如何从一张图片中提取出所有跟另一张给定图片类似的物体, 给出python代码

这个问题可以使用计算机视觉中的目标检测技术来解决。常见的目标检测算法有基于深度学习的SSD、YOLO、Faster R-CNN等。这里以Faster R-CNN为例，给出Python代码实现。

首先，需要安装一些必要的Python库，包括tensorflow、keras、matplotlib等。

pip install tensorflow keras matplotlib

然后，需要下载Faster R-CNN模型和预训练权重。这里使用Keras提供的frcnn模块，可以直接从GitHub上下载。

git clone https://github.com/yhenon/keras-frcnn.git

接着，需要定义一些辅助函数，用于读取图片、将目标框转换为坐标等操作。

import cv2
import numpy as np

def format_img_size(img, C):
    """调整输入图片的大小"""
    img_min_side = float(C.im_size)
    (height,width,_) = img.shape
    
    if width <= height:
        ratio = img_min_side/width
        new_height = int(ratio * height)
        new_width = int(img_min_side)
    else:
        ratio = img_min_side/height
        new_width = int(ratio * width)
        new_height = int(img_min_side)
    img = cv2.resize(img, (new_width, new_height), interpolation=cv2.INTER_CUBIC)
    return img, ratio

def format_img_channels(img, C):
    """将输入图片的通道数调整为3"""
    img = np.transpose(img, (2, 0, 1))
    img = np.expand_dims(img, axis=0)
    img = img[:,:,:,:3]
    img = img.astype(np.float32)
    img[:, :, :, 0] -= C.img_channel_mean[0]
    img[:, :, :, 1] -= C.img_channel_mean[1]
    img[:, :, :, 2] -= C.img_channel_mean[2]
    img /= C.img_scaling_factor
    return img

def get_real_coordinates(ratio, x1, y1, x2, y2):
    """将目标框的坐标还原到原始图片上"""
    real_x1 = int(round(x1 // ratio))
    real_y1 = int(round(y1 // ratio))
    real_x2 = int(round(x2 // ratio))
    real_y2 = int(round(y2 // ratio))
    return (real_x1, real_y1, real_x2 ,real_y2)

最后，使用Faster R-CNN模型检测图片中的物体，并输出与给定图片相似的物体。

from keras import backend as K
from keras_frcnn import config, data_generators
from keras_frcnn import roi_helpers
from keras_frcnn import roi_helpers
from keras_frcnn.models import rpn, classifier
from keras_frcnn.utils import simple_parser
from keras_frcnn.visualize import draw_boxes

def detect_similar_objects(img_path, given_img_path, overlap_thresh=0.5):
    # 加载配置
    config_output_filename = "config.pickle"
    with open(config_output_filename, 'rb') as f_in:
        C = pickle.load(f_in)

    # 加载模型
    model_path = "model_frcnn.hdf5"
    model_rpn, model_classifier_only, model_classifier = rpn.rpn_model(C, num_classes=len(C.class_mapping))
    model_rpn.load_weights(model_path, by_name=True)
    model_classifier.load_weights(model_path, by_name=True)

    # 读取图片
    img = cv2.imread(img_path)
    given_img = cv2.imread(given_img_path)

    # 调整图片大小和通道数
    X, ratio = format_img_size(img, C)
    X = format_img_channels(X, C)

    # 在图片上进行目标检测
    _, _, _, rpn_out = model_rpn.predict(X)
    rois = roi_helpers.rpn_to_roi(rpn_out[0], rpn_out[1], C, K.image_dim_ordering(), overlap_thresh=overlap_thresh)
    rois = np.expand_dims(rois, axis=0)
    _, _, _, _, _, probs = model_classifier.predict([X, rois])

    # 输出与给定图片相似的物体
    similar_objs = []
    for i in range(probs.shape[1]):
        class_idx = np.argmax(probs[0,i,:])
        if C.class_mapping[class_idx] == "bg":
            continue
        cls_name = C.class_mapping[class_idx]
        if cls_name not in similar_objs:
            img_box = rois[0, i, :]
            (x1, y1, x2, y2) = get_real_coordinates(ratio, img_box[0], img_box[1], img_box[2], img_box[3])
            obj = img[y1:y2, x1:x2, :]
            obj_gray = cv2.cvtColor(obj, cv2.COLOR_BGR2GRAY)
            given_gray = cv2.cvtColor(given_img, cv2.COLOR_BGR2GRAY)
            if obj_gray.shape == given_gray.shape:
                diff = cv2.absdiff(obj_gray, given_gray)
                if np.sum(diff) == 0:
                    similar_objs.append(cls_name)
    return similar_objs

使用示例：

img_path = "test.jpg"
given_img_path = "given.jpg"
similar_objs = detect_similar_objects(img_path, given_img_path)
print(similar_objs)

输出结果为与给定图片相似的物体名称列表。