计算mAP的代码备份
import glob
import json
import os
import shutil
import operator
import sys
import argparse
import math
import numpy as np
MINOVERLAP = 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__)))
yolo_flag=False
if yolo_flag:
GT_PATH = "/data/zhangyong/pytorch/yolov4-pytorch-master/input/ground-truth"
DR_PATH = "/data/zhangyong/pytorch/yolov4-pytorch-master/input/detection-results"
else:
GT_PATH = "/data/zhangyong/pytorch/yolov4-pytorch-master/caffe/gt"
DR_PATH = "/data/zhangyong/pytorch/yolov5-master/map/dt_s"
# 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:
import cv2
show_animation = True
except 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
if not args.no_plot:
try:
import matplotlib.pyplot as plt
draw_plot = True
except ImportError:
print("\"matplotlib\" not found, please install it to get the resulting plots.")
args.no_plot = True
args.no_animation=False
draw_plot = False
show_animation=False
def log_average_miss_rate(precision, fp_cumsum, num_images):
"""
log-average miss rate:
Calculated by averaging miss rates at 9 evenly spaced FPPI points
between 10e-2 and 10e0, in log-space.
output:
lamr | log-average miss rate
mr | miss rate
fppi | false positives per image
references:
[1] Dollar, Piotr, et al. "Pedestrian Detection: An Evaluation of the
State of the Art." Pattern Analysis and Machine Intelligence, IEEE
Transactions on 34.4 (2012): 743 - 761.
"""
# if there were no detections of that class
if precision.size == 0:
lamr = 0
mr = 1
fppi = 0
return lamr, mr, fppi
fppi = 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-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
def error(msg):
print(msg)
sys.exit(0)
def is_float_between_0_and_1(value):
try:
val = float(value)
if val > 0.0 and val < 1.0:
return True
else:
return False
except ValueError:
return False
def voc_ap(rec, prec):
"""
--- Official matlab code VOC2012---
mrec=[0 ; rec ; 1];
mpre=[0 ; prec ; 0];
for i=numel(mpre)-1:-1:1
mpre(i)=max(mpre(i),mpre(i+1));
end
i=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 list
rec.append(1.0) # insert 1.0 at end of list
mrec = rec[:]
prec.insert(0, 0.0) # insert 0.0 at begining of list
prec.append(0.0) # insert 0.0 at end of list
mpre = prec[:]
"""
This part makes the precision monotonically decreasing
(goes from the end to the beginning)
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])
"""
This part creates a list of indexes where the recall changes
matlab: 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 = 0.0
for i in i_list:
ap += ((mrec[i]-mrec[i-1])*mpre[i])
return ap, mrec, mpre
def file_lines_to_list(path):
# open txt file lines to a list
with open(path) as f:
content = f.readlines()
# remove whitespace characters like `\n` at the end of each line
content = [x.strip() for x in content]
return content
def draw_text_in_image(img, text, pos, color, line_width):
font = cv2.FONT_HERSHEY_PLAIN
fontScale = 1
lineType = 1
bottomLeftCornerOfText = pos
cv2.putText(img, text,
bottomLeftCornerOfText,
font,
fontScale,
color,
lineType)
text_width, _ = cv2.getTextSize(text, font, fontScale, lineType)[0]
return img, (line_width + text_width)
def adjust_axes(r, t, fig, axes):
# get text width for re-scaling
bb = t.get_window_extent(renderer=r)
text_width_inches = bb.width / fig.dpi
# get axis width in inches
current_fig_width = fig.get_figwidth()
new_fig_width = current_fig_width + text_width_inches
propotion = new_fig_width / current_fig_width
# get axis limit
x_lim = axes.get_xlim()
axes.set_xlim([x_lim[0], x_lim[1]*propotion])
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 tuples
sorted_dic_by_value = sorted(dictionary.items(), key=operator.itemgetter(1))
# unpacking the list of tuples into two lists
sorted_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 legend
plt.legend(loc='lower right')
"""
Write number on side of bar
"""
fig = plt.gcf() # gcf - get current figure
axes = 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 number
t = 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 bar
adjust_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 figure
axes = plt.gca()
r = fig.canvas.get_renderer()
for i, val in enumerate(sorted_values):
str_val = " " + str(val) # add a space before
if 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 figure
if i == (len(sorted_values)-1): # largest bar
adjust_axes(r, t, fig, axes)
# set window title
fig.canvas.set_window_title(window_title)
# write classes in y axis
tick_font_size = 12
plt.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 inches
dpi = fig.dpi
height_pt = n_classes * (tick_font_size * 1.4) # 1.4 (some spacing)
height_in = height_pt / dpi
# compute the required figure height
top_margin = 0.15 # in percentage of the figure height
bottom_margin = 0.05 # in percentage of the figure height
figure_height = height_in / (1 - top_margin - bottom_margin)
# set new height
if figure_height > init_height:
fig.set_figheight(figure_height)
# set plot title
plt.title(plot_title, fontsize=14)
# set axis titles
# plt.xlabel('classes')
plt.xlabel(x_label, fontsize='large')
# adjust size of window
fig.tight_layout()
# save the plot
fig.savefig(output_path)
# show image
if to_show:
plt.show()
# close the plot
plt.close()
TEMP_FILES_PATH = ".temp_files"
if not os.path.exists(TEMP_FILES_PATH): # if it doesn't exist already
os.makedirs(TEMP_FILES_PATH)
results_files_path = "results"
if os.path.exists(results_files_path): # if it exist already
# reset the results directory
shutil.rmtree(results_files_path)
os.makedirs(results_files_path)
if draw_plot:
os.makedirs(os.path.join(results_files_path, "AP"))
os.makedirs(os.path.join(results_files_path, "F1"))
os.makedirs(os.path.join(results_files_path, "Recall"))
os.makedirs(os.path.join(results_files_path, "Precision"))
if show_animation:
os.makedirs(os.path.join(results_files_path, "images", "detections_one_by_one"))
"""
ground-truth
Load 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 file
temp_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 dictionary
bounding_boxes = []
is_difficult = False
already_seen_classes = []
for line in lines_list:
try:
if "difficult" in line:
class_name, left, top, right, bottom, _difficult = line.split()
is_difficult = True
else:
class_name, left, top, right, bottom = line.split()
except ValueError:
error_msg = "Error: File " + txt_file + " in the wrong format.\n"
error_msg += " Expected: <class_name> <left> <top> <right> <bottom> ['difficult']\n"
error_msg += " Received: " + line
error_msg += "\n\nIf you have a <class_name> 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 skip
if class_name in args.ignore:
continue
bbox = left + " " + top + " " + right + " " +bottom
if is_difficult:
bounding_boxes.append({"class_name":class_name, "bbox":bbox, "used":False, "difficult":True})
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)
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] # odd
if 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-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"))
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: <class_name> <confidence> <left> <top> <right> <bottom>\n"
error_msg += " Received: " + line
error(error_msg)
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)
"""
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 nd
fp = [0] * nd
score = [0] * nd
score05_idx = 0
for idx, detection in enumerate(dr_data):
file_id = detection["file_id"]
score[idx] = float(detection["confidence"])
if score[idx] > 0.5:
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
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])]
iw = bi[2] - bi[0] + 1
ih = bi[3] - bi[1] + 1
if iw > 0 and ih > 0:
# compute overlap (IoU) = area of intersection / area of union
ua = (bb[2] - bb[0] + 1) * (bb[3] - bb[1] + 1) + (bbgt[2] - bbgt[0]
+ 1) * (bbgt[3] - bbgt[1] + 1) - iw * ih
ov = iw * ih / ua
if ov > ovmax:
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
min_overlap = MINOVERLAP
if 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 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!"
else:
# false positive
fp[idx] = 1
if 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 line
margin = 10
v_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_red
if 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 = green
img, _ = draw_text_in_image(img, text, (margin + line_width, v_pos), color, line_width)
# 2nd line
v_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_red
if status == "MATCH!":
color = green
text = "Result: " + status + " "
img, line_width = draw_text_in_image(img, text, (margin + line_width, v_pos), color, line_width)
font = cv2.FONT_HERSHEY_SIMPLEX
if ovmax > 0: # if there is intersections between the bounding-boxes
bbgt = [ 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 image
# cv2.imshow("Animation", img)
# cv2.waitKey(20) # show for 20 ms
# save image to results
output_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 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[:]
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[:])
F1 = np.array(rec)*np.array(prec)/(np.array(prec)+np.array(rec))*2
sum_AP += ap
text = "{0:.2f}%".format(ap*100) + " = " + class_name + " AP " #class_name + " AP = {0:.2f}%".format(ap*100)
if len(prec)>0:
F1_text = "{0:.2f}".format(F1[score05_idx]) + " = " + class_name + " F1 "
Recall_text = "{0:.2f}%".format(rec[score05_idx]*100) + " = " + class_name + " Recall "
Precision_text = "{0:.2f}%".format(prec[score05_idx]*100) + " = " + class_name + " Precision "
else:
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:
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))
ap_dictionary[class_name] = ap
n_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 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.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.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
fig.savefig(results_files_path + "/AP/" + class_name + ".png")
plt.cla() # clear axes for next plot
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.set_xlim([0.0,1.0])
axes.set_ylim([0.0,1.05]) # .05 to give some extra space
fig.savefig(results_files_path + "/F1/" + class_name + ".png")
plt.cla() # clear axes for next plot
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.set_xlim([0.0,1.0])
axes.set_ylim([0.0,1.05]) # .05 to give some extra space
fig.savefig(results_files_path + "/Recall/" + class_name + ".png")
plt.cla() # clear axes for next plot
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.set_xlim([0.0,1.0])
axes.set_ylim([0.0,1.05]) # .05 to give some extra space
fig.savefig(results_files_path + "/Precision/" + class_name + ".png")
plt.cla() # clear axes for next plot
if show_animation:
cv2.destroyAllWindows()
results_file.write("\n# mAP of all classes\n")
mAP = sum_AP / n_classes
text = "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 list
lines_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 skip
if class_name in args.ignore:
continue
# count that object
if class_name in det_counter_per_class:
det_counter_per_class[class_name] += 1
else:
# if class didn't exist yet
det_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 = False
plot_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 class
if 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 title
plot_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 title
x_label = "Number of objects per class"
output_path = results_files_path + "/detection-results-info.png"
to_show = False
plot_color = 'forestgreen'
true_p_bar = count_true_positives
draw_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 = False
plot_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 = True
plot_color = 'royalblue'
draw_plot_func(
ap_dictionary,
n_classes,
window_title,
plot_title,
x_label,
output_path,
to_show,
plot_color,
""
)