Add files via upload

get_miou.py

@@ -5,7 +5,7 @@
 from tqdm import tqdm
 
 from pspnet import Pspnet
-from utils.utils_metrics import compute_mIoU
+from utils.utils_metrics import compute_mIoU, show_results
 
 gpus = tf.config.experimental.list_physical_devices(device_type='GPU')
 for gpu in gpus:
@@ -39,9 +39,10 @@
     #-------------------------------------------------------#
     VOCdevkit_path  = 'VOCdevkit'
 
-    image_ids   = open(os.path.join(VOCdevkit_path, "VOC2007/ImageSets/Segmentation/val.txt"),'r').read().splitlines() 
-    gt_dir      = os.path.join(VOCdevkit_path, "VOC2007/SegmentationClass/")
-    pred_dir    = "miou_out"
+    image_ids       = open(os.path.join(VOCdevkit_path, "VOC2007/ImageSets/Segmentation/val.txt"),'r').read().splitlines() 
+    gt_dir          = os.path.join(VOCdevkit_path, "VOC2007/SegmentationClass/")
+    miou_out_path   = "miou_out"
+    pred_dir        = os.path.join(miou_out_path, 'detection-results')
 
     if miou_mode == 0 or miou_mode == 1:
         if not os.path.exists(pred_dir):
@@ -61,5 +62,6 @@
 
     if miou_mode == 0 or miou_mode == 2:
         print("Get miou.")
-        compute_mIoU(gt_dir, pred_dir, image_ids, num_classes, name_classes)  # 执行计算mIoU的函数
+        hist, IoUs, PA_Recall, Precision = compute_mIoU(gt_dir, pred_dir, image_ids, num_classes, name_classes)  # 执行计算mIoU的函数
         print("Get miou done.")
+        show_results(miou_out_path, hist, IoUs, PA_Recall, Precision, name_classes)

utils/utils_metrics.py

@@ -1,5 +1,8 @@
+import csv
+import os
 from os.path import join
 
+import matplotlib.pyplot as plt
 import numpy as np
 from PIL import Image
 from tensorflow.keras import backend
@@ -46,9 +49,15 @@ def fast_hist(a, b, n):
 def per_class_iu(hist):
     return np.diag(hist) / np.maximum((hist.sum(1) + hist.sum(0) - np.diag(hist)), 1) 
 
-def per_class_PA(hist):
+def per_class_PA_Recall(hist):
     return np.diag(hist) / np.maximum(hist.sum(1), 1) 
 
+def per_class_Precision(hist):
+    return np.diag(hist) / np.maximum(hist.sum(0), 1) 
+
+def per_Accuracy(hist):
+    return np.sum(np.diag(hist)) / np.maximum(np.sum(hist), 1) 
+
 def compute_mIoU(gt_dir, pred_dir, png_name_list, num_classes, name_classes):  
     print('Num classes', num_classes)  
     #-----------------------------------------#
@@ -90,22 +99,87 @@ def compute_mIoU(gt_dir, pred_dir, png_name_list, num_classes, name_classes):
         hist += fast_hist(label.flatten(), pred.flatten(),num_classes)  
         # 每计算10张就输出一下目前已计算的图片中所有类别平均的mIoU值
         if ind > 0 and ind % 10 == 0:  
-            print('{:d} / {:d}: mIou-{:0.2f}; mPA-{:0.2f}'.format(ind, len(gt_imgs),
-                                                    100 * np.nanmean(per_class_iu(hist)),
-                                                    100 * np.nanmean(per_class_PA(hist))))
+            print('{:d} / {:d}: mIou-{:0.2f}%; mPA-{:0.2f}%; Accuracy-{:0.2f}%'.format(
+                    ind, 
+                    len(gt_imgs),
+                    100 * np.nanmean(per_class_iu(hist)),
+                    100 * np.nanmean(per_class_PA_Recall(hist)),
+                    100 * per_Accuracy(hist)
+                )
+            )
     #------------------------------------------------#
     #   计算所有验证集图片的逐类别mIoU值
     #------------------------------------------------#
-    mIoUs   = per_class_iu(hist)
-    mPA     = per_class_PA(hist)
+    IoUs        = per_class_iu(hist)
+    PA_Recall   = per_class_PA_Recall(hist)
+    Precision   = per_class_Precision(hist)
     #------------------------------------------------#
     #   逐类别输出一下mIoU值
     #------------------------------------------------#
     for ind_class in range(num_classes):
-        print('===>' + name_classes[ind_class] + ':\tmIou-' + str(round(mIoUs[ind_class] * 100, 2)) + '; mPA-' + str(round(mPA[ind_class] * 100, 2)))
+        print('===>' + name_classes[ind_class] + ':\tIou-' + str(round(IoUs[ind_class] * 100, 2)) \
+            + '; Recall (equal to the PA)-' + str(round(PA_Recall[ind_class] * 100, 2))+ '; Precision-' + str(round(Precision[ind_class] * 100, 2)))
 
     #-----------------------------------------------------------------#
     #   在所有验证集图像上求所有类别平均的mIoU值，计算时忽略NaN值
     #-----------------------------------------------------------------#
-    print('===> mIoU: ' + str(round(np.nanmean(mIoUs) * 100, 2)) + '; mPA: ' + str(round(np.nanmean(mPA) * 100, 2)))  
-    return mIoUs
+    print('===> mIoU: ' + str(round(np.nanmean(IoUs) * 100, 2)) + '; mPA: ' + str(round(np.nanmean(PA_Recall) * 100, 2)) + '; Accuracy: ' + str(round(per_Accuracy(hist) * 100, 2)))  
+    return np.array(hist, np.int), IoUs, PA_Recall, Precision
+
+def adjust_axes(r, t, fig, axes):
+    bb                  = t.get_window_extent(renderer=r)
+    text_width_inches   = bb.width / fig.dpi
+    current_fig_width   = fig.get_figwidth()
+    new_fig_width       = current_fig_width + text_width_inches
+    propotion           = new_fig_width / current_fig_width
+    x_lim               = axes.get_xlim()
+    axes.set_xlim([x_lim[0], x_lim[1] * propotion])
+
+def draw_plot_func(values, name_classes, plot_title, x_label, output_path, tick_font_size = 12, plt_show = True):
+    fig     = plt.gcf() 
+    axes    = plt.gca()
+    plt.barh(range(len(values)), values, color='royalblue')
+    plt.title(plot_title, fontsize=tick_font_size + 2)
+    plt.xlabel(x_label, fontsize=tick_font_size)
+    plt.yticks(range(len(values)), name_classes, fontsize=tick_font_size)
+    r = fig.canvas.get_renderer()
+    for i, val in enumerate(values):
+        str_val = " " + str(val) 
+        if val < 1.0:
+            str_val = " {0:.2f}".format(val)
+        t = plt.text(val, i, str_val, color='royalblue', va='center', fontweight='bold')
+        if i == (len(values)-1):
+            adjust_axes(r, t, fig, axes)
+
+    fig.tight_layout()
+    fig.savefig(output_path)
+    if plt_show:
+        plt.show()
+    plt.close()
+
+def show_results(miou_out_path, hist, IoUs, PA_Recall, Precision, name_classes, tick_font_size = 12):
+    draw_plot_func(IoUs, name_classes, "mIoU = {0:.2f}%".format(np.nanmean(IoUs)*100), "Intersection over Union", \
+        os.path.join(miou_out_path, "mIoU.png"), tick_font_size = tick_font_size, plt_show = True)
+    print("Save mIoU out to " + os.path.join(miou_out_path, "mIoU.png"))
+
+    draw_plot_func(PA_Recall, name_classes, "mPA = {0:.2f}%".format(np.nanmean(PA_Recall)*100), "Pixel Accuracy", \
+        os.path.join(miou_out_path, "mPA.png"), tick_font_size = tick_font_size, plt_show = False)
+    print("Save mPA out to " + os.path.join(miou_out_path, "mPA.png"))
+
+    draw_plot_func(PA_Recall, name_classes, "mRecall = {0:.2f}%".format(np.nanmean(PA_Recall)*100), "Recall", \
+        os.path.join(miou_out_path, "Recall.png"), tick_font_size = tick_font_size, plt_show = False)
+    print("Save Recall out to " + os.path.join(miou_out_path, "Recall.png"))
+
+    draw_plot_func(Precision, name_classes, "mPrecision = {0:.2f}%".format(np.nanmean(Precision)*100), "Precision", \
+        os.path.join(miou_out_path, "Precision.png"), tick_font_size = tick_font_size, plt_show = False)
+    print("Save Precision out to " + os.path.join(miou_out_path, "Precision.png"))
+
+    with open(os.path.join(miou_out_path, "confusion_matrix.csv"), 'w', newline='') as f:
+        writer          = csv.writer(f)
+        writer_list     = []
+        writer_list.append([' '] + [str(c) for c in name_classes])
+        for i in range(len(hist)):
+            writer_list.append([name_classes[i]] + [str(x) for x in hist[i]])
+        writer.writerows(writer_list)
+    print("Save confusion_matrix out to " + os.path.join(miou_out_path, "confusion_matrix.csv"))
+