R-cnn源代码python

RCNN的Python源代码主要是基于Caffe框架实现的，以下是RCNN的Python源代码示例：

1. 数据预处理部分：

import numpy as np
import os
import cv2

# 读取图像并进行预处理
def load_image(filename):
    img = cv2.imread(filename)
    img = img.astype(np.float32, copy=False)
    # 图像缩放
    img = cv2.resize(img, (224, 224))
    # 图像减均值
    img -= [104, 117, 123]
    # 图像通道转换
    img = img.transpose((2, 0, 1))
    return img

2. 候选区域提取部分：

import selectivesearch

# 使用选择性搜索算法提取候选区域
def selective_search(image, mode='fast'):
    # 选择性搜索参数配置
    ss = selectivesearch.selective_search(image, mode=mode)
    # 提取候选区域
    candidates = []
    for e, (x, y, w, h) in enumerate(ss):
        if w < 10 or h < 10:
            continue
        candidates.append((x, y, w, h))
    return candidates

3. 特征提取部分：

import caffe

# 加载模型和权重
def load_net(model_file, pretrained_file):
    net = caffe.Net(model_file, pretrained_file, caffe.TEST)
    return net

# 提取候选区域的特征
def extract_features(net, image, candidates):
    features = []
    for x, y, w, h in candidates:
        # 裁剪候选区域并进行预处理
        roi = image[:, y:y+h, x:x+w]
        roi = cv2.resize(roi, (227, 227))
        roi = roi - np.array([104, 117, 123])
        # 将候选区域送入网络中进行前向传播
        net.blobs['data'].reshape(1, 3, 227, 227)
        net.blobs['data'].data[...] = roi
        net.forward()
        # 提取网络的特征表示
        feat = net.blobs['fc7'].data[0].copy()
        features.append(feat)
    return features

4. 目标分类和定位部分：

# 加载SVM模型
def load_svm_model(model_file):
    svm_model = cv2.ml.SVM_load(model_file)
    return svm_model

# 对候选区域进行分类和定位
def classify_and_locate(svm_model, features, candidates):
    labels = []
    bboxes = []
    for i, feat in enumerate(features):
        # 对特征进行分类
        label = svm_model.predict(feat.reshape(1, -1))[1][0][0]
        if label == 1:
            # 如果分类为正样本，则进行定位
            bbox = candidates[i]
            bboxes.append(bbox)
        labels.append(label)
    return labels, bboxes

5. 非极大值抑制部分：

# 非极大值抑制
def non_max_suppression(bboxes, overlap_threshold=0.3):
    # 计算候选区域的面积
    areas = [(x[2] - x[0] + 1) * (x[3] - x[1] + 1) for x in bboxes]
    # 根据y坐标排序
    idxs = np.argsort([x[1] for x in bboxes])
    picked_idxs = []
    # 对每个候选区域进行遍历
    while len(idxs) > 0:
        # 取出第一个候选区域
        i = idxs[0]
        picked_idxs.append(i)
        # 计算与其重叠度最大的候选区域
        overlaps = []
        for j in idxs[1:]:
            intersection = max(0, min(bboxes[i][2], bboxes[j][2]) - max(bboxes[i][0], bboxes[j][0])) \
                * max(0, min(bboxes[i][3], bboxes[j][3]) - max(bboxes[i][1], bboxes[j][1]))
            union = areas[i] + areas[j] - intersection
            overlap = intersection / union
            overlaps.append(overlap)
        # 删除与其重叠度大于阈值的候选区域
        idxs = np.delete(idxs, np.concatenate(([0], np.where(np.array(overlaps) > overlap_threshold)[0] + 1)))
    # 返回最终的候选区域
    bboxes = [bboxes[i] for i in picked_idxs]
    return bboxes

以上就是RCNN的Python源代码示例，其中主要使用了OpenCV和Caffe两个库。这里的代码仅供参考，具体实现还需要根据具体的任务和数据进行相应的调整和优化。