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Evaluate_NIRPed.py
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Evaluate_NIRPed.py
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import os, json, glob, shutil, argparse, pdb, sys, cv2, copy, datetime, pickle, operator, math
import matplotlib.pyplot as plt
from PIL import Image, ImageDraw, ImageFont
from keras_frcnn.coco import COCO
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
import numpy as np
np.set_printoptions(precision=6, threshold=np.inf, edgeitems=10, linewidth=260, suppress=True)
from keras_frcnn import config
cfg = config.Config()
MINOVERLAP = 0.50 # TODO:default value (defined in the PASCAL VOC2012 challenge)最小交并比MINOVERLAP = 0.5
Hf = 3985 #TODO:4125
cfg.evaluate_subset = 'Reasonable'
# cfg.evaluate_subset = 'All'
# evalute_new = 0 #根据得分阈值score_threshold，再次计算MR-FPPI曲线,评估测距结果
evalute_new = 1 #显示MR-2时的检测结果
idx_Dis_max = 8
idx_step = 10
max_boxes = 300
IoU_threshold_rpn = 0.70
score_threshold_rpn = 0.50
IoU_threshold_cls = 0.50
score_threshold_cls = 0.001
subset = 'val'
# subset = 'test'
Detection_results_dir = "./results_miniNIRPed/dt_results_%s_B%d_%s" % (subset, max_boxes, str(score_threshold_cls)[2:])
class NpEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, np.integer):
return int(obj)
elif isinstance(obj, np.floating):
return float(obj)
elif isinstance(obj, np.ndarray):
return obj.tolist()
else:
return super(NpEncoder, self).default(obj)
def error(msg): # throw error and exit 抛出错误并退出
print(msg)
sys.exit(0) # 干净利落地退出系统
""" ground-truth 真实标记框 Load each of the ground-truth files into a temporary '.json' file. 将每个真实标记框文件加载到一个临时的“.json”文件中。
Create a list of all the class names present in the ground-truth (gt_classes).创建一个包含真实标记框（gt_类）中所有类名的列表。"""
def Temp_gt_Generation(TEMP_FILES_Path):
cocoGt = COCO(cfg.annos_file)
imgIds = sorted(cocoGt.getImgIds()) # imgIds=[100013, 100024, 100063, 100065, 100074, 100084, 100143, 100154, 100159, 100164,...]
gt_counter_per_class = {}
ele = [0]*12
for class_name in ['Ped', 'Peo', 'Bic', 'Mot','Ign','bg']:
gt_counter_per_class[class_name] = {'All': copy.deepcopy(ele), 'short': copy.deepcopy(ele), 'normal_height': copy.deepcopy(ele), 'tall': copy.deepcopy(ele),
'Sun': copy.deepcopy(ele), 'Rain': copy.deepcopy(ele), 'Spate': copy.deepcopy(ele)}
num_test_imgs = {'All': 0, 'Sun': 0, 'Rain': 0, 'Spate': 0}
for i in range(len(imgIds)):
anno_ids = cocoGt.getAnnIds(imgIds=imgIds[i]) # anno_id=1037542#anno_ids= [7000000, 7000001]
annos = cocoGt.loadAnns(ids=anno_ids)
# annos=[{'occluded': False, 'Dif': False, 'bbox': [349, 227, 20, 41], 'id': 7000000, 'category_id': 1, 'image_id': 7000000, 'pose_id': 1, 'tracking_id': 7000000, 'ignore': 0, 'area': 820, 'truncated': False},
# {'occluded': False, 'Dif': False, 'bbox': [645, 239, 21, 40], 'id': 7000001, 'category_id': 1, 'image_id': 7000000, 'pose_id': 1, 'tracking_id': 7000001, 'ignore': 0, 'area': 840, 'truncated': False}]
# Show the annotation in its image
image = cocoGt.loadImgs(ids=imgIds[i])[0]
# image={'height': 640, 'width': 1024, 'daytime': 'night', 'file_name': '58c58285bc26013700140940.png', 'id': 1096678,'recordings_id': 15.0, 'timestamp': 1598649939}
img_name = image['file_name']
img_id = img_name.split('.', 1)[0]
if img_id[:12] in ['Data20181219', 'Data20181220', 'Data20190113']:
image['weathers_id'] = 0
num_test_imgs['All'] += 1
if image['weathers_id'] == 0:
Wea = 'Sun'
elif image['weathers_id'] == 2:
Wea = 'Spate'
else:
Wea = 'Rain'
num_test_imgs[Wea] += 1
# img_path = os.path.join('E:\\Datasets\\NIRPed2021\\NIRPed\\images\\{}\\{}'.format(subset, img_name))
# img_path = os.path.join('.\\data\\NIRPed\\images\\{}\\{}'.format(subset, img_name))
img_path = os.path.join('.\\data\\miniNIRPed\\images\\{}\\{}'.format(subset, img_name))
if img_path == None:
print('Notion:{} is not exist.'.format(img_path))
continue
groundTruth_boxes = []
try:
for anno in annos:
cat = cocoGt.loadCats(ids=anno['category_id'])[0] # cat={'name': 'pedestrian', 'id': 1}
class_name = cat['name']
if class_name in ['Pedestrian', 'pedestrian', 'Ped', 'ped']:
class_name = 'Ped'
bbox = anno['bbox']
height_gt = bbox[3]
distance = float(anno['Dis'])
if distance <= 0 or distance == 100:
distance = round(Hf /height_gt, 1) # TODO: 将像素高度直接估算为距离。
if distance > 110:
distance = 110
# distance = cfg.Dis_threshold
box = [bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3]]
# if flag_mr_height == 1:
dis_gt = distance
dis_gt_index = int(dis_gt//10)
dis_from_height_gt = Hf / height_gt
error_rate_dis_from_height_gt = 100 * (dis_from_height_gt - dis_gt) / dis_gt
if error_rate_dis_from_height_gt >= 10:
height = 'tall'
elif error_rate_dis_from_height_gt < -20:
height = 'short'
else:
height = 'normal_height'
if class_name in ['Ped', 'Peo', 'Bic', 'Mot']:
vis_box = anno['vis_box']
aera_box = float(bbox[2]) * float(bbox[3])
if 100 < aera_box < 1280 * 720 * 0.9:
vis_aera = float(vis_box[2]) * float(vis_box[3])
Occlusion_coefficient = 1 - vis_aera / aera_box
if 0 <= Occlusion_coefficient < 1:
anno['Occ_Coe'] = round(Occlusion_coefficient, 2)
else:
# print('Wrong visable box yeild to Occlusion_coefficient = {}, and change it to Occlusion_coefficient=0'.format(Occlusion_coefficient))
anno['Occ_Coe'] = 0
if cfg.evaluate_subset == 'Reasonable':
if anno['Occ_Coe'] < cfg.Occ_threshold and not anno['Dif']:
gt_counter_per_class[class_name]['All'][dis_gt_index] += 1
gt_counter_per_class[class_name][height][dis_gt_index] += 1
gt_counter_per_class[class_name][Wea][dis_gt_index] += 1
elif cfg.evaluate_subset == 'All':
gt_counter_per_class[class_name]['All'][dis_gt_index] += 1
gt_counter_per_class[class_name][height][dis_gt_index] += 1
gt_counter_per_class[class_name][Wea][dis_gt_index] += 1
groundTruth_boxes.append({'class_name': class_name, 'bbox': box, 'Dis': dis_gt, 'used': False,
'Dif': anno['Dif'], 'Occ_Coe': anno['Occ_Coe'], 'Age': anno['Age'],
'area': anno['area'], 'Height': height, 'Wea': Wea})
else:
gt_counter_per_class[class_name]['All'][dis_gt_index] += 1
groundTruth_boxes.append({'class_name': class_name, 'bbox': box, 'Dis': dis_gt, 'used': False,
'Dif': anno['Dif'], 'Occ_Coe': anno['Occ_Coe'], 'Age': anno['Age'],
'area': anno['area'], 'Height': height, 'Wea': Wea})
gt_counter_per_class[class_name][height][dis_gt_index] += 1
gt_counter_per_class[class_name][Wea][dis_gt_index] += 1
if len(groundTruth_boxes) == 0:
groundTruth_boxes.append({'class_name': 'Bg_Img', 'Height': 'normal_height', 'Wea': Wea})
except Exception as e:
s = sys.exc_info()
print("Exception: Error '%s' happened on line %d with image:'%s'" % (s[1], s[2].tb_lineno, img_id))
pdb.set_trace()
gt_TEMP_FILES_Path = os.path.join(TEMP_FILES_Path, img_id + '_gt.json')
if not os.path.exists(gt_TEMP_FILES_Path):
outfile = open(gt_TEMP_FILES_Path, 'w')
json.dump(groundTruth_boxes, outfile)
outfile.close()
# get a list with the ground-truth files 获取包含真实标记框文件的列表
gt_files_list = glob.glob(TEMP_FILES_Path + '/*.json')
# gt_files_list = ['E:\\Daixb\\Evaluation_mAP_MissRate-FPPI\\MissRate-FPPI_results\\NIRPed_WeightsResnet50NIR1RGB64_1024_128_2o5L0.18\\.temp_files\\Data20181219200348_020000_gt.json', ……]
gt_files_list.sort()
num_test_imgs['short'] = num_test_imgs['All']
num_test_imgs['normal_height'] = num_test_imgs['All']
num_test_imgs['tall'] = num_test_imgs['All']
for attr in num_test_imgs:
print('NIRPed：num_test_imgs_%s=%d' % (attr, num_test_imgs[attr]))
for class_name in ['Ped']:
for attr in gt_counter_per_class[class_name]:
print('class_name:%s,attribute:%s'% (class_name, attr))
print(gt_counter_per_class[class_name][attr])
return gt_files_list, gt_counter_per_class, num_test_imgs
# TODO: *** 有序显示标记。*** 有序显示标记。*** 有序显示标记。*** 有序显示标记。*** 有序显示标记。*** 有序显示标记。*** 有序显示标记。
colors = {'Slice': ['white', 'white'], 'GT': ['red', 'black'], 'DT': ['white', 'white'], 'TP': ['lime', 'lime'], 'FP': ['magenta', 'magenta'], 'miss_Ped': ['red', 'red'], 'AER_bad': ['red', 'red'],
'Peo': ['orchid', 'black'], 'Ped': ['orangered', 'black'], 'Bic': ['hotpink', 'black'], 'Mot': ['fuchsia', 'black'], 'Ign': ['limegreen', 'black'], 'bg': ['seagreen', 'black']}
# color_white = 'white'
# color_white = 'lightgrey'
color_white = 'dimgray'
color_Obj = colors['Ped']
color_Ped = colors['Ped']
color_bg = colors['bg']
# color_GT = colors['GT']
# color_TP = colors['TP']
# color_FP = colors['FP']
# color_AER_bad = colors['AER_bad']
# color_FN = colors['miss_Ped']
color_GT = colors['Slice']
color_TP = colors['Slice']
color_FP = colors['Slice']
color_AER_bad = colors['Slice']
color_FN = colors['Slice']
size_font_slice = 28
width_line_slice = 1
font_slice = ImageFont.truetype('arial.ttf', size=30)
# font_image = ImageFont.truetype('arial.ttf', size=32)
font_image = ImageFont.truetype('arial.ttf', size=50)
# width_line_image = 1
width_line_image = 2
size_font_highlight = 2
edge_kept = 2
edge_top = 0
def draw_tags_orderly(draw,label,Doted_text,color_cls,left,right,top,bottom,h_box,edge_kept=5):
label_size = draw.textsize(label, font_image)
if bottom < cfg.im_rows_show - 10 - edge_kept - label_size[1]:
bottom_text_boundary = bottom + 20
else:
bottom_text_boundary = bottom - 20
if h_box < cfg.im_rows_show / 4 and top - edge_kept - label_size[1] > 0:
text_origin = np.array([int(min(max(0.5 * (left + right) - 0.5 * label_size[0], 0), cfg.im_cols_show - label_size[0])), int(edge_kept)])
else:
text_origin = np.array([int(min(max(0.5 * (left + right) - 0.5 * label_size[0], 0), cfg.im_cols_show - label_size[0])), int(cfg.im_rows_show - edge_kept - label_size[1])])
x = text_origin[0]
y = text_origin[1]
y_modified = y
dy = label_size[1] // 5
y_range = int((cfg.im_rows_show - 2 * edge_kept) / dy)
for tp_all in range(0, y_range):
flagT = 1
if tp_all > 0:
if y_modified < top - label_size[1] - 10:
y_modified = y_modified + dy
elif y_modified >= bottom_text_boundary:
y_modified = y_modified - dy
if top - label_size[1] - 10 <= y_modified < bottom_text_boundary:
if np.random.randint(0, 2) == 0:
y_modified = edge_kept
else:
y_modified = cfg.im_rows_show - edge_kept - label_size[1]
if Doted_text != []:
for Dot_xy in Doted_text:
dis_x = np.abs(Dot_xy[0] - x)
dis_y = np.abs(Dot_xy[1] - y_modified)
if x < Dot_xy[0] and y_modified < Dot_xy[1]:
if dis_x < label_size[0] + 2 and dis_y < label_size[1] + 2:
flagT = 0
break
elif x < Dot_xy[0] and y_modified >= Dot_xy[1]:
if dis_x < label_size[0] + 2 and dis_y < Dot_xy[3] + 2:
flagT = 0
break
elif x >= Dot_xy[0] and y_modified < Dot_xy[1]:
if dis_x < Dot_xy[2] + 2 and dis_y < label_size[1] + 2:
flagT = 0
break
elif x >= Dot_xy[0] and y_modified >= Dot_xy[1]:
if dis_x < Dot_xy[2] + 2 and dis_y < Dot_xy[3] + 2:
flagT = 0
break
if flagT == 1:
try:
text_origin = np.array([x, y_modified])
text_origin_rectangle = np.array([x-1, y_modified])
draw.rectangle([tuple(text_origin_rectangle), tuple(text_origin_rectangle + label_size)], outline=color_cls[0], fill=color_white, width=width_line_image) #TODO:fill=(255, 255, 255) 将改成：fill=color_white
draw.text(text_origin, label, fill=color_cls[1], font=font_image)
Doted_text.append([text_origin[0], text_origin[1], label_size[0], label_size[1]])
except:
print('error in draw.rectangle()')
pdb.set_trace()
break
if flagT == 0:
text_origin = np.array([x, bottom_text_boundary])
text_origin_rectangle = np.array([x-1, bottom_text_boundary])
draw.rectangle([tuple(text_origin_rectangle), tuple(text_origin_rectangle + label_size)], outline=color_cls[0], fill=(255, 255, 255), width=width_line_image)
draw.text(text_origin, label, fill=color_cls[1], font=font_image)
Doted_text.append([text_origin[0], text_origin[1], label_size[0], label_size[1]])
if top > text_origin[1] + label_size[1]:
draw.line((int(0.5 * (left + right)), top, int(0.5 * (left + right)), text_origin[1] + label_size[1]), fill=color_cls[0], width=width_line_image)
if bottom < text_origin[1]:
draw.line((int(0.5 * (left + right)), bottom, int(0.5 * (left + right)), text_origin[1]), fill=color_cls[0], width=width_line_image)
# pdb.set_trace()
# draw.rectangle([tuple(text_origin), tuple(text_origin + label_size)], fill=(255, 255, 255, 20))
# draw.text(text_origin, label, fill=color_Ped[1], font=font_image)
# draw.rectangle([left, top, right, bottom], outline=color_cls[0], width=width_line_image) # 淡红色单框：缩放到resized图上标记框
return draw, Doted_text
#TODO:*** 图像显示MissRate_FPPI.*** 图像显示MissRate_FPPI.*** 图像显示MissRate_FPPI.*** 图像显示MissRate_FPPI.*** 图像显示MissRate_FPPI.
def MR_FPPI_Plot(MR_FPPI_dis_dic, detectResults_Path, max_or_seg):# TODO: ***图像显示MissRate_FPPI曲线。
font_size = 24
font = {'family': 'Times New Roman', 'weight': 'normal', 'size': font_size}
colors = ['green', 'lime', 'blue', 'purple', 'hotpink', 'orangered', 'fuchsia', 'red', 'black']
FPPI_0_2 = np.logspace(-2, 0, 9)
FPPI_0_4 = np.logspace(-4, 0, 9)
for attr in MR_FPPI_dis_dic:
fig, ax = plt.subplots(1, 1, figsize=(8, 6)) # fig = plt.figure(facecolor='w')
for dis_index in range(0, idx_Dis_max):
dis_idx = idx_Dis_max-1 - dis_index
MR_FPPI_array = np.array(MR_FPPI_dis_dic[attr][dis_idx])
MissRate_list = []
for i, x in enumerate(FPPI_0_4):
idx = np.argmin(np.abs(MR_FPPI_array[:, 0]-x))
MissRate_list.append(MR_FPPI_array[idx, 1])
# print('MissRate_list_4 of MR_FPPI[%s][%s]='% (attr, dis_index))
# print(MissRate_list)
LAMR_4 = pow(np.prod(MissRate_list), 1 / 9)
MissRate_list = []
for i, x in enumerate(FPPI_0_2):
idx = np.argmin(np.abs(MR_FPPI_array[:, 0]-x))
MissRate_list.append(MR_FPPI_array[idx, 1])
# print('MissRate_list_2 of MR_FPPI[%s][%s]=' % (attr, dis_index))
# print(MissRate_list)
LAMR_2 = pow(np.prod(MissRate_list), 1 / 9)
if max_or_seg == 'max':
dis_txt = 'd<%dm' % (dis_idx * 10 + 10)
elif max_or_seg == 'seg':
dis_txt = '%d≤d<%dm' % (dis_idx * 10, dis_idx * 10 + 10)
# pdb.set_trace()
if dis_idx * 10 + 10 == cfg.Dis_threshold:
if max_or_seg == 'max' and cfg.evaluate_subset == 'Reasonable':
plt.loglog(MR_FPPI_array[:, 0], MR_FPPI_array[:, 1], label='%.2f%%(%.2f%%) %s\n[reasonable]' % (LAMR_2*100, LAMR_4*100, dis_txt), color=colors[dis_idx], linestyle='-', linewidth=2)
else:
plt.loglog(MR_FPPI_array[:, 0], MR_FPPI_array[:, 1], label='%.2f%%(%.2f%%) %s' % (LAMR_2 * 100, LAMR_4 * 100, dis_txt), color=colors[dis_idx], linestyle='-', linewidth=2)
else:
plt.loglog(MR_FPPI_array[:, 0], MR_FPPI_array[:, 1], label='%.2f%%(%.2f%%) %s' % (LAMR_2*100, LAMR_4*100, dis_txt), color=colors[dis_idx], linestyle='-', linewidth=1)
plt.grid(color='b', linestyle='--', linewidth=0.5, alpha=0.3)
plt.xlabel('false positives per image', font)
plt.ylabel('miss rate ({})'.format(attr), font)
# plt.xlim(0.00004, 0.2)
plt.xlim(0.00003, 10) # T35
plt.ylim(0.001, 1)
plt.yticks([0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.14, 0.2, 0.3, 0.4, 0.5, 0.64, 0.8, 1],
['.001', '.002', '.005', '.01', '.02', '.05', '.10', '.14', '.20', '.30', '.40', '.50', '.64', '.80', '1'])
plt.tick_params(labelsize=font_size-10) # 刻度字体大小13
ax.spines['top'].set_visible(False) # 顶边界不可见
ax.spines['right'].set_visible(False) # 右边界不可见
legend = ax.legend(loc="lower left", fontsize=font_size-9)
if max_or_seg == 'seg':
legend = ax.legend(loc="upper right", fontsize=font_size-9)
frame = legend.get_frame()
frame.set_alpha(1)
frame.set_edgecolor('none') # 设置图例legend背景透明
frame.set_facecolor('none') # 设置图例legend背景透明
plt.grid(color='b', linestyle='--', linewidth=0.5, alpha=0.3)
plt.grid(True)
fig.subplots_adjust(left=0.12, right=0.97, top=0.98, bottom=0.12)
# plt.show()
fig.savefig(os.path.join(os.path.dirname(detectResults_Path), 'Miss_RateVsFPPI_loglog_dis_{}_{}_{}.png'.format(max_or_seg, cfg.evaluate_subset, attr)), bbox_inches='tight')
return None
#TODO:*** 图像显示MissRate_FPPI.*** 图像显示MissRate_FPPI.*** 图像显示MissRate_FPPI.*** 图像显示MissRate_FPPI.*** 图像显示MissRate_FPPI.
#TODO:*** 图像显示MissRate_FPPI.*** 图像显示MissRate_FPPI.*** 图像显示MissRate_FPPI.*** 图像显示MissRate_FPPI.*** 图像显示MissRate_FPPI.
def PR_Plot(PR_dis_dic, detectResults_Path, max_or_seg):# TODO: ***图像显示PR曲线。
font_size = 24
font = {'family': 'Times New Roman', 'weight': 'normal', 'size': font_size}
colors = ['green', 'lime', 'blue', 'purple', 'hotpink', 'orangered', 'fuchsia', 'red', 'black']
r_list = np.linspace(0, 1, 11)
# pdb.set_trace()
for attr in PR_dis_dic:
fig, ax = plt.subplots(1, 1, figsize=(8, 6)) # fig = plt.figure(facecolor='w')
for dis_index in range(0, idx_Dis_max):
dis_idx = idx_Dis_max-1 - dis_index
PR_array = np.array(PR_dis_dic[attr][dis_idx])
if PR_array.size == 0:
if max_or_seg == 'seg':
print('Did not detect any pedestrian in distance segment [%d,%d)]' % (dis_idx*10, (dis_idx+1)*10))
elif max_or_seg == 'max':
print('Did not detect any pedestrian in distance range [3,%d)]' % ((dis_idx + 1) * 10))
continue
pr_list = []
for ri in r_list:
index_ri = np.argmin(np.abs(PR_array[:, 1] - ri))
pr_list.append(np.max(PR_array[index_ri:, 0]))
AP = np.mean(pr_list)
if max_or_seg == 'max':
dis_txt = 'd<%dm' % (dis_idx * 10 + 10)
elif max_or_seg == 'seg':
dis_txt = '%d≤d<%dm' % (dis_idx * 10, dis_idx * 10 + 10)
if dis_idx * 10 + 10 == cfg.Dis_threshold:
if max_or_seg == 'max' and cfg.evaluate_subset == 'Reasonable':
plt.plot(PR_array[:, 0], PR_array[:, 1], label='AP=%.2f%% %s[reasonable]' % (100*AP, dis_txt), color=colors[dis_idx], linestyle='-', linewidth=2)
else:
plt.plot(PR_array[:, 0], PR_array[:, 1], label='AP=%.2f%% %s' % (100*AP, dis_txt), color=colors[dis_idx], linestyle='-', linewidth=2)
else:
plt.plot(PR_array[:, 0], PR_array[:, 1], label='AP=%.2f%% %s' % (100*AP, dis_txt), color=colors[dis_idx], linestyle='-', linewidth=1)
plt.grid(color='b', linestyle='--', linewidth=0.5, alpha=0.3)
plt.xlabel('recall', font)
plt.ylabel('precision (%s)' % (attr), font)
plt.xlim(0.4, 1) # T35
plt.ylim(0, 1)
plt.tick_params(labelsize=font_size-10) # 刻度字体大小13
ax.spines['top'].set_visible(False) # 顶边界不可见
ax.spines['right'].set_visible(False) # 右边界不可见
legend = ax.legend(loc="lower left", fontsize=font_size-9)
if max_or_seg == 'seg':
legend = ax.legend(loc="upper right", fontsize=font_size-9)
frame = legend.get_frame()
frame.set_alpha(1)
frame.set_edgecolor('none') # 设置图例legend背景透明
frame.set_facecolor('none') # 设置图例legend背景透明
plt.grid(color='b', linestyle='--', linewidth=0.5, alpha=0.3)
plt.grid(True)
fig.subplots_adjust(left=0.12, right=0.97, top=0.98, bottom=0.12)
plt.show()
fig.savefig(os.path.join(os.path.dirname(detectResults_Path), 'PR_dis_{}_{}_{}.png'.format(max_or_seg, cfg.evaluate_subset, attr)), bbox_inches='tight')
return None
#TODO:******后处理测距误差******后处理测距误差******后处理测距误差******后处理测距误差******后处理测距误差******后处理测距误差******
def post_processing(DE_conf, abs_flag = False):
DE_max_dic = copy.deepcopy(DE_conf)
DE_seg_dic = copy.deepcopy(DE_conf)
for attr in DE_conf:
Results_all_dis = []
for key_dis, v_dis in DE_conf[attr].items():
Results_all_dis += v_dis
num_dis = len(v_dis)
if abs_flag:
v_dis = np.abs(v_dis)
if num_dis > 0:
mean = round(np.mean(v_dis), 2)
sigma = round(np.std(v_dis), 2)
else:
mean = 0
sigma = 0
DE_seg_dic[attr][key_dis] = [mean, sigma, num_dis]
num_dis_all = len(Results_all_dis)
if abs_flag:
Results_all_dis = np.abs(Results_all_dis)
if num_dis_all > 0:
mean_all = round(np.mean(Results_all_dis), 2)
sigma_all = round(np.std(Results_all_dis), 2)
else:
mean_all = 0
sigma_all = 0
DE_max_dic[attr][key_dis] = [mean_all, sigma_all, num_dis_all]
return DE_seg_dic, DE_max_dic
#TODO: ******计算MissRate_FPPI和PR曲线的函数******计算MissRate_FPPI和PR曲线的函数******计算MissRate_FPPI和PR曲线的函数******
def MR_FPPI_PR_Calculate(detectResults_Path):
""" Create a '.temp_files/' and 'results/' directory 创建“.temp_files/”和“results/”目录"""
TEMP_FILES_DIR = os.path.dirname(detectResults_Path)
TEMP_FILES_Path = os.path.join(TEMP_FILES_DIR, '.temp_files')
if not os.path.exists(TEMP_FILES_Path): # if it doesn't exist already如果'.temp_files'文件夹不存在与当前目录
os.makedirs(TEMP_FILES_Path) #在程序当前目录下创建一个新的文件夹TEMP_FILES_Path = '.temp_files'
gt_files_list, gt_counter_per_class, num_test_imgs = Temp_gt_Generation(TEMP_FILES_Path)
gt_classes = list(gt_counter_per_class.keys()) # 获取标记框分类计数的键值——类别，并转化成list=['Ped', 'Peo', 'Bic', 'Mot', 'Ign', 'bg']
for class_index, class_name in enumerate(gt_classes): #gt_classes = ['pedestrian', 'motorbikedriver', 'ignore', 'bicycledriver'] ['bg', 'Peo', 'Ped', 'Mot', 'Ign', 'Bic']
"""Load detection-result of that class 加载该类的检测结果"""
if class_name not in ['Ped', 'ped', 'Pedestrian', 'pedestrian']:
continue
dt_data = json.load(open(detectResults_Path)) #加载json文件中的数据
num_dt_boxes = len(dt_data) # 网络检测到的当前类的总数量（包括正正例tp和假正例FP）：num_dt_boxes = 1639
ele = np.zeros((num_dt_boxes, 12))
tp_dic = {'All':copy.deepcopy(ele), 'Sun':copy.deepcopy(ele), 'Rain':copy.deepcopy(ele), 'Spate':copy.deepcopy(ele),
'short':copy.deepcopy(ele), 'normal_height':copy.deepcopy(ele), 'tall':copy.deepcopy(ele)}
fp_dic = copy.deepcopy(tp_dic)
MR_FPPI_dis_max_dic = {'All':{}, 'Sun':{}, 'Rain':{}, 'Spate':{}, 'short':{}, 'normal_height':{}, 'tall':{}}
MR_FPPI_dis_seg_dic = copy.deepcopy(MR_FPPI_dis_max_dic)
PR_dis_max_dic = copy.deepcopy(MR_FPPI_dis_max_dic)
PR_dis_seg_dic = copy.deepcopy(MR_FPPI_dis_max_dic)
AE_dtp_max_dic = copy.deepcopy(MR_FPPI_dis_max_dic)
AE_dtp_seg_dic = copy.deepcopy(MR_FPPI_dis_max_dic)
AE_DE_max_dic = copy.deepcopy(MR_FPPI_dis_max_dic)
AE_DE_seg_dic = copy.deepcopy(MR_FPPI_dis_max_dic)
AER_dtp_max_dic = copy.deepcopy(MR_FPPI_dis_max_dic)
AER_dtp_seg_dic = copy.deepcopy(MR_FPPI_dis_max_dic)
AER_DE_max_dic = copy.deepcopy(MR_FPPI_dis_max_dic)
AER_DE_seg_dic = copy.deepcopy(MR_FPPI_dis_max_dic)
annos_AER_bad = {}
annos_FN = {}
annos_FP = {}
for attr in MR_FPPI_dis_max_dic:
for dis_index in range(0, idx_Dis_max):
MR_FPPI_dis_max_dic[attr][dis_index] = []
MR_FPPI_dis_seg_dic[attr][dis_index] = []
PR_dis_max_dic[attr][dis_index] = []
PR_dis_seg_dic[attr][dis_index] = []
AE_dtp_max_dic[attr][dis_index] = []
AE_dtp_seg_dic[attr][dis_index] = []
AE_DE_max_dic[attr][dis_index] = []
AE_DE_seg_dic[attr][dis_index] = []
AER_dtp_max_dic[attr][dis_index] = []
AER_dtp_seg_dic[attr][dis_index] = []
AER_DE_max_dic[attr][dis_index] = []
AER_DE_seg_dic[attr][dis_index] = []
"""#*************************************************************************对当前类所有检测框与真实标记框进行匹配，并做好TP/FP记录。"""
min_overlap = MINOVERLAP # 最小交并比设置为：MINOVERLAP = 0.5
gt_file_used = []
ignored_dt_count = [0]*12 #TODO: 记录被忽略检测框的数量。
# TODO: 按检测框得分高低开始评估MR-FPPI和AP。
# idx = 0, box_detection = {'class_name': 'Ped', 'confidence': '1.0', 'file_id': 'Data20200624201506_971695N850F12', 'bbox': [1040.0, 202.0, 1168.0, 517.0], 'Dis': 14.78}
for idx, box_detection in enumerate(dt_data):
img_id = box_detection['file_id']
# assign box_detection-result to ground truth object if any
# open ground-truth with that img_id
gt_file = os.path.join(TEMP_FILES_Path, img_id + '_gt.json') # gt_file = '.temp_files/Data20200624201506_971695N850F12_gt.json'
try:
gt_data_temp = json.load(open(gt_file))
except:
print("\033[5;31;47m\tImage:{}, is not in ground-truth. Please check that ground-truth and detect results are matched.\033[0m".format(img_id))
continue
ovmax = -1 # 最大交并比初值设为-1
a_percentage_max = -1 # 最大交并比初值设为-1
gt_matched = -1 # 当前第idx预测框没有匹配上GT
# load detected object bounding-box
bbdtF = [float(x) for x in box_detection['bbox']] # 检测框信息：bbdtF = [1040.0, 202.0, 1168.0, 517.0]
cx_dt = 0.5*(bbdtF[0]+bbdtF[2])
cy_dt = 0.5*(bbdtF[1]+bbdtF[3])
width_dt = bbdtF[2] - bbdtF[0]
height_dt = bbdtF[3] - bbdtF[1]
dis_dt = min(110, box_detection['Dis'])
dis_dt_index = int(dis_dt//10)
in_boxes = False
""" Assign detection-result to ground-truth objects 将检测结果与标记对象进行匹配。"""
for obj in gt_data_temp:
'''gt_data_temp=[{"class_name": "Ped", "bbox": [39, 191, 153, 525], "Dis": 13.4, "used": false, "Dif": false, "Occ_Coe": 0.0, "Age": "Adult", "area": 38076},
{"class_name": "Ped", "bbox": [434, 208, 569, 511], "Dis": 13.0, "used": false, "Dif": false, "Occ_Coe": 0.0, "Age": "Adult", "area": 40905},
{"class_name": "Ped", "bbox": [196, 217, 346, 517], "Dis": 13.2, "used": false, "Dif": false, "Occ_Coe": 0.0, "Age": "Adult", "area": 45000}]'''
# look for a class_name match obj = {'class_name': 'Ped', 'bbox': '466 313 518 433 38', 'used': False, 'Dif': True}
if obj['class_name'] != 'Bg_Img':
bbgtF = [float(x) for x in obj['bbox']] # 真实标记框信息：bbgtF = [466.0, 313.0, 518.0, 433.0, 38.0]
if (bbgtF[0]-2 <= cx_dt <= bbgtF[2]+2) and (bbgtF[1]-2 <= cy_dt <= bbgtF[3]+2):
in_boxes = True
bi = [max(bbdtF[0], bbgtF[0]), max(bbdtF[1], bbgtF[1]), min(bbdtF[2], bbgtF[2]), min(bbdtF[3], bbgtF[3])] # 用于计算检测框和真实标记框的交集面积
iw = bi[2] - bi[0] + 1 # 计算交集宽
ih = bi[3] - bi[1] + 1 # 计算交集高
if iw > 0 and ih > 0: # 如果相交，才开始计算交并比 compute overlap (IoU) = area of intersection / area of union
if obj['class_name'] in ['Ped', 'ped', 'Pedestrian', 'pedestrian']:
ua = (width_dt + 1) * (height_dt + 1) + (bbgtF[2] - bbgtF[0] + 1) * (bbgtF[3] - bbgtF[1] + 1) - iw * ih # 计算并集面积
ov = iw * ih / ua # 计算交并比
if ov > ovmax:
ovmax = ov
gt_matched = obj # 用gt_match记录与当前检测框bb匹配的真实标记框gt_match = {'class_name': 'Bic', 'bbox': [1035, 229, 1156, 526],
# 'Dis': 15.4, 'used': False, 'Dif': False, 'Occ_Coe': 0.0, 'Age': 'Adult', 'area': 35937}
else:
a_percentage = iw * ih / ((width_dt + 1) * (height_dt + 1))
if a_percentage > a_percentage_max:
a_percentage_max = a_percentage
gt_matched = obj
try: # gt_match={'class_name': 'Ped', 'bbox': '98 309 212 584 16.3', 'used': False, 'scale_dis': 'near'}gt_match=-1将出错不能进行。
if not in_boxes: #TODO:{'class_name': 'Ped', 'confidence': '1.0', 'file_id': 'Data20190113195620_050000', 'bbox': [1040.0, 0.0, 1312.0, 652.0], 'Dis': 20520.62}
gt_matched = gt_data_temp[0]
fp_dic['All'][idx, dis_dt_index] = 1
fp_dic[gt_matched['Wea']][idx, dis_dt_index] = 1
fp_dic[gt_matched['Height']][idx, dis_dt_index] = 1
else: #TODO:对匹配上扎推的人群、骑单车、摩托车的人、背景画、或者人类都难以识别的行人进行忽略，即：既不判定为TP又不判定为FP。
if gt_matched['class_name'] in ['Ped', 'ped', 'Pedestrian', 'pedestrian']:
if ovmax >= min_overlap: # 最小交并比设置为：min_overlap = MINOVERLAP = 0.5
dis_gt = gt_matched['Dis']
dis_gt_index = int(dis_gt // 10)
bbgtF = [float(x) for x in gt_matched['bbox']]
if cfg.evaluate_subset == 'Reasonable':
if not (gt_matched['Dif'] or float(gt_matched['Occ_Coe']) > cfg.Occ_threshold or dis_gt >= cfg.Dis_threshold):
if not bool(gt_matched['used']): # 换成布尔值
tp_dic['All'][idx, dis_gt_index] = 1
tp_dic[gt_matched['Wea']][idx, dis_gt_index] = 1
tp_dic[gt_matched['Height']][idx, dis_gt_index] = 1
# TODO：正确检测目标的距离误差求解
dis_dtp = round(Hf / height_dt, 2) # TODO: 将预测框像素高度直接估算为距离。
AE_dtp_instance = abs(dis_dtp - dis_gt) # TODO:AE=absolute_error
AE_dtp_seg_dic['All'][dis_gt_index].append(AE_dtp_instance)
AE_dtp_seg_dic[gt_matched['Wea']][dis_gt_index].append(AE_dtp_instance)
AE_dtp_seg_dic[gt_matched['Height']][dis_gt_index].append(AE_dtp_instance)
AE_DE_instance = abs(dis_dt - dis_gt) # TODO:AE=absolute_error
AE_DE_seg_dic['All'][dis_gt_index].append(AE_DE_instance)
AE_DE_seg_dic[gt_matched['Wea']][dis_gt_index].append(AE_DE_instance)
AE_DE_seg_dic[gt_matched['Height']][dis_gt_index].append(AE_DE_instance)
AER_dtp_instance = round(100 * AE_dtp_instance / dis_gt, 3) # TODO:AE=error
AER_dtp_seg_dic['All'][dis_gt_index].append(AER_dtp_instance)
AER_dtp_seg_dic[gt_matched['Wea']][dis_gt_index].append(AER_dtp_instance)
AER_dtp_seg_dic[gt_matched['Height']][dis_gt_index].append(AER_dtp_instance)
AER_DE_instance = round(100 * AE_DE_instance / dis_gt, 3) # TODO:AER = error_rate
AER_DE_seg_dic['All'][dis_gt_index].append(AER_DE_instance)
AER_DE_seg_dic[gt_matched['Wea']][dis_gt_index].append(AER_DE_instance)
AER_DE_seg_dic[gt_matched['Height']][dis_gt_index].append(AER_DE_instance)
if (abs(AER_DE_instance) > 15):
if img_id not in annos_AER_bad:
annos_AER_bad[img_id] = []
annos_AER_bad[img_id].append({'class_name': gt_matched['class_name'],
'BB_gt': gt_matched['bbox'], 'Dis_gt': dis_gt,
'BB_dt': bbdtF,
'Dis_dt': dis_dt, 'Wea': gt_matched['Wea'],
'Height': gt_matched['Height']})
# TODO：正确检测目标的距离误差求解
gt_matched['used'] = True # 真实标记框已被检测到，标记为已使用，以后的预测框不再考虑此真实标记框。
with open(gt_file, 'w') as f: # gt_file = '.temp_files/Tongxy13m161R850_gt.json'
f.write(json.dumps(gt_data_temp)) # 更新gt_data
f.close()
if img_id not in gt_file_used:
gt_file_used.append(img_id)
else:# false positive (multiple box_detection)
fp_dic['All'][idx, dis_gt_index] = 1
fp_dic[gt_matched['Wea']][idx, dis_gt_index] = 1
fp_dic[gt_matched['Height']][idx, dis_gt_index] = 1
else: #TODO: 忽略此检测框，不计入误报。
ignored_dt_count[dis_gt_index] += 1
elif cfg.evaluate_subset == 'All':
if dis_dt < cfg.Dis_threshold:
if not bool(gt_matched['used']): # 换成布尔值
tp_dic['All'][idx, dis_gt_index] = 1
tp_dic[gt_matched['Wea']][idx, dis_gt_index] = 1
tp_dic[gt_matched['Height']][idx, dis_gt_index] = 1
# TODO：正确检测目标的距离误差求解
dis_dtp = round(Hf / height_dt, 2) # TODO: 将预测框像素高度直接估算为距离。
AE_dtp_instance = abs(dis_dtp - dis_gt) # TODO:AE=absolute_error
AE_dtp_seg_dic['All'][dis_gt_index].append(AE_dtp_instance)
AE_dtp_seg_dic[gt_matched['Wea']][dis_gt_index].append(AE_dtp_instance)
AE_dtp_seg_dic[gt_matched['Height']][dis_gt_index].append(AE_dtp_instance)
AE_DE_instance = abs(dis_dt - dis_gt) # TODO:AE=absolute_error
AE_DE_seg_dic['All'][dis_gt_index].append(AE_DE_instance)
AE_DE_seg_dic[gt_matched['Wea']][dis_gt_index].append(AE_DE_instance)
AE_DE_seg_dic[gt_matched['Height']][dis_gt_index].append(AE_DE_instance)
AER_dtp_instance = round(100 * AE_dtp_instance / dis_gt, 3) # TODO:AE=error
AER_dtp_seg_dic['All'][dis_gt_index].append(AER_dtp_instance)
AER_dtp_seg_dic[gt_matched['Wea']][dis_gt_index].append(AER_dtp_instance)
AER_dtp_seg_dic[gt_matched['Height']][dis_gt_index].append(AER_dtp_instance)
AER_DE_instance = round(100 * AE_DE_instance / dis_gt, 3) # TODO:AER = error_rate
AER_DE_seg_dic['All'][dis_gt_index].append(AER_DE_instance)
AER_DE_seg_dic[gt_matched['Wea']][dis_gt_index].append(AER_DE_instance)
AER_DE_seg_dic[gt_matched['Height']][dis_gt_index].append(AER_DE_instance)
if (abs(AER_DE_instance) > 15):
if img_id not in annos_AER_bad:
annos_AER_bad[img_id] = []
annos_AER_bad[img_id].append({'class_name': gt_matched['class_name'],
'BB_gt': gt_matched['bbox'], 'Dis_gt': dis_gt,
'BB_dt': bbdtF,
'Dis_dt': dis_dt, 'Wea': gt_matched['Wea'],
'Height': gt_matched['Height']})
gt_matched['used'] = True # 真实标记框已被检测到，标记为已使用，以后的预测框不再考虑此真实标记框。
with open(gt_file, 'w') as f: # gt_file = '.temp_files/Tongxy13m161R850_gt.json'
f.write(json.dumps(gt_data_temp)) # 更新gt_data
f.close()
if img_id not in gt_file_used:
gt_file_used.append(img_id)
else:# TODO:第idx个检测框匹配上当前类的标记框，且交并比不小于min_overlap=0.5，但此标记框在之前已被检测框匹配到了，判定为真正例FP
fp_dic['All'][idx, dis_gt_index] = 1
fp_dic[gt_matched['Wea']][idx, dis_gt_index] = 1
fp_dic[gt_matched['Height']][idx, dis_gt_index] = 1
else: #TODO: 忽略此检测框，不计入误报。
ignored_dt_count[dis_gt_index] += 1
elif ovmax > 0: # 如果ovmax < min_overlap: # false positive
if cfg.evaluate_subset == 'Reasonable':
if not (gt_matched['Dif'] or float(gt_matched['Occ_Coe']) > cfg.Occ_threshold or dis_dt >= cfg.Dis_threshold):
fp_dic['All'][idx, dis_dt_index] = 1
fp_dic[gt_matched['Wea']][idx, dis_dt_index] = 1
fp_dic[gt_matched['Height']][idx, dis_dt_index] = 1
else: #TODO: 忽略此检测框，不计入误报。
ignored_dt_count[dis_dt_index] += 1
elif cfg.evaluate_subset == 'All':
if dis_dt < cfg.Dis_threshold:
fp_dic['All'][idx, dis_dt_index] = 1
fp_dic[gt_matched['Wea']][idx, dis_dt_index] = 1
fp_dic[gt_matched['Height']][idx, dis_dt_index] = 1
else: #TODO: 忽略此检测框，不计入误报。
ignored_dt_count[dis_dt_index] += 1
else: # elif gt_matched['class_name'] in ['Peo', 'Bic', 'Mot', 'Ign', 'bg', 'Bg', 'BG']:
if a_percentage_max >= min_overlap: #TODO:匹配上任何忽略区域，将被忽略。
ignored_dt_count[dis_dt_index] += 1
#print('Exception: {} in img_id:{}'.format(e, img_id))
else: #TODO:没有匹配上目标，也没有匹配上任何忽略区域，检测框判定为假正例fp。
fp_dic['All'][idx, dis_dt_index] = 1
fp_dic[gt_matched['Wea']][idx, dis_dt_index] = 1
fp_dic[gt_matched['Height']][idx, dis_dt_index] = 1
except Exception as e: #TODO:对没有匹配上任何目标的检测框判定为假正例fp。
# errormessage = '{}'.format(e)
s = sys.exc_info()
print("Exception: Error '%s' happened on line %d with image:'%s'" % (s[1], s[2].tb_lineno, img_id))
pdb.set_trace()
#fp_all[idx, dis_dt_index] = 1
if idx % idx_step != 0: #每个100个检测结果，计算一次MR_FPPI和PR
continue
flag_break = 0
for attr in MR_FPPI_dis_max_dic:
for dis_index in range(0, idx_Dis_max):
num_tp = tp_dic[attr][:idx+1, dis_index:dis_index+1].sum() # 找出不同距离段的TP的位置序列，#正确检测到的行人统计。tp_all = np.zeros((num_dt_boxes, 1))
num_fp = fp_dic[attr][:idx+1, dis_index:dis_index+1].sum() # 找出不同距离段的TP的位置序列，#错误检测到的行人统计。fp_all = np.zeros((num_dt_boxes, 1))
num_gt_boxes = sum(gt_counter_per_class[class_name][attr][dis_index:dis_index+1])
FPPI_seg = round(num_fp/num_test_imgs[attr], 6)
if num_gt_boxes == 0:
MissRate_seg=0
else:
MissRate_seg = round((num_gt_boxes - num_tp) / num_gt_boxes, 6)
if num_tp+num_fp != 0: #防止num_tp=0.0; num_fp=0.0
re_seg = 1 - MissRate_seg
pr_seg = round(num_tp/(num_tp+num_fp), 6)
PR_dis_seg_dic[attr][dis_index].append([pr_seg, re_seg])
MR_FPPI_dis_seg_dic[attr][dis_index].append([FPPI_seg, MissRate_seg])
num_tp = tp_dic[attr][:idx+1, :dis_index+1].sum() # 找出不同距离段的TP的位置序列，#正确检测到的行人统计。tp_all = np.zeros((num_dt_boxes, 1))
num_fp = fp_dic[attr][:idx+1, :dis_index+1].sum() # 找出不同距离段的TP的位置序列，#错误检测到的行人统计。fp_all = np.zeros((num_dt_boxes, 1))
num_gt_boxes = sum(gt_counter_per_class[class_name][attr][:dis_index+1])
FPPI_max = round(num_fp / num_test_imgs[attr], 6)
if num_gt_boxes == 0:
MissRate_max = 0
else:
MissRate_max = round((num_gt_boxes - num_tp) / num_gt_boxes, 6)
MR_FPPI_dis_max_dic[attr][dis_index].append([FPPI_max, MissRate_max])
if (num_tp + num_fp) != 0: #防止num_tp=0.0; num_fp=0.0
re_max = 1-MissRate_max
pr_max = round(num_tp / (num_tp+num_fp), 6)
PR_dis_max_dic[attr][dis_index].append([pr_max, re_max])
if (attr == 'All') and (dis_index*10+10 == cfg.Dis_threshold):
score_threshold = float(box_detection['confidence'])
if idx % 200 == 0:
print('idx=%d/%d;d<=%d;score_threshold=\033[30;41m %.4f\033[0m:[FPPI, MissRate]=[\033[30;42m %.6f\033[0m, \033[30;41m %.4f\033[0m]; ignored_dt_count=%d' % (idx, num_dt_boxes, dis_index * 10 + 10, score_threshold, FPPI_max, MissRate_max, sum(ignored_dt_count)))
# print(ignored_dt_count)
if FPPI_max > 10:
flag_break = 1 #TODO: 提前终止计算标识。
if flag_break == 1:
break
# TODO:***将判定为假正例fp的检测框保存下来，为后续可视化做准备。***将判定为假正例fp的检测框保存下来，为后续可视化做准备。***将判定为假正例fp的检测框保存下来，为后续可视化做准备。
if max(fp_dic['All'][idx, :int(cfg.Dis_threshold // 10)]) == 1:
if img_id not in annos_FP:
annos_FP[img_id] = []
annos_FP[img_id].append({'class_name': box_detection['class_name'], 'confidence': box_detection['confidence'],
'BB_dt': box_detection['bbox'], 'Dis_dt': box_detection['Dis'], 'Wea':gt_matched['Wea'],
'Height':gt_matched['Height']})
for gt_file in gt_files_list:
img_id = os.path.basename(gt_file)
img_id = img_id.split('_gt', 1)[0]
try:
gt_data_temp = json.load(open(gt_file))
except:
print("\033[5;31;47m\tImage:{}, is not in ground-truth. Please check that ground-truth and detect results are matched.\033[0m".format(img_id))
continue
for anno in gt_data_temp:
if anno['class_name'] not in ['Ped', 'ped', 'Pedestrian', 'pedestrian']:
continue
if anno['used']:
continue
if cfg.evaluate_subset == 'Reasonable':
if anno['Dif'] or (anno['Occ_Coe'] > cfg.Occ_threshold) or (anno['Dis'] > cfg.Dis_threshold): #TODO：检查此步是否执行到位？？？？
continue
elif cfg.evaluate_subset == 'All':
if anno['Dis'] > cfg.Dis_threshold: #TODO：检查此步是否执行到位？？？？
continue
if img_id not in annos_FN:
annos_FN[img_id] = []
annos_FN[img_id].append(anno)
AE_dtp_seg_dic, AE_dtp_max_dic = post_processing(AE_dtp_seg_dic)
AE_DE_seg_dic, AE_DE_max_dic = post_processing(AE_DE_seg_dic)
AER_dtp_seg_dic, AER_dtp_max_dic = post_processing(AER_dtp_seg_dic)
AER_DE_seg_dic, AER_DE_max_dic = post_processing(AER_DE_seg_dic)
# TODO:***将判定为假正例fp的检测框保存下来，为后续可视化做准备。***将判定为假正例fp的检测框保存下来，为后续可视化做准备。***将判定为假正例fp的检测框保存下来，为后续可视化做准备。
print('\n\nMR_FPPI_dis_max_dic:')
MR_FPPI_Plot(MR_FPPI_dis_max_dic, detectResults_Path, 'max') #TODO:max_or_seg
print('\n\nPR_dis_max_dic:')
PR_Plot(PR_dis_max_dic, detectResults_Path, 'max') #TODO:max_or_seg
print('\n\nMR_FPPI_dis_seg_dic:')
MR_FPPI_Plot(MR_FPPI_dis_seg_dic, detectResults_Path, 'seg') #TODO:max_or_seg
print('\n\nPR_dis_seg_dic:')
PR_Plot(PR_dis_seg_dic, detectResults_Path, 'seg') # TODO:max_or_seg
shutil.rmtree(TEMP_FILES_Path) # 删除临时文件夹及其下所有文件。
return MR_FPPI_dis_max_dic, MR_FPPI_dis_seg_dic,PR_dis_max_dic,PR_dis_seg_dic, AE_dtp_seg_dic, AE_dtp_max_dic,\
AE_DE_seg_dic, AE_DE_max_dic,AER_dtp_seg_dic, AER_dtp_max_dic,AER_DE_seg_dic, AER_DE_max_dic,\
annos_AER_bad, annos_FN, annos_FP
#TODO:*** 图像显示检测结果.***图像显示检测结果.***图像显示检测结果.***图像显示检测结果.***图像显示检测结果.***图像显示检测结果.***图像显示检测结果.
def visulize_FP_FN_bad_ER(imgs_data, dt_data_plot, imgs_dt_show_dir, annos_FP_Dis, annos_AER_bad_Dis, annos_miss_Ped_Dis, score_threshold):
txt_output_path = os.path.join(imgs_dt_show_dir, 'annos_GT_txt_FP_AER_bad_miss_Ped.txt')
annos_GT_txt = open(txt_output_path, 'w')
annos_GT_txt.write('path, x1, y1, x2, y2, Dis, Age, Dif, Sce, Wea, Occlusion, Ignore, pose, truncation, ID, cls\n')
#TODO: ***将标记写入记事本。 ***将标记写入记事本。 ***将标记写入记事本。 ***将标记写入记事本。 ***将标记写入记事本。
num_Bg_Img = 700000
cocoGt = COCO(cfg.annos_file)
images = imgs_data['images']
scenes = imgs_data['scenes']
weathers = imgs_data['weathers']
path_img_dt_FP_region_dir = os.path.join(imgs_dt_show_dir, 'visualize_FP')
if not os.path.exists(path_img_dt_FP_region_dir):
os.makedirs(path_img_dt_FP_region_dir)
path_img_dt_FP_region_dir_Bg = os.path.join(path_img_dt_FP_region_dir, 'visualize_FP_Bg')
if not os.path.exists(path_img_dt_FP_region_dir_Bg):
os.makedirs(path_img_dt_FP_region_dir_Bg)
path_img_dt_FP_region_dir_FP = os.path.join(path_img_dt_FP_region_dir, 'visualize_FP_FP')
if not os.path.exists(path_img_dt_FP_region_dir_FP):
os.makedirs(path_img_dt_FP_region_dir_FP)
path_img_dt_FP_region_dir_TP = os.path.join(path_img_dt_FP_region_dir, 'visualize_FP_TP')
if not os.path.exists(path_img_dt_FP_region_dir_TP):
os.makedirs(path_img_dt_FP_region_dir_TP)
path_img_dt_AER_bad_region_dir = os.path.join(imgs_dt_show_dir, 'visualize_AER_bad')
if not os.path.exists(path_img_dt_AER_bad_region_dir):
os.makedirs(path_img_dt_AER_bad_region_dir)
path_img_dt_AER_bad_region_dir_plus = os.path.join(path_img_dt_AER_bad_region_dir, 'visualize_AER_bad_Plus')
if not os.path.exists(path_img_dt_AER_bad_region_dir_plus):
os.makedirs(path_img_dt_AER_bad_region_dir_plus)
path_img_dt_AER_bad_region_dir_minus = os.path.join(path_img_dt_AER_bad_region_dir, 'visualize_AER_bad_minus')
if not os.path.exists(path_img_dt_AER_bad_region_dir_minus):
os.makedirs(path_img_dt_AER_bad_region_dir_minus)
path_img_dt_FN_region_dir = os.path.join(imgs_dt_show_dir, 'visualize_FN')
if not os.path.exists(path_img_dt_FN_region_dir):
os.makedirs(path_img_dt_FN_region_dir)
path_img_dt_FN_region_dir_Bg = os.path.join(path_img_dt_FN_region_dir, 'visualize_FN_Bg')
if not os.path.exists(path_img_dt_FN_region_dir_Bg):
os.makedirs(path_img_dt_FN_region_dir_Bg)
path_img_dt_FN_region_dir_FP = os.path.join(path_img_dt_FN_region_dir, 'visualize_FN_FP')
if not os.path.exists(path_img_dt_FN_region_dir_FP):
os.makedirs(path_img_dt_FN_region_dir_FP)
# img_data={'id': 100000, 'file_name': 'Data20181219200348_010000.png', 'height': 720, 'width': 1280,
# 'daytime': 'night', 'scenes_id': 2, 'weathers_id': 1, 'seasons_id': 0, 'recordings_id': 0, 'imageset': 'train'}
num_test_imgs = len(images)
for img_idx, img_data in enumerate(images):
img_name = img_data['file_name']
image_id = img_name.split('.', 1)[0]
print('\033[1;30;43m Process:%d / %d \033[0m:%s' % (img_idx, num_test_imgs, image_id))
try:
# img_path = os.path.join('E:\\Datasets\\NIRPed2021\\NIRPed\\images\\{}\\{}'.format(subset, img_name))
img_path = '.\\data\\miniNIRPed\\images\\{}\\{}'.format(subset, img_name)
except:
continue
anno_ids = cocoGt.getAnnIds(imgIds=img_data['id']) # anno_id=1037542
annos_GT = cocoGt.loadAnns(ids=anno_ids)
'''annos_GT =[{'occluded': None, 'difficult': None, 'bbox': [453, 207, 30, 54], 'id': 1000007, 'category_id': 4, 'image_id': 1000043, 'pose_id': 5, 'tracking_id': 1000000, 'ignore': 1, 'area': 1620, 'truncated': False},
{'occluded': None, 'difficult': None, 'bbox': [514, 233, 95, 40], 'id': 1000010, 'category_id': 4, 'image_id': 1000043, 'pose_id': 5, 'tracking_id': 1000001, 'ignore': 1, 'area': 3800, 'truncated': False}]
'''
Wea = weathers[img_data['weathers_id']]['name']
Sce = scenes[img_data['scenes_id']]['name']
img_dt_results_path = os.path.join(imgs_dt_show_dir, image_id + '.png')
if os.path.exists(img_dt_results_path):
print('Notion:Detect results have shown for image {} .'.format(img_dt_results_path))
continue
dt_boxes = [box for box in dt_data_plot if box['file_id'] == image_id]
# dt_boxes = [{'class_name': 'Ped', 'confidence': '0.999998', 'file_id': 'Data20181219200348_020000', 'bbox': [448.0, 202.0, 576.0, 517.0], 'Dis': 13.91},
# {'class_name': 'Ped', 'confidence': '0.999998', 'file_id': 'Data20181219200348_020000', 'bbox': [448.0, 202.0, 576.0, 517.0], 'Dis': 13.91},
# {'class_name': 'Ped', 'confidence': '0.999997', 'file_id': 'Data20181219200348_020000', 'bbox': [32.0, 179.0, 160.0, 539.0], 'Dis': 12.74},
# {'class_name': 'Ped', 'confidence': '0.999997', 'file_id': 'Data20181219200348_020000', 'bbox': [32.0, 179.0, 160.0, 539.0], 'Dis': 12.74},
# {'class_name': 'Ped', 'confidence': '0.999964', 'file_id': 'Data20181219200348_020000', 'bbox': [208.0, 202.0, 336.0, 494.0], 'Dis': 15.45},
# {'class_name': 'Ped', 'confidence': '0.999964', 'file_id': 'Data20181219200348_020000', 'bbox': [208.0, 202.0, 336.0, 494.0], 'Dis': 15.45}]
if dt_boxes == []:
print('Notion:image {} detect no object. Continue!'.format(img_path))
#continue
try:
img = cv2.imread(img_path)
except:
print('Notion:do not find image: {}.'.format(img_path))
# TODO: ***将背景图片写入记事本。 ***将背景图片写入记事本。 ***将背景图片写入记事本。 ***将背景图片写入记事本。 ***将背景图片写入记事本。 ***将背景图片写入记事本。
if annos_GT == []:
annos_GT_txt.write(img_dt_results_path + ',' + '0,0,0,0' + ',' + '100' + ',' + '0,0,0,0' + ',' + 'Neu' + ',' + 'Esay' + ',' + Sce + ',' + Wea + ',' + str(num_Bg_Img) + ',' + 'Bg_Img' + '\n')
image_with_boxes =Image.fromarray(np.uint8(img))
draw = ImageDraw.Draw(image_with_boxes)
'''gt_data_temp=[{"class_name": "Ped", "bbox": [39, 191, 153, 525], "Dis": 13.4, "used": false, "Dif": false, "Occ_Coe": 0.0, "Age": "Adult", "area": 38076},
{"class_name": "Ped", "bbox": [434, 208, 569, 511], "Dis": 13.0, "used": false, "Dif": false, "Occ_Coe": 0.0, "Age": "Adult", "area": 40905},
{"class_name": "Ped", "bbox": [196, 217, 346, 517], "Dis": 13.2, "used": false, "Dif": false, "Occ_Coe": 0.0, "Age": "Adult", "area": 45000}]'''
# TODO: ***切片FP 检测结果。***切片FP 检测结果。***切片FP 检测结果。***切片FP 检测结果。***切片FP 检测结果。***切片FP 检测结果。***切片FP 检测结果。
# TODO: ***切片FP 检测结果。***切片FP 检测结果。***切片FP 检测结果。***切片FP 检测结果。***切片FP 检测结果。***切片FP 检测结果。***切片FP 检测结果。
if image_id in annos_FP_Dis:
annos_FP = annos_FP_Dis[image_id]
for index, anno in enumerate(annos_FP): #anno={'class_name': 'Ped', 'confidence': '0.999982', 'BB_dt': [240.0, 224.0, 288.0, 404.0], 'Dis_dt': 25.35}
b = anno['BB_dt']
left, top, right, bottom = int(b[0]), int(b[1]), int(b[2]), int(b[3])
#TODO:***切片显示FP。***切片显示FP。***切片显示FP。***切片显示FP。***切片显示FP。***切片显示FP。
ovmax = 0
'''annos_GT=[{'id': 10036185, 'category_id': 1, 'image_id': 107911, 'pose_id': 0, 'tracking_id': 100000, 'bbox': [294, 200, 68, 225], 'Dis': 19.2, 'vis_box': [294, 200, 68, 225],
'Occ_Coe': 0.0, 'Dif': False, 'Ign': 0, 'area': 15300, 'Tru': 0, 'Age': 'Adult'}, {'id': 10036186, 'category_id': 1, 'image_id': 107911, 'pose_id': 0, 'tracking_id': 100000,
'bbox': [343, 214, 61, 196], 'Dis': 21.1, 'vis_box': [343, 214, 61, 196], 'Occ_Coe': 0.0, 'Dif': False, 'Ign': 0, 'area': 11956, 'Tru': 0, 'Age': 'Adult'},
{'id': 10036187, 'category_id': 1, 'image_id': 107911, 'pose_id': 0, 'tracking_id': 100000, 'bbox': [255, 219, 49, 178], 'Dis': 23.3, 'vis_box': [255, 219, 49, 178],
'Occ_Coe': 0.0, 'Dif': False, 'Ign': 0, 'area': 8722, 'Tru': 0, 'Age': 'Adult'}, {'id': 10036188, 'category_id': 1, 'image_id': 107911, 'pose_id': 0, 'tracking_id': 100000,
'bbox': [491, 218, 59, 173], 'Dis': 25.0, 'vis_box': [491, 218, 59, 173], 'Occ_Coe': 0.0, 'Dif': False, 'Ign': 0, 'area': 10207, 'Tru': 0, 'Age': 'Adult'},
{'id': 10036189, 'category_id': 1, 'image_id': 107911, 'pose_id': 0, 'tracking_id': 100000, 'bbox': [619, 220, 78, 163], 'Dis': 27.0, 'vis_box': [619, 220, 78, 163],
'Occ_Coe': 0.0, 'Dif': False, 'Ign': 0, 'area': 12714, 'Tru': 0, 'Age': 'Adult'}]'''
for gt_box in annos_GT: # TODO: ***以标记框高度排序。
cat = cocoGt.loadCats(ids=gt_box['category_id'])[0] # cat={'name': 'pedestrian', 'id': 1}
class_name = cat['name']
if class_name not in ['Ped', 'ped', 'Pedestrian', 'pedestrian']:
continue
b_gt = gt_box['bbox']
x1, y1, x2, y2 = b_gt[0], b_gt[1], b_gt[0] + b_gt[2], b_gt[1] + b_gt[3]
bi = [max(left, x1), max(top, y1), min(right, x2), min(bottom, y2)] # 用于计算检测框和真实标记框的交集面积
iw = bi[2] - bi[0] + 1 # 计算交集宽
ih = bi[3] - bi[1] + 1 # 计算交集高
if iw > 0 and ih > 0: # 如果相交，才开始计算交并比 compute overlap (IoU) = area of intersection / area of union
ua = (right - left + 1) * (bottom - top + 1) + (x2 - x1 + 1) * (y2 - y1 + 1) - iw * ih # 计算并集面积
ov = iw * ih / ua # 计算交并比
if 0 < ov <= 1:
if ov > ovmax:
ovmax = ov
gt_match_box = gt_box # 用gt_match记录与当前检测框bb匹配的真实标记框gt_match = {'class_name': 'Bic', 'bbox': [1035, 229, 1156, 526],
else:
print('error overlap = %.1f%%' % ov * 100)
if ovmax >= 0.5:
if (gt_match_box['Dif'] or float(gt_match_box['Occ_Coe']) > cfg.Occ_threshold or float(gt_match_box['Dis']) >= cfg.Dis_threshold):
continue
if ovmax > 0.1:
dis_gt = gt_match_box['Dis']
b_gt = gt_match_box['bbox']
w = b_gt[2]
h = b_gt[3]
x1, y1, x2, y2 = b_gt[0], b_gt[1], b_gt[0] + w, b_gt[1] + h
w_edge = int(0.2 * w)
h_edge = int(0.12 * h)
'''annos_GT =[{'occluded': None, 'difficult': None, 'bbox': [453, 207, 30, 54], 'id': 1000007, 'category_id': 4, 'image_id': 1000043, 'pose_id': 5, 'tracking_id': 1000000, 'ignore': 1, 'area': 1620, 'truncated': False},
{'occluded': None, 'difficult': None, 'bbox': [514, 233, 95, 40], 'id': 1000010, 'category_id': 4, 'image_id': 1000043, 'pose_id': 5, 'tracking_id': 1000001, 'ignore': 1, 'area': 3800, 'truncated': False}]
'''
dt_match_box = -1
if ovmax >= 0.5:
ovmax2 = MINOVERLAP
# pdb.set_trace()
for dt_box in dt_boxes:
class_name_dt = dt_box['class_name']
if class_name_dt not in ['Ped', 'ped', 'Pedestrian', 'pedestrian']:
continue
b2 = dt_box['bbox']
if abs(b2[0]-left+b2[1]-top+b2[2]-right+b2[3]-bottom) < 10:
continue
left2, top2, right2, bottom2 = b2[0], b2[1], b2[2], b2[3]
bi = [max(left2, x1), max(top2, y1), min(right2, x2), min(bottom2, y2)] # 用于计算检测框和真实标记框的交集面积
iw = bi[2] - bi[0] + 1 # 计算交集宽
ih = bi[3] - bi[1] + 1 # 计算交集高
if iw > 0 and ih > 0: # 如果相交，才开始计算交并比 compute overlap (IoU) = area of intersection / area of union
ua = (right2 - left2 + 1) * (bottom2 - top2 + 1) + (x2 - x1 + 1) * (y2 - y1 + 1) - iw * ih # 计算并集面积
ov = iw * ih / ua # 计算交并比