python 实现图片上画出标注文档中的数据

import matplotlib.pyplot as plt
import cv2
import numpy as np
import json

def process_api_return_value(str):

    class Stack(object):
        def __init__(self):
            self.stack = []

        def isEmpty(self):
            return self.stack ==[]

        def push(self, item):
            self.stack.append(item)

        def pop(self):
            if self.isEmpty():
                print(‘stack is empty‘)
                return
            return self.stack.pop()
        def peek(self):
            return self.stack[-1]

        def size(self):
            return len(self.stack)




    my_list = []
    stack = Stack()
    begin = 0

    for index, value in enumerate(str):
        if value == ‘{‘:
            if stack.isEmpty():
                begin=index

            stack.push(value)

        if value == ‘}‘:
            stack.pop()
            if stack.isEmpty():
                end =index
                my_list.append(str[begin:end + 1])
    boxes_list = []
    classes = []

    for value in my_list:
        boxes_list_each = []
        dic = json.loads(value)
        classes.append(dic[‘label‘])

        x1 = dic[‘X1‘]
        x2 = dic[‘X2‘]
        y1 = dic[‘Y1‘]
        y2 = dic[‘Y2‘]


        boxes_list_each.append(x1)
        boxes_list_each.append(y1)
        boxes_list_each.append(x2)
        boxes_list_each.append(y2)


        boxes_list_each.append(dic[‘score‘])


        boxes_list.append(boxes_list_each)

    boxes =np.array(boxes_list)

    return boxes, classes

def get_class_string(class_name, score):
    return class_name + ‘ {:0.2f}‘.format(score).lstrip(‘0‘)



def vis_one_image(im, class_names,
                    boxes, classes,
                        thresh=0.9, dpi=100,
                            box_alpha=1.0, show_class=True,
                               filename=None, ext=‘png‘ ):
    if boxes is None or boxes.shape[0] ==0 or max(boxes[:, 4]) < thresh:
        return
    im = im[:, :, ::-1]
    fig = plt.figure(frameon=False)
    fig.set_size_inches(im.shape[1] /dpi, im.shape[0] / dpi) #shape[1]宽 shape[1]高
    ax=plt.Axes(fig, [0., 0., 1., 1.])
    ax.axis(‘off‘)
    fig.add_axes(ax)
    ax.imshow(im)

    areas =(boxes[:, 2] -boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
    sorted_inds = np.argsort(-areas) #big to small

    for i in sorted_inds:
        bbox = boxes[i, :4]
        score = boxes[i, -1]
        if score < thresh:
            contiune
            
        ax.add_patch(
            plt.Rectangle((bbox[0], bbox[1]),
                          bbox[2] - bbox[0],
                          bbox[3] - bbox[1],
                          fill=False, edgecolor= ‘g‘,
                          linewidth=1.0, alpha=box_alpha))

        if show_class:
            ax.text(
                bbox[0], bbox[1] - 2,
                get_class_string(class_names[classes[i]], score),
                fontsize=11,
                family= ‘serif‘,
                bbox=dict(
                    facecolor=‘g‘, alpha=0.4, pad=0, edgecolor=‘none‘),
                color=‘white‘)

    if filename is not None:
        fig.savefig(filename + ‘.‘ + ext, dpi = dpi)
        plt.close(‘all‘)

    else:
        plt.imshow(im)
        plt.show()






if __name__ == ‘__main__‘:
    class_names = [‘__background__‘,  # always index 0
                    ‘bai_sui_shan‘,‘cestbon‘,‘cocacola‘,‘jing_tian‘,‘pepsi_cola‘,‘sprite‘, ‘starbucks_black_tea‘,‘starbucks_matcha‘,
                   ‘starbucks_mocha‘, ‘vita_lemon_tea‘, ‘vita_soymilk_blue‘, ‘wanglaoji_green‘]
    raw_image = cv2.imread("/home/stt/data/wh_t/img/1.jpg")
    api_return_value = ‘{"X1":317.0668,"X2":334.80807,"Y1":111.80373,"Y2":138.84392,"label":1,"rotate":0.0,"scale":1.0,"score":0.9973503},{"X1":209.34439,"X2":233.45705,"Y1":98.4779,"Y2":123.20604,"label":4,"rotate":0.0,"scale":1.0,"score":0.98664963},{"X1":120.77178,"X2":154.04027,"Y1":159.96211,"Y2":227.42447,"label":12,"rotate":0.0,"scale":1.0,"score":0.37864015},{"X1":161.31192,"X2":200.91737,"Y1":31.158348,"Y2":51.811104,"label":1,"rotate":0.0,"scale":1.0,"score":0.30787778}‘


    boxes, classes = process_api_return_value(api_return_value)
    vis_one_image(raw_image, class_names, boxes, classes, thresh=0.0, box_alpha=1.0, show_class=True)