init pro

README.md

+# classification   
+物体识别分类  
+
+## 项目介绍   
+该项目对物体进行识别分类。

read_datasets.py

+#-*-coding:utf-8-*-
+# date:2020-02-08
+# Author: Eric.Lee
+## function: read datasets label files
+
+import os
+import cv2
+import numpy as np
+import xml.etree.cElementTree as ET
+import time
+
+def plot_one_box(x, img, color=None, label=None, line_thickness=None):
+    # Plots one bounding box on image img
+    tl = line_thickness or round(0.002 * max(img.shape[0:2])) + 1  # line thickness
+    color = color or [random.randint(0, 255) for _ in range(3)]# color
+    c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
+    cv2.rectangle(img, c1, c2, color, thickness=tl)
+    if label:
+        tf = max(tl - 2, 1)  # font thickness
+        t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] # label size
+        c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 # 字体的bbox
+        cv2.rectangle(img, c1, c2, color, -1)  # filled rectangle
+        cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 4, [225, 255, 255],\
+        thickness=tf, lineType=cv2.LINE_AA)
+
+def get_xml_msg(path):
+    list_x = []
+    tree=ET.parse(path)# 解析 xml 文件
+    root=tree.getroot()
+    for Object in root.findall('object'):
+        name=Object.find('name').text
+        #----------------------------
+        bndbox=Object.find('bndbox')
+        xmin= np.float32((bndbox.find('xmin').text))
+        ymin= np.float32((bndbox.find('ymin').text))
+        xmax= np.float32((bndbox.find('xmax').text))
+        ymax= np.float32((bndbox.find('ymax').text))
+        bbox = int(xmin),int(ymin),int(xmax),int(ymax)
+        xyxy = xmin,ymin,xmax,ymax
+        list_x.append((name,xyxy))
+    return list_x
+
+if __name__ == "__main__":
+    path = "./datasets/train/"
+    idx = 0
+    data_dict = {} # 统计每一类的个数
+
+    for doc_ in os.listdir(path):# 遍历文件夹 文件
+        for f_ in os.listdir(path + doc_):
+            if ".jpg" not in f_:
+                continue
+            img_ = cv2.imread(path +doc_+"/"+ f_)
+
+            idx += 1
+            print('%s) '%(idx)+f_)
+            cv2.putText(img_, ('index : ' + str(idx)), (5,img_.shape[0]-5),cv2.FONT_HERSHEY_PLAIN, 1.8, (255, 255, 0), 6)
+            cv2.putText(img_, ('index : ' + str(idx)), (5,img_.shape[0]-5),cv2.FONT_HERSHEY_PLAIN, 1.8, (255, 60, 255), 2)
+
+            xml_ = path + doc_+"/"+f_.strip('.jpg').strip('.png')+'.xml'
+
+            list_x = get_xml_msg(xml_)# 获取 xml 文件 的 object
+
+            # 绘制 bbox
+            for j in range(len(list_x)):
+                label_,bbox_ = list_x[j]
+                plot_one_box(bbox_,img_, label=label_, color=(255,0,0))
+                if label_ not in data_dict.keys():
+                    data_dict[label_] = 1
+                else:
+                    data_dict[label_] += 1
+                cv2.namedWindow('image',0)
+                cv2.imshow('image',img_)
+                cv2.waitKey(1)
+
+    cv2.destroyAllWindows()
+
+    for key_ in data_dict.keys():
+        print(key_,':',data_dict[key_])