python 实现图片上画出标注文档中的数据

心已赠人 2021-12-14 06:25 185阅读 0赞

我的代码能力果然真的是菜啊

这个代码前前后后看了三天后，终于看懂并能自己code下来了

看懂了其实并不难，可是总是就是不想看

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)

转载于:https://www.cnblogs.com/stt-ac/p/11076582.html