MAP，AP的python代码实现

新建一个map文件夹，文件夹分别新建一个input和result文件夹，一个放输入数据，一个放输出结果

input下分别新建三个文件夹，分别是groung-truth,detection-results,images-optional;

left，top，right，bottom对应位置

groung-truth存放的分别是原始标注，格式是  []

detection-results存放的是检测的结果，格式是 

images-optional存放的是待检测的图片

具体代码为：

import glob
import json
import os
import shutil
import operator
import sys
import argparse
import mathimport numpy as npMINOVERLAP = 0.5 # default value (defined in the PASCAL VOC2012 challenge)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()# if there are no classes to ignore then replace None by empty list
if args.ignore is None:args.ignore = []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 foundargs.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:import cv2show_animation = Trueexcept ImportError: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:import matplotlib.pyplot as pltdraw_plot = Trueexcept ImportError:print("\"matplotlib\" not found, please install it to get the resulting plots.")args.no_plot = Truedef log_average_miss_rate(precision, fp_cumsum, num_images):"""log-average miss rate:Calculated by averaging miss rates at 9 evenly spaced FPPI pointsbetween 10e-2 and 10e0, in log-space.output:lamr | log-average miss ratemr | miss ratefppi | false positives per imagereferences:[1] Dollar, Piotr, et al. "Pedestrian Detection: An Evaluation of theState of the Art." Pattern Analysis and Machine Intelligence, IEEETransactions on 34.4 (2012): 743 - 761."""# if there were no detections of that classif precision.size == 0:lamr = 0mr = 1fppi = 0return lamr, mr, fppifppi = fp_cumsum / float(num_images)mr = (1 - precision)fppi_tmp = np.insert(fppi, 0, -1.0)mr_tmp = np.insert(mr, 0, 1.0)# Use 9 evenly spaced reference points in log-spaceref = 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) = -1.0j = 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"""throw error and exit
"""
def error(msg):print(msg)sys.exit(0)"""check if the number is a float between 0.0 and 1.0
"""
def is_float_between_0_and_1(value):try:val = float(value)if val > 0.0 and val < 1.0:return Trueelse:return Falseexcept ValueError:return False"""Calculate the AP given the recall and precision array1st) We compute a version of the measured precision/recall curve withprecision monotonically decreasing2nd) We compute the AP as the area under this curve by numerical integration.
"""
def voc_ap(rec, prec):"""--- Official matlab code VOC2012---mrec=[0 ; rec ; 1];mpre=[0 ; prec ; 0];for i=numel(mpre)-1:-1:1mpre(i)=max(mpre(i),mpre(i+1));endi=find(mrec(2:end)~=mrec(1:end-1))+1;ap=sum((mrec(i)-mrec(i-1)).*mpre(i));"""rec.insert(0, 0.0) # insert 0.0 at begining of listrec.append(1.0) # insert 1.0 at end of listmrec = rec[:]prec.insert(0, 0.0) # insert 0.0 at begining of listprec.append(0.0) # insert 0.0 at end of listmpre = prec[:]"""This part makes the precision monotonically decreasing(goes from the end to the beginning)matlab: for i=numel(mpre)-1:-1:1mpre(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])"""This part creates a list of indexes where the recall changesmatlab: i=find(mrec(2:end)~=mrec(1:end-1))+1;"""i_list = []for i in range(1, len(mrec)):if mrec[i] != mrec[i-1]:i_list.append(i) # if it was matlab would be i + 1"""The Average Precision (AP) is the area under the curve(numerical integration)matlab: ap=sum((mrec(i)-mrec(i-1)).*mpre(i));"""ap = 0.0for i in i_list:ap += ((mrec[i]-mrec[i-1])*mpre[i])return ap, mrec, mpre"""Convert the lines of a file to a list
"""
def file_lines_to_list(path):# open txt file lines to a listwith open(path) as f:content = f.readlines()# remove whitespace characters like `\n` at the end of each linecontent = [x.strip() for x in content]return content"""Draws text in image
"""
def draw_text_in_image(img, text, pos, color, line_width):font = cv2.FONT_HERSHEY_PLAINfontScale = 1lineType = 1bottomLeftCornerOfText = poscv2.putText(img, text,bottomLeftCornerOfText,font,fontScale,color,lineType)text_width, _ = cv2.getTextSize(text, font, fontScale, lineType)[0]return img, (line_width + text_width)"""Plot - adjust axes
"""
def adjust_axes(r, t, fig, axes):# get text width for re-scalingbb = t.get_window_extent(renderer=r)text_width_inches = bb.width / fig.dpi# get axis width in inchescurrent_fig_width = fig.get_figwidth()new_fig_width = current_fig_width + text_width_inchespropotion = new_fig_width / current_fig_width# get axis limitx_lim = axes.get_xlim()axes.set_xlim([x_lim[0], x_lim[1]*propotion])"""Draw plot using Matplotlib
"""
def draw_plot_func(dictionary, n_classes, window_title, plot_title, x_label, output_path, to_show, plot_color, true_p_bar):# sort the dictionary by decreasing value, into a list of tuplessorted_dic_by_value = sorted(dictionary.items(), key=operator.itemgetter(1))# unpacking the list of tuples into two listssorted_keys, sorted_values = zip(*sorted_dic_by_value)#if true_p_bar != "":"""Special case to draw in:- green -> TP: True Positives (object detected and matches ground-truth)- red -> FP: False Positives (object detected but does not match ground-truth)- orange -> FN: False Negatives (object not detected but present in the ground-truth)"""fp_sorted = []tp_sorted = []for key in sorted_keys:fp_sorted.append(dictionary[key] - true_p_bar[key])tp_sorted.append(true_p_bar[key])plt.barh(range(n_classes), fp_sorted, align='center', color='crimson', label='False Positive')plt.barh(range(n_classes), tp_sorted, align='center', color='forestgreen', label='True Positive', left=fp_sorted)# add legendplt.legend(loc='lower right')"""Write number on side of bar"""fig = plt.gcf() # gcf - get current figureaxes = plt.gca()r = fig.canvas.get_renderer()for i, val in enumerate(sorted_values):fp_val = fp_sorted[i]tp_val = tp_sorted[i]fp_str_val = " " + str(fp_val)tp_str_val = fp_str_val + " " + str(tp_val)# trick to paint multicolor with offset:# first paint everything and then repaint the first numbert = plt.text(val, i, tp_str_val, color='forestgreen', va='center', fontweight='bold')plt.text(val, i, fp_str_val, color='crimson', va='center', fontweight='bold')if i == (len(sorted_values)-1): # largest baradjust_axes(r, t, fig, axes)else:plt.barh(range(n_classes), sorted_values, color=plot_color)"""Write number on side of bar"""fig = plt.gcf() # gcf - get current figureaxes = plt.gca()r = fig.canvas.get_renderer()for i, val in enumerate(sorted_values):str_val = " " + str(val) # add a space beforeif val < 1.0:str_val = " {0:.2f}".format(val)t = plt.text(val, i, str_val, color=plot_color, va='center', fontweight='bold')# re-set axes to show number inside the figureif i == (len(sorted_values)-1): # largest baradjust_axes(r, t, fig, axes)# set window titlefig.canvas.set_window_title(window_title)# write classes in y axistick_font_size = 12plt.yticks(range(n_classes), sorted_keys, fontsize=tick_font_size)"""Re-scale height accordingly"""init_height = fig.get_figheight()# comput the matrix height in points and inchesdpi = fig.dpiheight_pt = n_classes * (tick_font_size * 1.4) # 1.4 (some spacing)height_in = height_pt / dpi# compute the required figure heighttop_margin = 0.15 # in percentage of the figure heightbottom_margin = 0.05 # in percentage of the figure heightfigure_height = height_in / (1 - top_margin - bottom_margin)# set new heightif figure_height > init_height:fig.set_figheight(figure_height)# set plot titleplt.title(plot_title, fontsize=14)# set axis titles# plt.xlabel('classes')plt.xlabel(x_label, fontsize='large')# adjust size of windowfig.tight_layout()# save the plotfig.savefig(output_path)# show imageif to_show:plt.show()# close the plotplt.close()"""Create a ".temp_files/" and "results/" directory
"""
TEMP_FILES_PATH = ".temp_files"
if not os.path.exists(TEMP_FILES_PATH): # if it doesn't exist alreadyos.makedirs(TEMP_FILES_PATH)
results_files_path = "results"
if os.path.exists(results_files_path): # if it exist already# reset the results directoryshutil.rmtree(results_files_path)os.makedirs(results_files_path)
if draw_plot:os.makedirs(os.path.join(results_files_path, "classes"))
if show_animation:os.makedirs(os.path.join(results_files_path, "images", "detections_one_by_one"))"""ground-truthLoad each of the ground-truth files into a temporary ".json" file.Create a list of all the class names present in the ground-truth (gt_classes).
"""
# get a list with the ground-truth files
ground_truth_files_list = glob.glob(GT_PATH + '/*.txt')
if len(ground_truth_files_list) == 0:error("Error: No ground-truth files found!")
ground_truth_files_list.sort()
# dictionary with counter per class
gt_counter_per_class = {}
counter_images_per_class = {}for txt_file in ground_truth_files_list:#print(txt_file)file_id = txt_file.split(".txt", 1)[0]file_id = os.path.basename(os.path.normpath(file_id))# check if there is a correspondent detection-results filetemp_path = os.path.join(DR_PATH, (file_id + ".txt"))if not os.path.exists(temp_path):error_msg = "Error. File not found: {}\n".format(temp_path)error_msg += "(You can avoid this error message by running extra/intersect-gt-and-dr.py)"error(error_msg)lines_list = file_lines_to_list(txt_file)# create ground-truth dictionarybounding_boxes = []is_difficult = Falsealready_seen_classes = []for line in lines_list:try:if "difficult" in line:class_name, left, top, right, bottom, _difficult = line.split()is_difficult = Trueelse:class_name, left, top, right, bottom = line.split()except ValueError:error_msg = "Error: File " + txt_file + " in the wrong format.\n"error_msg += " Expected:      ['difficult']\n"error_msg += " Received: " + lineerror_msg += "\n\nIf you have a  with spaces between words you should remove them\n"error_msg += "by running the script \"remove_space.py\" or \"rename_class.py\" in the \"extra/\" folder."error(error_msg)# check if class is in the ignore list, if yes skipif class_name in args.ignore:continuebbox = left + " " + top + " " + right + " " +bottomif is_difficult:bounding_boxes.append({"class_name":class_name, "bbox":bbox, "used":False, "difficult":True})is_difficult = Falseelse:bounding_boxes.append({"class_name":class_name, "bbox":bbox, "used":False})# count that objectif class_name in gt_counter_per_class:gt_counter_per_class[class_name] += 1else:# if class didn't exist yetgt_counter_per_class[class_name] = 1if class_name not in already_seen_classes:if class_name in counter_images_per_class:counter_images_per_class[class_name] += 1else:# if class didn't exist yetcounter_images_per_class[class_name] = 1already_seen_classes.append(class_name)# dump bounding_boxes into a ".json" filewith 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)e.g. check if class exists
"""
if specific_iou_flagged:n_args = len(args.set_class_iou)error_msg = \'\n --set-class-iou [class_1] [IoU_1] [class_2] [IoU_2] [...]'if n_args % 2 != 0:error('Error, missing arguments. Flag usage:' + error_msg)# [class_1] [IoU_1] [class_2] [IoU_2]# specific_iou_classes = ['class_1', 'class_2']specific_iou_classes = args.set_class_iou[::2] # even# iou_list = ['IoU_1', 'IoU_2']iou_list = args.set_class_iou[1::2] # oddif len(specific_iou_classes) != len(iou_list):error('Error, missing arguments. Flag usage:' + error_msg)for tmp_class in specific_iou_classes:if tmp_class not in gt_classes:error('Error, unknown class \"' + tmp_class + '\". Flag usage:' + error_msg)for num in iou_list:if not is_float_between_0_and_1(num):error('Error, IoU must be between 0.0 and 1.0. Flag usage:' + error_msg)"""detection-resultsLoad 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 existfile_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"))if class_index == 0:if not os.path.exists(temp_path):error_msg = "Error. File not found: {}\n".format(temp_path)error_msg += "(You can avoid this error message by running extra/intersect-gt-and-dr.py)"error(error_msg)lines = file_lines_to_list(txt_file)for line in lines:try:tmp_class_name, confidence, left, top, right, bottom = line.split()except ValueError:error_msg = "Error: File " + txt_file + " in the wrong format.\n"error_msg += " Expected:      \n"error_msg += " Received: " + lineerror(error_msg)if tmp_class_name == class_name:#print("match")bbox = left + " " + top + " " + right + " " +bottombounding_boxes.append({"confidence":confidence, "file_id":file_id, "bbox":bbox})#print(bounding_boxes)# sort detection-results by decreasing confidencebounding_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)"""Calculate the AP for each class
"""
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 = {}for class_index, class_name in enumerate(gt_classes):count_true_positives[class_name] = 0"""Load detection-results of that class"""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 ndfp = [0] * ndfor idx, detection in enumerate(dr_data):file_id = detection["file_id"]if show_animation:# find ground truth imageground_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 imageimg = cv2.imread(IMG_PATH + "/" + ground_truth_img[0])# load image with draws of multiple detectionsimg_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 imagebottom_border = 60BLACK = [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_idgt_file = TEMP_FILES_PATH + "/" + file_id + "_ground_truth.json"ground_truth_data = json.load(open(gt_file))ovmax = -1gt_match = -1# load detected object bounding-boxbb = [ float(x) for x in detection["bbox"].split() ]for obj in ground_truth_data:# look for a class_name matchif 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])]iw = bi[2] - bi[0] + 1ih = bi[3] - bi[1] + 1if iw > 0 and ih > 0:# compute overlap (IoU) = area of intersection / area of unionua = (bb[2] - bb[0] + 1) * (bb[3] - bb[1] + 1) + (bbgt[2] - bbgt[0]+ 1) * (bbgt[3] - bbgt[1] + 1) - iw * ihov = iw * ih / uaif ov > ovmax:ovmax = ovgt_match = obj# assign detection as true positive/don't care/false positiveif show_animation:status = "NO MATCH FOUND!" # status is only used in the animation# set minimum overlapmin_overlap = MINOVERLAPif specific_iou_flagged:if class_name in specific_iou_classes:index = specific_iou_classes.index(class_name)min_overlap = float(iou_list[index])if ovmax >= min_overlap:if "difficult" not in gt_match:if not bool(gt_match["used"]):# true positivetp[idx] = 1gt_match["used"] = Truecount_true_positives[class_name] += 1# update the ".json" filewith 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] = 1if show_animation:status = "REPEATED MATCH!"else:# false positivefp[idx] = 1if ovmax > 0:status = "INSUFFICIENT OVERLAP""""Draw image to show animation"""if show_animation:height, widht = img.shape[:2]# colors (OpenCV works with BGR)white = (255,255,255)light_blue = (255,200,100)green = (0,255,0)light_red = (30,30,255)# 1st linemargin = 10v_pos = int(height - margin - (bottom_border / 2.0))text = "Image: " + ground_truth_img[0] + " "img, line_width = draw_text_in_image(img, text, (margin, v_pos), white, 0)text = "Class [" + str(class_index) + "/" + str(n_classes) + "]: " + class_name + " "img, line_width = draw_text_in_image(img, text, (margin + line_width, v_pos), light_blue, line_width)if ovmax != -1:color = light_redif status == "INSUFFICIENT OVERLAP":text = "IoU: {0:.2f}% ".format(ovmax*100) + "< {0:.2f}% ".format(min_overlap*100)else:text = "IoU: {0:.2f}% ".format(ovmax*100) + ">= {0:.2f}% ".format(min_overlap*100)color = greenimg, _ = draw_text_in_image(img, text, (margin + line_width, v_pos), color, line_width)# 2nd linev_pos += int(bottom_border / 2.0)rank_pos = str(idx+1) # rank position (idx starts at 0)text = "Detection #rank: " + rank_pos + " confidence: {0:.2f}% ".format(float(detection["confidence"])*100)img, line_width = draw_text_in_image(img, text, (margin, v_pos), white, 0)color = light_redif status == "MATCH!":color = greentext = "Result: " + status + " "img, line_width = draw_text_in_image(img, text, (margin + line_width, v_pos), color, line_width)font = cv2.FONT_HERSHEY_SIMPLEXif ovmax > 0: # if there is intersections between the bounding-boxesbbgt = [ int(round(float(x))) for x in gt_match["bbox"].split() ]cv2.rectangle(img,(bbgt[0],bbgt[1]),(bbgt[2],bbgt[3]),light_blue,2)cv2.rectangle(img_cumulative,(bbgt[0],bbgt[1]),(bbgt[2],bbgt[3]),light_blue,2)cv2.putText(img_cumulative, class_name, (bbgt[0],bbgt[1] - 5), font, 0.6, light_blue, 1, cv2.LINE_AA)bb = [int(i) for i in bb]cv2.rectangle(img,(bb[0],bb[1]),(bb[2],bb[3]),color,2)cv2.rectangle(img_cumulative,(bb[0],bb[1]),(bb[2],bb[3]),color,2)cv2.putText(img_cumulative, class_name, (bb[0],bb[1] - 5), font, 0.6, color, 1, cv2.LINE_AA)# show imagecv2.imshow("Animation", img)cv2.waitKey(20) # show for 20 ms# save image to resultsoutput_img_path = results_files_path + "/images/detections_one_by_one/" + class_name + "_detection" + str(idx) + ".jpg"cv2.imwrite(output_img_path, img)# save the image with all the objects drawn to itcv2.imwrite(img_cumulative_path, img_cumulative)#print(tp)# compute precision/recallcumsum = 0for idx, val in enumerate(fp):fp[idx] += cumsumcumsum += valcumsum = 0for idx, val in enumerate(tp):tp[idx] += cumsumcumsum += val#print(tp)rec = tp[:]for idx, val in enumerate(tp):rec[idx] = float(tp[idx]) / gt_counter_per_class[class_name]#print(rec)prec = tp[:]for idx, val in enumerate(tp):prec[idx] = float(tp[idx]) / (fp[idx] + tp[idx])#print(prec)ap, mrec, mprec = voc_ap(rec[:], prec[:])sum_AP += aptext = "{0:.2f}%".format(ap*100) + " = " + class_name + " AP " #class_name + " AP = {0:.2f}%".format(ap*100)"""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:print(text)ap_dictionary[class_name] = apn_images = counter_images_per_class[class_name]lamr, mr, fppi = log_average_miss_rate(np.array(rec), np.array(fp), n_images)lamr_dictionary[class_name] = lamr"""Draw plot"""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 valuearea_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 titlefig = plt.gcf() # gcf - get current figurefig.canvas.set_window_title('AP ' + class_name)# set plot titleplt.title('class: ' + text)#plt.suptitle('This is a somewhat long figure title', fontsize=16)# set axis titlesplt.xlabel('Recall')plt.ylabel('Precision')# optional - set axesaxes = plt.gca() # gca - get current axesaxes.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 plotfig.savefig(results_files_path + "/classes/" + class_name + ".png")plt.cla() # clear axes for next plotif show_animation:cv2.destroyAllWindows()results_file.write("\n# mAP of all classes\n")mAP = sum_AP / n_classestext = "mAP = {0:.2f}%".format(mAP*100)results_file.write(text + "\n")print(text)# remove the temp_files directory
shutil.rmtree(TEMP_FILES_PATH)"""Count total of detection-results
"""
# iterate through all the files
det_counter_per_class = {}
for txt_file in dr_files_list:# get lines to listlines_list = file_lines_to_list(txt_file)for line in lines_list:class_name = line.split()[0]# check if class is in the ignore list, if yes skipif class_name in args.ignore:continue# count that objectif class_name in det_counter_per_class:det_counter_per_class[class_name] += 1else:# if class didn't exist yetdet_counter_per_class[class_name] = 1
#print(det_counter_per_class)
dr_classes = list(det_counter_per_class.keys())"""Plot the total number of occurences of each class in the ground-truth
"""
if draw_plot:window_title = "ground-truth-info"plot_title = "ground-truth\n"plot_title += "(" + str(len(ground_truth_files_list)) + " files and " + str(n_classes) + " classes)"x_label = "Number of objects per class"output_path = results_files_path + "/ground-truth-info.png"to_show = Falseplot_color = 'forestgreen'draw_plot_func(gt_counter_per_class,n_classes,window_title,plot_title,x_label,output_path,to_show,plot_color,'',)"""Write number of ground-truth objects per class to results.txt
"""
with open(results_files_path + "/results.txt", 'a') as results_file:results_file.write("\n# Number of ground-truth objects per class\n")for class_name in sorted(gt_counter_per_class):results_file.write(class_name + ": " + str(gt_counter_per_class[class_name]) + "\n")"""Finish counting true positives
"""
for class_name in dr_classes:# if class exists in detection-result but not in ground-truth then there are no true positives in that classif class_name not in gt_classes:count_true_positives[class_name] = 0
#print(count_true_positives)"""Plot the total number of occurences of each class in the "detection-results" folder
"""
if draw_plot:window_title = "detection-results-info"# Plot titleplot_title = "detection-results\n"plot_title += "(" + str(len(dr_files_list)) + " files and "count_non_zero_values_in_dictionary = sum(int(x) > 0 for x in list(det_counter_per_class.values()))plot_title += str(count_non_zero_values_in_dictionary) + " detected classes)"# end Plot titlex_label = "Number of objects per class"output_path = results_files_path + "/detection-results-info.png"to_show = Falseplot_color = 'forestgreen'true_p_bar = count_true_positivesdraw_plot_func(det_counter_per_class,len(det_counter_per_class),window_title,plot_title,x_label,output_path,to_show,plot_color,true_p_bar)"""Write number of detected objects per class to results.txt
"""
with open(results_files_path + "/results.txt", 'a') as results_file:results_file.write("\n# Number of detected objects per class\n")for class_name in sorted(dr_classes):n_det = det_counter_per_class[class_name]text = class_name + ": " + str(n_det)text += " (tp:" + str(count_true_positives[class_name]) + ""text += ", fp:" + str(n_det - count_true_positives[class_name]) + ")\n"results_file.write(text)"""Draw log-average miss rate plot (Show lamr of all classes in decreasing order)
"""
if draw_plot:window_title = "lamr"plot_title = "log-average miss rate"x_label = "log-average miss rate"output_path = results_files_path + "/lamr.png"to_show = Falseplot_color = 'royalblue'draw_plot_func(lamr_dictionary,n_classes,window_title,plot_title,x_label,output_path,to_show,plot_color,"")"""Draw mAP plot (Show AP's of all classes in decreasing order)
"""
if draw_plot:window_title = "mAP"plot_title = "mAP = {0:.2f}%".format(mAP*100)x_label = "Average Precision"output_path = results_files_path + "/mAP.png"to_show = Trueplot_color = 'royalblue'draw_plot_func(ap_dictionary,n_classes,window_title,plot_title,x_label,output_path,to_show,plot_color,"")

参考资料：https://github.com/Cartucho/mAP

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