Update get_map.py

get_map.py

-import argparse
 import glob
 import json
-import math
-import operator
 import os
 import shutil
+import operator
 import sys
+import argparse
+import math
 
 import numpy as np
 
-#----------------------------------------------------#
-#   用于计算mAP
-#   代码克隆自https://github.com/Cartucho/mAP
-#----------------------------------------------------#
-MINOVERLAP = 0.5 # default value (defined in the PASCAL VOC2012 challenge)
+'''
+用于计算mAP
+代码克隆自https://github.com/Cartucho/mAP
+如果想要设定mAP0.x，比如计算mAP0.75，可以设定MINOVERLAP = 0.75。
+'''
+MINOVERLAP = 0.5
 
 parser = argparse.ArgumentParser()
 parser.add_argument('-na', '--no-animation', help="no animation is shown.", action="store_true")
 parser.add_argument('-np', '--no-plot', help="no plot is shown.", action="store_true")
 parser.add_argument('-q', '--quiet', help="minimalistic console output.", action="store_true")
-# argparse receiving list of classes to be ignored
 parser.add_argument('-i', '--ignore', nargs='+', type=str, help="ignore a list of classes.")
-# argparse receiving list of classes with specific IoU (e.g., python main.py --set-class-iou person 0.7)
 parser.add_argument('--set-class-iou', nargs='+', type=str, help="set IoU for a specific class.")
 args = parser.parse_args()
 
@@ -37,7 +36,6 @@ args = parser.parse_args()
                 (Right,Bottom)
 '''
 
-# if there are no classes to ignore then replace None by empty list
 if args.ignore is None:
     args.ignore = []
 
@@ -45,22 +43,18 @@ specific_iou_flagged = False
 if args.set_class_iou is not None:
     specific_iou_flagged = True
 
-# make sure that the cwd() is the location of the python script (so that every path makes sense)
 os.chdir(os.path.dirname(os.path.abspath(__file__)))
 
 GT_PATH = os.path.join(os.getcwd(), 'input', 'ground-truth')
 DR_PATH = os.path.join(os.getcwd(), 'input', 'detection-results')
-# if there are no images then no animation can be shown
 IMG_PATH = os.path.join(os.getcwd(), 'input', 'images-optional')
 if os.path.exists(IMG_PATH): 
     for dirpath, dirnames, files in os.walk(IMG_PATH):
         if not files:
-            # no image files found
             args.no_animation = True
 else:
     args.no_animation = True
 
-# try to import OpenCV if the user didn't choose the option --no-animation
 show_animation = False
 if not args.no_animation:
     try:
@@ -70,7 +64,6 @@ if not args.no_animation:
         print("\"opencv-python\" not found, please install to visualize the results.")
         args.no_animation = True
 
-# try to import Matplotlib if the user didn't choose the option --no-plot
 draw_plot = False
 if not args.no_plot:
     try:
@@ -98,7 +91,6 @@ def log_average_miss_rate(precision, fp_cumsum, num_images):
                Transactions on 34.4 (2012): 743 - 761.
     """
 
-    # if there were no detections of that class
     if precision.size == 0:
         lamr = 0
         mr = 1
@@ -111,14 +103,11 @@ def log_average_miss_rate(precision, fp_cumsum, num_images):
     fppi_tmp = np.insert(fppi, 0, -1.0)
     mr_tmp = np.insert(mr, 0, 1.0)
 
-    # Use 9 evenly spaced reference points in log-space
     ref = np.logspace(-2.0, 0.0, num = 9)
     for i, ref_i in enumerate(ref):
-        # np.where() will always find at least 1 index, since min(ref) = 0.01 and min(fppi_tmp) = -1.0
         j = np.where(fppi_tmp <= ref_i)[-1][-1]
         ref[i] = mr_tmp[j]
 
-    # log(0) is undefined, so we use the np.maximum(1e-10, ref)
     lamr = math.exp(np.mean(np.log(np.maximum(1e-10, ref))))
 
     return lamr, mr, fppi
@@ -172,10 +161,6 @@ def voc_ap(rec, prec):
         matlab: for i=numel(mpre)-1:-1:1
                     mpre(i)=max(mpre(i),mpre(i+1));
     """
-    # matlab indexes start in 1 but python in 0, so I have to do:
-    #     range(start=(len(mpre) - 2), end=0, step=-1)
-    # also the python function range excludes the end, resulting in:
-    #     range(start=(len(mpre) - 2), end=-1, step=-1)
     for i in range(len(mpre)-2, -1, -1):
         mpre[i] = max(mpre[i], mpre[i+1])
     """
@@ -386,10 +371,10 @@ for txt_file in ground_truth_files_list:
     for line in lines_list:
         try:
             if "difficult" in line:
-                    class_name, left, top, right, bottom, _difficult = line.split()
-                    is_difficult = True
+                class_name, left, top, right, bottom, _difficult = line.split()
+                is_difficult = True
             else:
-                    class_name, left, top, right, bottom = line.split()
+                class_name, left, top, right, bottom = line.split()
                     
         except:
             if "difficult" in line:
@@ -401,7 +386,8 @@ for txt_file in ground_truth_files_list:
                 left = line_split[-5]
                 class_name = ""
                 for name in line_split[:-5]:
-                    class_name += name
+                    class_name += name + " "
+                class_name = class_name[:-1]
                 is_difficult = True
             else:
                 line_split = line.split()
@@ -411,8 +397,8 @@ for txt_file in ground_truth_files_list:
                 left = line_split[-4]
                 class_name = ""
                 for name in line_split[:-4]:
-                    class_name += name
-        # check if class is in the ignore list, if yes skip
+                    class_name += name + " "
+                class_name = class_name[:-1]
         if class_name in args.ignore:
             continue
         bbox = left + " " + top + " " + right + " " +bottom
@@ -421,32 +407,25 @@ for txt_file in ground_truth_files_list:
                 is_difficult = False
         else:
                 bounding_boxes.append({"class_name":class_name, "bbox":bbox, "used":False})
-                # count that object
                 if class_name in gt_counter_per_class:
                     gt_counter_per_class[class_name] += 1
                 else:
-                    # if class didn't exist yet
                     gt_counter_per_class[class_name] = 1
 
                 if class_name not in already_seen_classes:
                     if class_name in counter_images_per_class:
                         counter_images_per_class[class_name] += 1
                     else:
-                        # if class didn't exist yet
                         counter_images_per_class[class_name] = 1
                     already_seen_classes.append(class_name)
 
 
-    # dump bounding_boxes into a ".json" file
     with open(TEMP_FILES_PATH + "/" + file_id + "_ground_truth.json", 'w') as outfile:
         json.dump(bounding_boxes, outfile)
 
 gt_classes = list(gt_counter_per_class.keys())
-# let's sort the classes alphabetically
 gt_classes = sorted(gt_classes)
 n_classes = len(gt_classes)
-#print(gt_classes)
-#print(gt_counter_per_class)
 
 """
  Check format of the flag --set-class-iou (if used)
@@ -476,15 +455,12 @@ if specific_iou_flagged:
  detection-results
      Load each of the detection-results files into a temporary ".json" file.
 """
-# get a list with the detection-results files
 dr_files_list = glob.glob(DR_PATH + '/*.txt')
 dr_files_list.sort()
 
 for class_index, class_name in enumerate(gt_classes):
     bounding_boxes = []
     for txt_file in dr_files_list:
-        #print(txt_file)
-        # the first time it checks if all the corresponding ground-truth files exist
         file_id = txt_file.split(".txt",1)[0]
         file_id = os.path.basename(os.path.normpath(file_id))
         temp_path = os.path.join(GT_PATH, (file_id + ".txt"))
@@ -506,14 +482,13 @@ for class_index, class_name in enumerate(gt_classes):
                 confidence = line_split[-5]
                 tmp_class_name = ""
                 for name in line_split[:-5]:
-                    tmp_class_name += name
+                    tmp_class_name += name + " "
+                tmp_class_name = tmp_class_name[:-1]
 
             if tmp_class_name == class_name:
-                #print("match")
                 bbox = left + " " + top + " " + right + " " +bottom
                 bounding_boxes.append({"confidence":confidence, "file_id":file_id, "bbox":bbox})
-                #print(bounding_boxes)
-    # sort detection-results by decreasing confidence
+
     bounding_boxes.sort(key=lambda x:float(x['confidence']), reverse=True)
     with open(TEMP_FILES_PATH + "/" + class_name + "_dr.json", 'w') as outfile:
         json.dump(bounding_boxes, outfile)
@@ -524,7 +499,6 @@ for class_index, class_name in enumerate(gt_classes):
 sum_AP = 0.0
 ap_dictionary = {}
 lamr_dictionary = {}
-# open file to store the results
 with open(results_files_path + "/results.txt", 'w') as results_file:
     results_file.write("# AP and precision/recall per class\n")
     count_true_positives = {}
@@ -536,12 +510,11 @@ with open(results_files_path + "/results.txt", 'w') as results_file:
         """
         dr_file = TEMP_FILES_PATH + "/" + class_name + "_dr.json"
         dr_data = json.load(open(dr_file))
-
         """
          Assign detection-results to ground-truth objects
         """
         nd = len(dr_data)
-        tp = [0] * nd # creates an array of zeros of size nd
+        tp = [0] * nd
         fp = [0] * nd
         score = [0] * nd
         score05_idx = 0
@@ -552,37 +525,28 @@ with open(results_files_path + "/results.txt", 'w') as results_file:
                 score05_idx = idx
 
             if show_animation:
-                # find ground truth image
                 ground_truth_img = glob.glob1(IMG_PATH, file_id + ".*")
-                #tifCounter = len(glob.glob1(myPath,"*.tif"))
                 if len(ground_truth_img) == 0:
                     error("Error. Image not found with id: " + file_id)
                 elif len(ground_truth_img) > 1:
                     error("Error. Multiple image with id: " + file_id)
-                else: # found image
-                    #print(IMG_PATH + "/" + ground_truth_img[0])
-                    # Load image
+                else:
                     img = cv2.imread(IMG_PATH + "/" + ground_truth_img[0])
-                    # load image with draws of multiple detections
                     img_cumulative_path = results_files_path + "/images/" + ground_truth_img[0]
                     if os.path.isfile(img_cumulative_path):
                         img_cumulative = cv2.imread(img_cumulative_path)
                     else:
                         img_cumulative = img.copy()
-                    # Add bottom border to image
                     bottom_border = 60
                     BLACK = [0, 0, 0]
                     img = cv2.copyMakeBorder(img, 0, bottom_border, 0, 0, cv2.BORDER_CONSTANT, value=BLACK)
-            # assign detection-results to ground truth object if any
-            # open ground-truth with that file_id
+
             gt_file = TEMP_FILES_PATH + "/" + file_id + "_ground_truth.json"
             ground_truth_data = json.load(open(gt_file))
             ovmax = -1
             gt_match = -1
-            # load detected object bounding-box
             bb = [ float(x) for x in detection["bbox"].split() ]
             for obj in ground_truth_data:
-                # look for a class_name match
                 if obj["class_name"] == class_name:
                     bbgt = [ float(x) for x in obj["bbox"].split() ]
                     bi = [max(bb[0],bbgt[0]), max(bb[1],bbgt[1]), min(bb[2],bbgt[2]), min(bb[3],bbgt[3])]
@@ -597,10 +561,8 @@ with open(results_files_path + "/results.txt", 'w') as results_file:
                             ovmax = ov
                             gt_match = obj
 
-            # assign detection as true positive/don't care/false positive
             if show_animation:
-                status = "NO MATCH FOUND!" # status is only used in the animation
-            # set minimum overlap
+                status = "NO MATCH FOUND!" 
             min_overlap = MINOVERLAP
             if specific_iou_flagged:
                 if class_name in specific_iou_classes:
@@ -608,23 +570,19 @@ with open(results_files_path + "/results.txt", 'w') as results_file:
                     min_overlap = float(iou_list[index])
             if ovmax >= min_overlap:
                 if "difficult" not in gt_match:
-                        if not bool(gt_match["used"]):
-                            # true positive
-                            tp[idx] = 1
-                            gt_match["used"] = True
-                            count_true_positives[class_name] += 1
-                            # update the ".json" file
-                            with open(gt_file, 'w') as f:
-                                    f.write(json.dumps(ground_truth_data))
-                            if show_animation:
-                                status = "MATCH!"
-                        else:
-                            # false positive (multiple detection)
-                            fp[idx] = 1
-                            if show_animation:
-                                status = "REPEATED MATCH!"
+                    if not bool(gt_match["used"]):
+                        tp[idx] = 1
+                        gt_match["used"] = True
+                        count_true_positives[class_name] += 1
+                        with open(gt_file, 'w') as f:
+                                f.write(json.dumps(ground_truth_data))
+                        if show_animation:
+                            status = "MATCH!"
+                    else:
+                        fp[idx] = 1
+                        if show_animation:
+                            status = "REPEATED MATCH!"
             else:
-                # false positive
                 fp[idx] = 1
                 if ovmax > 0:
                     status = "INSUFFICIENT OVERLAP"
@@ -684,27 +642,26 @@ with open(results_files_path + "/results.txt", 'w') as results_file:
                 # save the image with all the objects drawn to it
                 cv2.imwrite(img_cumulative_path, img_cumulative)
 
-        # compute precision/recall
         cumsum = 0
         for idx, val in enumerate(fp):
             fp[idx] += cumsum
             cumsum += val
+
         cumsum = 0
         for idx, val in enumerate(tp):
             tp[idx] += cumsum
             cumsum += val
-        #print(tp)
-        rec = tp[:]
 
+        rec = tp[:]
         for idx, val in enumerate(tp):
-            rec[idx] = float(tp[idx]) / gt_counter_per_class[class_name]
-        #print(rec)
+            rec[idx] = float(tp[idx]) / np.maximum(gt_counter_per_class[class_name], 1)
+
         prec = tp[:]
         for idx, val in enumerate(tp):
-            prec[idx] = float(tp[idx]) / (fp[idx] + tp[idx])
-        #print(prec)
+            prec[idx] = float(tp[idx]) / np.maximum((fp[idx] + tp[idx]), 1)
+
         ap, mrec, mprec = voc_ap(rec[:], prec[:])
-        F1 = np.array(rec)*np.array(prec)/(np.array(prec)+np.array(rec))*2
+        F1 = np.array(rec)*np.array(prec)*2 / np.where((np.array(prec)+np.array(rec))==0, 1, (np.array(prec)+np.array(rec)))
 
         sum_AP += ap
         text = "{0:.2f}%".format(ap*100) + " = " + class_name + " AP " #class_name + " AP = {0:.2f}%".format(ap*100)
@@ -717,16 +674,16 @@ with open(results_files_path + "/results.txt", 'w') as results_file:
             F1_text = "0.00" + " = " + class_name + " F1 " 
             Recall_text = "0.00%" + " = " + class_name + " Recall " 
             Precision_text = "0.00%" + " = " + class_name + " Precision " 
-        """
-         Write to results.txt
-        """
+
         rounded_prec = [ '%.2f' % elem for elem in prec ]
         rounded_rec = [ '%.2f' % elem for elem in rec ]
         results_file.write(text + "\n Precision: " + str(rounded_prec) + "\n Recall :" + str(rounded_rec) + "\n\n")
         if not args.quiet:
-            if(len(rec)!=0):
+            if len(prec)>0:
                 print(text + "\t||\tscore_threhold=0.5 : " + "F1=" + "{0:.2f}".format(F1[score05_idx])\
                     + " ; Recall=" + "{0:.2f}%".format(rec[score05_idx]*100) + " ; Precision=" + "{0:.2f}%".format(prec[score05_idx]*100))
+            else:
+                print(text + "\t||\tscore_threhold=0.5 : F1=0.00% ; Recall=0.00% ; Precision=0.00%")
         ap_dictionary[class_name] = ap
 
         n_images = counter_images_per_class[class_name]
@@ -738,63 +695,52 @@ with open(results_files_path + "/results.txt", 'w') as results_file:
         """
         if draw_plot:
             plt.plot(rec, prec, '-o')
-            # add a new penultimate point to the list (mrec[-2], 0.0)
-            # since the last line segment (and respective area) do not affect the AP value
             area_under_curve_x = mrec[:-1] + [mrec[-2]] + [mrec[-1]]
             area_under_curve_y = mprec[:-1] + [0.0] + [mprec[-1]]
             plt.fill_between(area_under_curve_x, 0, area_under_curve_y, alpha=0.2, edgecolor='r')
-            # set window title
-            fig = plt.gcf() # gcf - get current figure
+
+            fig = plt.gcf()
             fig.canvas.set_window_title('AP ' + class_name)
-            # set plot title
+
             plt.title('class: ' + text)
-            #plt.suptitle('This is a somewhat long figure title', fontsize=16)
-            # set axis titles
             plt.xlabel('Recall')
             plt.ylabel('Precision')
-            # optional - set axes
-            axes = plt.gca() # gca - get current axes
+            axes = plt.gca()
             axes.set_xlim([0.0,1.0])
-            axes.set_ylim([0.0,1.05]) # .05 to give some extra space
-            # Alternative option -> wait for button to be pressed
-            # while not plt.waitforbuttonpress(): pass # wait for key display
-            # Alternative option -> normal display
-            # plt.show()
-
-            # save the plot
+            axes.set_ylim([0.0,1.05]) 
             fig.savefig(results_files_path + "/AP/" + class_name + ".png")
-            plt.cla() # clear axes for next plot
+            plt.cla()
 
             plt.plot(score, F1, "-", color='orangered')
             plt.title('class: ' + F1_text + "\nscore_threhold=0.5")
             plt.xlabel('Score_Threhold')
             plt.ylabel('F1')
-            axes = plt.gca() # gca - get current axes
+            axes = plt.gca()
             axes.set_xlim([0.0,1.0])
-            axes.set_ylim([0.0,1.05]) # .05 to give some extra space
+            axes.set_ylim([0.0,1.05])
             fig.savefig(results_files_path + "/F1/" + class_name + ".png")
-            plt.cla() # clear axes for next plot
+            plt.cla()
 
             plt.plot(score, rec, "-H", color='gold')
             plt.title('class: ' + Recall_text + "\nscore_threhold=0.5")
             plt.xlabel('Score_Threhold')
             plt.ylabel('Recall')
-            axes = plt.gca() # gca - get current axes
+            axes = plt.gca()
             axes.set_xlim([0.0,1.0])
-            axes.set_ylim([0.0,1.05]) # .05 to give some extra space
+            axes.set_ylim([0.0,1.05])
             fig.savefig(results_files_path + "/Recall/" + class_name + ".png")
-            plt.cla() # clear axes for next plot
+            plt.cla()
 
             plt.plot(score, prec, "-s", color='palevioletred')
             plt.title('class: ' + Precision_text + "\nscore_threhold=0.5")
             plt.xlabel('Score_Threhold')
             plt.ylabel('Precision')
-            axes = plt.gca() # gca - get current axes
+            axes = plt.gca()
             axes.set_xlim([0.0,1.0])
-            axes.set_ylim([0.0,1.05]) # .05 to give some extra space
+            axes.set_ylim([0.0,1.05])
             fig.savefig(results_files_path + "/Precision/" + class_name + ".png")
-            plt.cla() # clear axes for next plot
-
+            plt.cla()
+            
     if show_animation:
         cv2.destroyAllWindows()