：反映目标边界信息形状描述符 ：圆形度、面积比等几何特征数据收集 ：收集大量正负样本进行训练特征优化 ：根据具体目标调整特征提取策略参数调优 ：调整窗口大小、步长、置信度阈值等多尺度检测 ：使用不同尺寸的窗口检测不同大小的目标计算资源受限的嵌入式设备 目标具有明显纹理或形状特征的场景 需要快速部署和调试的原型系统 传统图像处理流程的补充 import cv2
import numpy as np
from sklearn. ensemble import RandomForestClassifier
from sklearn. model_selection import train_test_split
from sklearn. metrics import classification_report
from skimage. feature import local_binary_pattern, graycomatrix, graycoprops
from skimage. measure import regionprops
import os
from typing import List, Tuple
import matplotlib. pyplot as pltclass RandomForestObjectDetector : """基于随机森林的目标检测器""" def __init__ ( self, window_size= ( 64 , 64 ) , step_size= 16 , n_estimators= 100 ) : """初始化检测器Args:window_size: 滑动窗口大小step_size: 滑动步长n_estimators: 随机森林中树的数量""" self. window_size = window_sizeself. step_size = step_sizeself. rf_classifier = RandomForestClassifier( n_estimators= n_estimators, random_state= 42 , max_depth= 10 , min_samples_split= 5 ) self. is_trained = False def extract_features ( self, image_patch: np. ndarray) - > np. ndarray: """从图像块中提取特征Args:image_patch: 输入图像块Returns:特征向量""" features = [ ] if len ( image_patch. shape) == 3 : gray = cv2. cvtColor( image_patch, cv2. COLOR_BGR2GRAY) else : gray = image_patch. copy( ) features. extend( [ np. mean( gray) , np. std( gray) , np. median( gray) , np. min ( gray) , np. max ( gray) , np. var( gray) ] ) radius = 3 n_points = 8 * radiuslbp = local_binary_pattern( gray, n_points, radius, method= 'uniform' ) lbp_hist, _ = np. histogram( lbp. ravel( ) , bins= n_points + 2 , range = ( 0 , n_points + 2 ) , density= True ) features. extend( lbp_hist) try : glcm = graycomatrix( gray, distances= [ 1 ] , angles= [ 0 , 45 , 90 , 135 ] , levels= 256 , symmetric= True , normed= True ) contrast = graycoprops( glcm, 'contrast' ) . mean( ) dissimilarity = graycoprops( glcm, 'dissimilarity' ) . mean( ) homogeneity = graycoprops( glcm, 'homogeneity' ) . mean( ) energy = graycoprops( glcm, 'energy' ) . mean( ) correlation = graycoprops( glcm, 'correlation' ) . mean( ) features. extend( [ contrast, dissimilarity, homogeneity, energy, correlation] ) except : features. extend( [ 0 , 0 , 0 , 0 , 0 ] ) edges = cv2. Canny( gray, 50 , 150 ) edge_density = np. sum ( edges > 0 ) / edges. sizefeatures. append( edge_density) grad_x = cv2. Sobel( gray, cv2. CV_64F, 1 , 0 , ksize= 3 ) grad_y = cv2. Sobel( gray, cv2. CV_64F, 0 , 1 , ksize= 3 ) grad_magnitude = np. sqrt( grad_x** 2 + grad_y** 2 ) features. extend( [ np. mean( grad_magnitude) , np. std( grad_magnitude) ] ) _, binary = cv2. threshold( gray, 0 , 255 , cv2. THRESH_BINARY + cv2. THRESH_OTSU) contours, _ = cv2. findContours( binary, cv2. RETR_EXTERNAL, cv2. CHAIN_APPROX_SIMPLE) if contours: largest_contour = max ( contours, key= cv2. contourArea) area = cv2. contourArea( largest_contour) perimeter = cv2. arcLength( largest_contour, True ) if perimeter > 0 : circularity = 4 * np. pi * area / ( perimeter ** 2 ) else : circularity = 0 features. extend( [ area / ( gray. shape[ 0 ] * gray. shape[ 1 ] ) , circularity] ) else : features. extend( [ 0 , 0 ] ) return np. array( features) def sliding_window ( self, image: np. ndarray) - > List[ Tuple] : """在图像上应用滑动窗口Args:image: 输入图像Returns:窗口位置和图像块的列表""" windows = [ ] h, w = image. shape[ : 2 ] for y in range ( 0 , h - self. window_size[ 1 ] + 1 , self. step_size) : for x in range ( 0 , w - self. window_size[ 0 ] + 1 , self. step_size) : window = image[ y: y + self. window_size[ 1 ] , x: x + self. window_size[ 0 ] ] if window. shape[ : 2 ] == self. window_size: windows. append( ( ( x, y) , window) ) return windowsdef prepare_training_data ( self, positive_samples: List[ np. ndarray] , negative_samples: List[ np. ndarray] ) - > Tuple[ np. ndarray, np. ndarray] : """准备训练数据Args:positive_samples: 正样本图像块列表negative_samples: 负样本图像块列表Returns:特征矩阵和标签向量""" features = [ ] labels = [ ] print ( "提取正样本特征..." ) for sample in positive_samples: feature = self. extract_features( sample) features. append( feature) labels. append( 1 ) print ( "提取负样本特征..." ) for sample in negative_samples: feature = self. extract_features( sample) features. append( feature) labels. append( 0 ) return np. array( features) , np. array( labels) def train ( self, positive_samples: List[ np. ndarray] , negative_samples: List[ np. ndarray] ) : """训练随机森林分类器Args:positive_samples: 正样本图像块列表negative_samples: 负样本图像块列表""" print ( "准备训练数据..." ) X, y = self. prepare_training_data( positive_samples, negative_samples) print ( f"训练数据形状: { X. shape} , 标签分布: { np. bincount( y) } " ) X_train, X_val, y_train, y_val = train_test_split( X, y, test_size= 0.2 , random_state= 42 , stratify= y) print ( "训练随机森林分类器..." ) self. rf_classifier. fit( X_train, y_train) val_pred = self. rf_classifier. predict( X_val) print ( "\n验证集性能:" ) print ( classification_report( y_val, val_pred) ) self. is_trained = True print ( "训练完成!" ) def detect ( self, image: np. ndarray, confidence_threshold: float = 0.7 ) - > List[ Tuple] : """在图像中检测目标Args:image: 输入图像confidence_threshold: 置信度阈值Returns:检测结果列表 [(x, y, w, h, confidence), ...]""" if not self. is_trained: raise ValueError( "模型尚未训练，请先调用train()方法" ) detections = [ ] windows = self. sliding_window( image) print ( f"处理 { len ( windows) } 个窗口..." ) for ( x, y) , window in windows: features = self. extract_features( window) . reshape( 1 , - 1 ) prediction = self. rf_classifier. predict( features) [ 0 ] confidence = self. rf_classifier. predict_proba( features) [ 0 ] [ 1 ] if prediction == 1 and confidence >= confidence_threshold: detections. append( ( x, y, self. window_size[ 0 ] , self. window_size[ 1 ] , confidence) ) return detectionsdef non_max_suppression ( self, detections: List[ Tuple] , overlap_threshold: float = 0.3 ) - > List[ Tuple] : """非极大值抑制Args:detections: 检测结果列表overlap_threshold: 重叠阈值Returns:过滤后的检测结果""" if not detections: return [ ] detections = sorted ( detections, key= lambda x: x[ 4 ] , reverse= True ) keep = [ ] while detections: current = detections. pop( 0 ) keep. append( current) remaining = [ ] for detection in detections: iou = self. calculate_iou( current, detection) if iou < overlap_threshold: remaining. append( detection) detections = remainingreturn keep@staticmethod def calculate_iou ( box1: Tuple, box2: Tuple) - > float : """计算两个边界框的IoU""" x1, y1, w1, h1, _ = box1x2, y2, w2, h2, _ = box2xi1 = max ( x1, x2) yi1 = max ( y1, y2) xi2 = min ( x1 + w1, x2 + w2) yi2 = min ( y1 + h1, y2 + h2) if xi2 <= xi1 or yi2 <= yi1: return 0.0 intersection = ( xi2 - xi1) * ( yi2 - yi1) union = w1 * h1 + w2 * h2 - intersectionreturn intersection / union if union > 0 else 0.0 def visualize_detections ( self, image: np. ndarray, detections: List[ Tuple] , title: str = "检测结果" ) : """可视化检测结果Args:image: 原始图像detections: 检测结果列表title: 图像标题""" img_vis = image. copy( ) for x, y, w, h, confidence in detections: cv2. rectangle( img_vis, ( x, y) , ( x + w, y + h) , ( 0 , 255 , 0 ) , 2 ) label = f" { confidence: .2f } " . putText( img_vis, label, ( x, y - 10 ) , cv2. FONT_HERSHEY_SIMPLEX, 0.5 , ( 0 , 255 , 0 ) , 1 ) plt. figure( figsize= ( 12 , 8 ) ) plt. imshow( cv2. cvtColor( img_vis, cv2. COLOR_BGR2RGB) ) plt. title( f" { title} - 检测到 { len ( detections) } 个目标" ) plt. axis( 'off' ) plt. show( )
def create_sample_data ( ) : """创建示例训练数据""" positive_samples = [ ] for _ in range ( 100 ) : sample = np. random. randint( 50 , 100 , ( 64 , 64 ) , dtype= np. uint8) cv2. rectangle( sample, ( 10 , 20 ) , ( 50 , 40 ) , 150 , - 1 ) noise = np. random. normal( 0 , 10 , sample. shape) sample = np. clip( sample + noise, 0 , 255 ) . astype( np. uint8) positive_samples. append( sample) negative_samples = [ ] for _ in range ( 200 ) : sample = np. random. randint( 0 , 50 , ( 64 , 64 ) , dtype= np. uint8) noise = np. random. normal( 0 , 15 , sample. shape) sample = np. clip( sample + noise, 0 , 255 ) . astype( np. uint8) negative_samples. append( sample) return positive_samples, negative_samples
if __name__ == "__main__" : detector = RandomForestObjectDetector( window_size= ( 64 , 64 ) , step_size= 32 ) print ( "创建示例数据..." ) positive_samples, negative_samples = create_sample_data( ) detector. train( positive_samples, negative_samples) test_image = np. random. randint( 0 , 50 , ( 300 , 400 ) , dtype= np. uint8) cv2. rectangle( test_image, ( 50 , 50 ) , ( 114 , 114 ) , 150 , - 1 ) cv2. rectangle( test_image, ( 200 , 150 ) , ( 264 , 214 ) , 150 , - 1 ) print ( "进行目标检测..." ) detections = detector. detect( test_image, confidence_threshold= 0.6 ) filtered_detections = detector. non_max_suppression( detections, overlap_threshold= 0.3 ) print ( f"原始检测数量: { len ( detections) } " ) print ( f"NMS后检测数量: { len ( filtered_detections) } " ) if len ( filtered_detections) > 0 : detector. visualize_detections( cv2. cvtColor( test_image, cv2. COLOR_GRAY2BGR) , filtered_detections) else : print ( "未检测到目标" )
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