代码主要是参考bubbliiing的github YOLOv3的代码：github.com/bubbliiiing…
对于源代码的解读
map部分
get_map函数
def get_map(MINOVERLAP, draw_plot, path = './map_out'):
""" 参数：MINOVERLAP用于指定想要获得的mAP0.x draw_plot是否要画图，传入的是True """
# 对下面的文件夹路径，有则加载，没有则创建
GT_PATH = os.path.join(path, 'ground-truth')
DR_PATH = os.path.join(path, 'detection-results')
IMG_PATH = os.path.join(path, 'images-optional')
TEMP_FILES_PATH = os.path.join(path, '.temp_files')
RESULTS_FILES_PATH = os.path.join(path, 'results')
show_animation = True
if os.path.exists(IMG_PATH):
for dirpath, dirnames, files in os.walk(IMG_PATH):
if not files:
show_animation = False
else:
show_animation = False
# 创建临时文件夹，存储中间文件
if not os.path.exists(TEMP_FILES_PATH):
os.makedirs(TEMP_FILES_PATH)
# 存在结果文件的时候，直接删除
if os.path.exists(RESULTS_FILES_PATH):
shutil.rmtree(RESULTS_FILES_PATH)
# 我们要绘制图像的时候我们需要创建四个指标 AP,F1,Recall,Precision
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_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()
gt_counter_per_class = {}
counter_images_per_class = {}
# 整个接下来的一段都是构造一个临时文件，存储的都是json数据
for txt_file in ground_truth_files_list:
# 循环遍历真实框，得到img_id
file_id = txt_file.split(".txt", 1)[0]
file_id = os.path.basename(os.path.normpath(file_id))
# 加载预测的文件
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(error_msg)
# 行数据列表 类名，置信度，x1,y1,x2,y2
lines_list = file_lines_to_list(txt_file)
bounding_boxes = []
# 对存在difficult设置flag
is_difficult = False
already_seen_classes = []
for line in lines_list:
# 循环每条数据，我们的类名中存在xxx xxx的格式，所以我们使用split的方式可能会出现错误
# try针对大多数都是可以成功分离的，对于异常的情况我们在except中进行处理
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:
#
if "difficult" in line:
line_split = line.split()
_difficult = line_split[-1]
bottom = line_split[-2]
right = line_split[-3]
top = line_split[-4]
left = line_split[-5]
class_name = ""
# 对类名进行拼接
for name in line_split[:-5]:
class_name += name + " "
class_name = class_name[:-1]
is_difficult = True
else:
line_split = line.split()
bottom = line_split[-1]
right = line_split[-2]
top = line_split[-3]
left = line_split[-4]
class_name = ""
# 对类名进行拼接
for name in line_split[:-4]:
class_name += name + " "
class_name = class_name[:-1]
# 将四个坐标进行拼接
bbox = left + " " + top + " " + right + " " + bottom
# 根据是否是difficult对数据分成两种格式
if is_difficult:
bounding_boxes.append({"class_name":class_name, "bbox":bbox, "used":False, "difficult":True})
is_difficult = False
else:
# 注意这个used这个地方我们的used的设置，后面我们真实框中只有一个飞机，我们预测出来两个飞机，则只能是一真一假
# used判断这个真实的物体是不是真的被预测过了
bounding_boxes.append({"class_name":class_name, "bbox":bbox, "used":False})
# 通过这个gt_counter_per_class对真实存在的物体进行统计
if class_name in gt_counter_per_class:
gt_counter_per_class[class_name] += 1
else:
gt_counter_per_class[class_name] = 1
# 统计类别
if class_name not in already_seen_classes:
if class_name in counter_images_per_class:
counter_images_per_class[class_name] += 1
else:
counter_images_per_class[class_name] = 1
already_seen_classes.append(class_name)
# 对上面的json数据写入临时文件
# gt_counter_per_class：{'aeroplane': 28, 'person': 451, 'boat': 35, 'dog': 59, 'sheep': 14, 'cat': 33, 'tvmonitor': 29, 'bottle': 35, 'bus': 27, 'car': 109, 'bird': 70, 'horse': 40, 'pottedplant': 47, 'chair': 60, 'motorbike': 35, 'sofa': 31, 'diningtable': 17, 'bicycle': 36, 'cow': 22, 'train': 28}，所有图片中包含aeroplane一共28个
# counter_images_per_class：{'aeroplane': 25, 'person': 204, 'boat': 21, 'dog': 50, 'sheep': 5, 'cat': 32, 'tvmonitor': 27, 'bottle': 26, 'bus': 17, 'car': 69, 'bird': 38, 'horse': 31, 'pottedplant': 27, 'chair': 40, 'motorbike': 25, 'sofa': 28, 'diningtable': 14, 'bicycle': 28, 'cow': 13, 'train': 21}，所有图片中包含aeroplane的图片一共25张，一张图片多个aeroplane也只统计一次
with open(TEMP_FILES_PATH + "/" + file_id + "_ground_truth.json", 'w') as outfile:
json.dump(bounding_boxes, outfile)
# 类别列表
gt_classes = list(gt_counter_per_class.keys())
# 排序
gt_classes = sorted(gt_classes)
# 一共多少个类别
n_classes = len(gt_classes)
# 获取预测
dr_files_list = glob.glob(DR_PATH + '/*.txt')
# 排序
dr_files_list.sort()
# 对每个类进行循环,这个部分的整体的思想就是，我们对所有的预测文件进行扫描，然后我们将某一类的预测框进行汇总，排序后存储成jsons数据
# 因为是按照置信度进行排序，所以我们针对这个类进行和真实框物体进行扫描，看能否对的上
for class_index, class_name in enumerate(gt_classes):
bounding_boxes = []
# 对预测的文件列表进行循环
for txt_file in dr_files_list:
# 和上面套路一样
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(error_msg)
# 获取lines_data_list
lines = file_lines_to_list(txt_file)
for line in lines:
# 循环每条数据，我们的类名中存在xxx xxx的格式，所以我们使用split的方式可能会出现错误
# try针对大多数都是可以成功分离的，对于异常的情况我们在except中进行处理
try:
tmp_class_name, confidence, left, top, right, bottom = line.split()
except:
line_split = line.split()
bottom = line_split[-1]
right = line_split[-2]
top = line_split[-3]
left = line_split[-4]
confidence = line_split[-5]
tmp_class_name = ""
for name in line_split[:-5]:
tmp_class_name += name + " "
tmp_class_name = tmp_class_name[:-1]
if tmp_class_name == class_name:
bbox = left + " " + top + " " + right + " " +bottom
# 注意这个地方我们需要两个数据，confidence用于排序，将真正大概绿预测放在前面
# file_id 是去找到对应的文件进行对比
bounding_boxes.append({"confidence":confidence, "file_id":file_id, "bbox":bbox})
# 进行排序
bounding_boxes.sort(key=lambda x:float(x['confidence']), reverse=True)
# 将中间数据写入到json文件中
with open(TEMP_FILES_PATH + "/" + class_name + "_dr.json", 'w') as outfile:
json.dump(bounding_boxes, outfile)
sum_AP = 0.0
ap_dictionary = {}
lamr_dictionary = {}
# 开启结果文件，记录结果
with open(RESULTS_FILES_PATH + "/results.txt", 'w') as results_file:
results_file.write("# AP and precision/recall per class\n")
count_true_positives = {}
# 我们对每个类进行扫描
for class_index, class_name in enumerate(gt_classes):
count_true_positives[class_name] = 0
# 加载这个类的所有预测框，已经按照confidence排好序了
dr_file = TEMP_FILES_PATH + "/" + class_name + "_dr.json"
dr_data = json.load(open(dr_file))
# 我们构造一个核预测框同样大小的[0,0,...],用于记录正确和错误的信息
nd = len(dr_data)
tp = [0] * nd
fp = [0] * nd
score = [0] * nd
# 记录置信度大于0.5时候的index下标
score05_idx = 0
# 然后我们对预测框列表进行循环
for idx, detection in enumerate(dr_data):
# image_id
file_id = detection["file_id"]
# 置信度
score[idx] = float(detection["confidence"])
# 记录下标，主要是记录map0.5的时候一共多少个数据
if score[idx] > 0.5:
score05_idx = idx
# 画图相关操作以后在丰富
if show_animation:
ground_truth_img = glob.glob1(IMG_PATH, file_id + ".*")
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:
img = cv2.imread(IMG_PATH + "/" + ground_truth_img[0])
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()
bottom_border = 60
BLACK = [0, 0, 0]
img = cv2.copyMakeBorder(img, 0, bottom_border, 0, 0, cv2.BORDER_CONSTANT, value=BLACK)
# 我们根据image_id取出来真实的框的信息
gt_file = TEMP_FILES_PATH + "/" + file_id + "_ground_truth.json"
ground_truth_data = json.load(open(gt_file))
ovmax = -1
gt_match = -1
# bounding_box 我们得到四个坐标
bb = [float(x) for x in detection["bbox"].split()]
# 我们对真实框信息进行扫描
for obj in ground_truth_data:
# 要是类相同的话
if obj["class_name"] == class_name:
# 我们将bbox的信息放到列表中
bbgt = [ float(x) for x in obj["bbox"].split() ]
# 下面就是iou的计算
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:
ua = (bb[2] - bb[0] + 1) * (bb[3] - bb[1] + 1) + (bbgt[2] - bbgt[0]
+ 1) * (bbgt[3] - bbgt[1] + 1) - iw * ih
# iou的公式
ov = iw * ih / ua
# 记录最大的iou值和{obj数据}
if ov > ovmax:
ovmax = ov
gt_match = obj
if show_animation:
status = "NO MATCH FOUND!"
# 最小的overlop设置成0.5
min_overlap = MINOVERLAP
# 这里的比较有点意思，首先需要满足的是iou面积大于0.5，第二去除difficult,第三这个obj没有被更牛逼的计算框匹配到，used=False
# 综合以上的三个条件我们就认为是预测的就是准确的
if ovmax >= min_overlap:
if "difficult" not in gt_match:
if not bool(gt_match["used"]):
# 将指定的位置置成1，这样的话，证明预测框表现良好
tp[idx] = 1
# 这个obj的used直接置成True
gt_match["used"] = True
# 预测出来的类别数加上1
count_true_positives[class_name] += 1
# 更新ground_truth的信息
with open(gt_file, 'w') as f:
f.write(json.dumps(ground_truth_data))
if show_animation:
status = "MATCH!"
else:
# 虽然我们的预测框和真实框之间的iou满足条件，但是这个物体已经被匹配过了，我们的fp中相对位置变成1
fp[idx] = 1
if show_animation:
status = "REPEATED MATCH!"
else:
# iou没有过关，直接fp指定位置置成1
fp[idx] = 1
if ovmax > 0:
status = "INSUFFICIENT OVERLAP"
""" Draw image to show animation """
# 否，跳过
if show_animation:
height, widht = img.shape[:2]
white = (255,255,255)
light_blue = (255,200,100)
green = (0,255,0)
light_red = (30,30,255)
margin = 10
# 1nd line
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)
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:
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)
cv2.imshow("Animation", img)
cv2.waitKey(20)
output_img_path = RESULTS_FILES_PATH + "/images/detections_one_by_one/" + class_name + "_detection" + str(idx) + ".jpg"
cv2.imwrite(output_img_path, img)
cv2.imwrite(img_cumulative_path, img_cumulative)
# 统计信息计算
cumsum = 0
# 我们计算fp = 1的数量放在cunsum中，最终fp累计展示：[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 3, 3, 4, 5, 5, 6, 7, 8, 9, 10, 10, 11, 12, 13, 14, 15, 16, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]
for idx, val in enumerate(fp):
fp[idx] += cumsum
cumsum += val
# 同上，累计结果展示：[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 20, 21, 22, 22, 22, 23, 23, 24, 24, 24, 25, 25, 25, 25, 25, 25, 26, 26, 26, 26, 26, 26, 26, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27]
cumsum = 0
for idx, val in enumerate(tp):
tp[idx] += cumsum
cumsum += val
# rec:[0.03571428571428571, 0.07142857142857142, 0.10714285714285714, 0.14285714285714285, 0.17857142857142858, 0.21428571428571427, 0.25, 0.2857142857142857, 0.32142857142857145, 0.35714285714285715, 0.39285714285714285, 0.42857142857142855, 0.4642857142857143, 0.5, 0.5357142857142857, 0.5714285714285714, 0.6071428571428571, 0.6428571428571429, 0.6785714285714286, 0.7142857142857143, 0.7142857142857143, 0.75, 0.7857142857142857, 0.7857142857142857, 0.7857142857142857, 0.8214285714285714, 0.8214285714285714, 0.8571428571428571, 0.8571428571428571, 0.8571428571428571, 0.8928571428571429, 0.8928571428571429, 0.8928571428571429, 0.8928571428571429, 0.8928571428571429, 0.8928571428571429, 0.9285714285714286, 0.9285714285714286, 0.9285714285714286, 0.9285714285714286, 0.9285714285714286, 0.9285714285714286, 0.9285714285714286, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143, 0.9642857142857143]
# 这个地方是召回(查全)率，正好是查找出来的数据量比上真实类别的总数量，框越多，查全率越准
rec = tp[:]
for idx, val in enumerate(tp):
rec[idx] = float(tp[idx]) / np.maximum(gt_counter_per_class[class_name], 1)
# prec:[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9523809523809523, 0.9545454545454546, 0.9565217391304348, 0.9565217391304348, 0.9166666666666666, 0.92, 0.8846153846153846, 0.8888888888888888, 0.8571428571428571, 0.8275862068965517, 0.8333333333333334, 0.8064516129032258, 0.78125, 0.7575757575757576, 0.7352941176470589, 0.7142857142857143, 0.7222222222222222, 0.7027027027027027, 0.6842105263157895, 0.6666666666666666, 0.65, 0.6341463414634146, 0.6190476190476191, 0.627906976744186, 0.6136363636363636, 0.6, 0.5869565217391305, 0.574468085106383, 0.5625, 0.5510204081632653, 0.54, 0.5294117647058824, 0.5192307692307693, 0.5094339622641509, 0.5, 0.4909090909090909, 0.48214285714285715, 0.47368421052631576, 0.46551724137931033, 0.4576271186440678, 0.45, 0.4426229508196721, 0.43548387096774194, 0.42857142857142855, 0.421875, 0.4153846153846154, 0.4090909090909091, 0.40298507462686567, 0.39705882352941174, 0.391304347826087, 0.38571428571428573, 0.38028169014084506, 0.375, 0.3698630136986301, 0.36486486486486486, 0.36, 0.35526315789473684, 0.35064935064935066, 0.34615384615384615, 0.34177215189873417, 0.3375, 0.3333333333333333, 0.32926829268292684, 0.3253012048192771, 0.32142857142857145, 0.3176470588235294, 0.313953488372093, 0.3103448275862069, 0.3068181818181818, 0.30337078651685395, 0.3, 0.2967032967032967, 0.29347826086956524, 0.2903225806451613, 0.2872340425531915, 0.28421052631578947, 0.28125, 0.27835051546391754, 0.2755102040816326, 0.2727272727272727, 0.27, 0.26732673267326734, 0.2647058823529412, 0.2621359223300971, 0.25961538461538464, 0.2571428571428571, 0.25471698113207547, 0.2523364485981308, 0.25, 0.24770642201834864, 0.24545454545454545, 0.24324324324324326, 0.24107142857142858, 0.23893805309734514, 0.23684210526315788, 0.23478260869565218, 0.23275862068965517, 0.23076923076923078, 0.2288135593220339, 0.226890756302521, 0.225, 0.2231404958677686, 0.22131147540983606, 0.21951219512195122, 0.21774193548387097, 0.216, 0.21428571428571427, 0.2125984251968504, 0.2109375, 0.20930232558139536, 0.2076923076923077, 0.20610687022900764, 0.20454545454545456, 0.20300751879699247, 0.20149253731343283, 0.2, 0.19852941176470587, 0.19708029197080293, 0.1956521739130435, 0.19424460431654678, 0.19285714285714287, 0.19148936170212766, 0.19014084507042253, 0.1888111888111888, 0.1875, 0.18620689655172415, 0.18493150684931506, 0.1836734693877551, 0.18243243243243243, 0.18120805369127516]
# 这个地方是准确率，查找出来的真实类的数据量占所有框的比例，框越多，准确率越低
prec = tp[:]
for idx, val in enumerate(tp):
prec[idx] = float(tp[idx]) / np.maximum((fp[idx] + tp[idx]), 1)
# 计算AP和mpre,mrec
ap, mrec, mprec = voc_ap(rec[:], prec[:])
# 计算F1的值
F1 = np.array(rec)*np.array(prec)*2 / np.where((np.array(prec)+np.array(rec))==0, 1, (np.array(prec)+np.array(rec)))
# 计算所有类下的AP得到总和
sum_AP += ap
# 构造输出信息
text = "{0:.2f}%".format(ap*100) + " = " + class_name + " AP " #class_name + " AP = {0:.2f}%".format(ap*100)
# 其实就是我们去取score05的下标的位置上的各个数据，假设score05_idx = 25，我们就会得到F1，rec,prec的在该下标下的数据值
# 作为我们的map0.5时候的召回率，准确率和F1分数
# 下面这一大段都是在构造输出到前台的数据字符串的格式
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 "
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 len(prec)>0:
print(text + "\t||\tscore_threhold=0.5 : " + "F1=" + "{0:.2f}".format(F1[score05_idx])\
+ " ; Recall=" + "{0:.2f}%".format(rec[score05_idx]*100) + " ; Precision=" + "{0:.2f}%".format(prec[score05_idx]*100))
else:
print(text + "\t||\tscore_threhold=0.5 : F1=0.00% ; Recall=0.00% ; Precision=0.00%")
# 将数据放入到ap字典中
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
# 下面全都是绘图的部分，不进行详细分析
if draw_plot:
plt.plot(rec, prec, '-o')
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')
fig = plt.gcf()
fig.canvas.set_window_title('AP ' + class_name)
plt.title('class: ' + text)
plt.xlabel('Recall')
plt.ylabel('Precision')
axes = plt.gca()
axes.set_xlim([0.0,1.0])
axes.set_ylim([0.0,1.05])
fig.savefig(RESULTS_FILES_PATH + "/AP/" + class_name + ".png")
plt.cla()
plt.plot(score, F1, "-", color='orangered')
plt.title('class: ' + F1_text + "\nscore_threhold=0.5")
plt.xlabel('Score_Threhold')
plt.ylabel('F1')
axes = plt.gca()
axes.set_xlim([0.0,1.0])
axes.set_ylim([0.0,1.05])
fig.savefig(RESULTS_FILES_PATH + "/F1/" + class_name + ".png")
plt.cla()
plt.plot(score, rec, "-H", color='gold')
plt.title('class: ' + Recall_text + "\nscore_threhold=0.5")
plt.xlabel('Score_Threhold')
plt.ylabel('Recall')
axes = plt.gca()
axes.set_xlim([0.0,1.0])
axes.set_ylim([0.0,1.05])
fig.savefig(RESULTS_FILES_PATH + "/Recall/" + class_name + ".png")
plt.cla()
plt.plot(score, prec, "-s", color='palevioletred')
plt.title('class: ' + Precision_text + "\nscore_threhold=0.5")
plt.xlabel('Score_Threhold')
plt.ylabel('Precision')
axes = plt.gca()
axes.set_xlim([0.0,1.0])
axes.set_ylim([0.0,1.05])
fig.savefig(RESULTS_FILES_PATH + "/Precision/" + class_name + ".png")
plt.cla()
if show_animation:
cv2.destroyAllWindows()
results_file.write("\n# mAP of all classes\n")
# 最后得到的总的map的值除以总的类别数，就是最终的平均的map
mAP = sum_AP / n_classes
text = "mAP = {0:.2f}%".format(mAP*100)
results_file.write(text + "\n")
print(text)
shutil.rmtree(TEMP_FILES_PATH)
""" Count total of detection-results """
det_counter_per_class = {}
for txt_file in dr_files_list:
lines_list = file_lines_to_list(txt_file)
for line in lines_list:
class_name = line.split()[0]
if class_name in det_counter_per_class:
det_counter_per_class[class_name] += 1
else:
det_counter_per_class[class_name] = 1
dr_classes = list(det_counter_per_class.keys())
""" 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_name not in gt_classes:
count_true_positives[class_name] = 0
""" 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)
""" 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,
'',
)
# """
# 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
# )
""" 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,
""
)
获得的类别预测框json
真实文件的框json
- 对于VOC_AP的调用
def voc_ap(rec, prec):
# 这里面有个核心就是，我们的准确率是框越多，准确率越低，因为匹配上的框比较少，但是recall越高，是recall是查全率，框越多，越容易框到所有物体，
# 所以recall框越多，数值越大；precision框越多，值越小
""" --- 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)); """
# 这个部分实际上是在制作精度下降，应为我们可以知道我们的框越多，我们的准确率和召回率都会下降，我们其实在每个下标的位置保留的就是
# 当前框数下的准确率
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; """
# 我们主要是计算面积，横坐标是召回率，纵坐标是准确率，绘制的图像，我们可以得到多个点，将每个点的长宽补齐，最终形成的面积就是AP
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)); """
# 计算矩形面积的和，得到AP
ap = 0.0
for i in i_list:
ap += ((mrec[i]-mrec[i-1])*mpre[i])
# 将AP返回
return ap, mrec, mpre
AP的计算图，我们寻找不变的点的，主要是为了确定矩形的长宽信息，方便计算。
最后得到的效果如图所示：
我们看下其他的统计数据
- 每个类的物品的数量
- 单个类的AP的图像
- 单个类的F1值图像
- 单个类的Precision
- 单个类的recall
- log-average miss rate的图像
今天的文章YOLOv3的源代码精度理解(十二) get_map函数分享到此就结束了，感谢您的阅读。
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