add_dir_predict_for_csv

data_analysis.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -49,10 +49,75 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'numpy.ndarray'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "label, top, left, bottom, right = 'jacket 0.51',538 ,528,707,616\n",
+    "data=(label, top, left, bottom, right)\n",
+    "array = np.asarray(data).reshape(1,5)\n",
+    "array = np.concatenate((array,array),axis=0)\n",
+    "print(type(array))\n",
+    "test=pd.DataFrame(array,columns=['label', 'top', 'left', 'bottom', 'right'])\n",
+    "test.to_csv('save1.csv', index=False, sep=',')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "import os\n",
+    "image_name = 'sadfhkj.jpg'\n",
+    "label, top, left, bottom, right = 'jacket 0.51',538 ,528,707,616\n",
+    "data=(image_name,label, top, left, bottom, right)\n",
+    "array = np.asarray(data).reshape(1,6)\n",
+    "array = np.concatenate((array,array),axis=0)\n",
+    "# image_name = np.asarray(image_name)\n",
+    "# array_image_name = np.concatenate((image_name,array),axis=1)\n",
+    "# label, top, left, bottom, right = array\n",
+    "# array_image_name = image_name + array\n",
+    "\n",
+    "test=pd.DataFrame(array,columns=['image_name','label', 'top', 'left', 'bottom', 'right'])\n",
+    "# test=pd.DataFrame(array)\n",
+    "if os.path.exists('save1.csv'):\n",
+    "    test.to_csv('save1.csv', index=False, sep=',',mode='a', header=None)\n",
+    "    \n",
+    "else:\n",
+    "    test.to_csv('save1.csv', index=False, sep=',',mode='a')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "os.path.exists('save1.csv')"
+   ]
   }
  ],
  "metadata": {

get_map.py

@@ -29,7 +29,7 @@
     #   此处的classes_path用于指定需要测量VOC_map的类别
     #   一般情况下与训练和预测所用的classes_path一致即可
     #--------------------------------------------------------------------------------------#
-    classes_path    = 'model_data/voc_classes.txt'
+    classes_path    = 'model_data/cowboy_classes.txt'
     #--------------------------------------------------------------------------------------#
     #   MINOVERLAP用于指定想要获得的mAP0.x，mAP0.x的意义是什么请同学们百度一下。
     #   比如计算mAP0.75，可以设定MINOVERLAP = 0.75。

predict.py

@@ -21,7 +21,7 @@
     #   'export_onnx'       表示将模型导出为onnx，需要pytorch1.7.1以上。
     #   'predict_onnx'      表示利用导出的onnx模型进行预测，相关参数的修改在yolo.py_423行左右处的YOLO_ONNX
     #----------------------------------------------------------------------------------------------------------#
-    mode = "predict"
+    mode = "dir_predict"
     #-------------------------------------------------------------------------#
     #   crop                指定了是否在单张图片预测后对目标进行截取
     #   count               指定了是否进行目标的计数
@@ -158,7 +158,7 @@
             if img_name.lower().endswith(('.bmp', '.dib', '.png', '.jpg', '.jpeg', '.pbm', '.pgm', '.ppm', '.tif', '.tiff')):
                 image_path  = os.path.join(dir_origin_path, img_name)
                 image       = Image.open(image_path)
-                r_image     = yolo.detect_image(image)
+                r_image     = yolo.detect_image(image,image_name=img_name)
                 if not os.path.exists(dir_save_path):
                     os.makedirs(dir_save_path)
                 r_image.save(os.path.join(dir_save_path, img_name.replace(".jpg", ".png")), quality=95, subsampling=0)

yolo.py

@@ -3,6 +3,9 @@
 os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
 import time
 
+import pandas as pd
+import numpy as np
+
 import cv2
 import numpy as np
 import torch
@@ -125,7 +128,7 @@ def generate(self, onnx=False):
     #---------------------------------------------------#
     #   检测图片
     #---------------------------------------------------#
-    def detect_image(self, image, crop = False, count = False):
+    def detect_image(self, image, crop = False, count = False, image_name = None):
         #---------------------------------------------------#
         #   计算输入图片的高和宽
         #---------------------------------------------------#
@@ -203,6 +206,8 @@ def detect_image(self, image, crop = False, count = False):
         #---------------------------------------------------------#
         #   图像绘制
         #---------------------------------------------------------#
+        # data_array = np.array(('',538 ,528,707,616))
+        # data_array = np.asarray(data_array).reshape(1,5)
         for i, c in list(enumerate(top_label)):
             predicted_class = self.class_names[int(c)]
             box             = top_boxes[i]
@@ -216,12 +221,20 @@ def detect_image(self, image, crop = False, count = False):
             right   = min(image.size[0], np.floor(right).astype('int32'))
 
             label = '{} {:.2f}'.format(predicted_class, score)
-            # print(label)
+
+            print(label, top, left, bottom, right)
+            data=([image_name],[label], [top], [left], [bottom], [right])
+            array = np.asarray(data).reshape(1,6)
+            if 'data_array' not in dir():       #查看变量有没有定义，没有就加一个定义
+                data_array = array
+            else:
+                data_array = np.concatenate((data_array,array),axis=0)
+
+
             draw = ImageDraw.Draw(image)
             label_size = draw.textsize(label, font)
             label = label.encode('utf-8')
-            # print("sss")
-            print(label, top, left, bottom, right)
+
 
             if top - label_size[1] >= 0:
                 text_origin = np.array([left, top - label_size[1]])
@@ -233,7 +246,13 @@ def detect_image(self, image, crop = False, count = False):
             draw.rectangle([tuple(text_origin), tuple(text_origin + label_size)], fill=self.colors[c])
             draw.text(text_origin, str(label,'UTF-8'), fill=(0, 0, 0), font=font)
             del draw
+        # if image_name != None:
 
+        test=pd.DataFrame(data_array,columns=['image_name','label', 'top', 'left', 'bottom', 'right'])
+        if os.path.exists('save1.csv'):
+            test.to_csv('save1.csv', index=False, sep=',',mode='a', header=None)
+        else:
+            test.to_csv('save1.csv', index=False, sep=',',mode='a')
         return image
 
     def get_FPS(self, image, test_interval):