update,
This commit is contained in:
louiscklaw
@@ -0,0 +1,886 @@
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|
||||
<_>
|
||||
16 7 2 4 2.</_></rects>
|
||||
<tilted>0</tilted></_>
|
||||
<_>
|
||||
<rects>
|
||||
<_>
|
||||
16 18 4 2 -1.</_>
|
||||
<_>
|
||||
18 18 2 2 2.</_></rects>
|
||||
<tilted>0</tilted></_>
|
||||
<_>
|
||||
<rects>
|
||||
<_>
|
||||
18 0 1 20 -1.</_>
|
||||
<_>
|
||||
18 5 1 10 2.</_></rects>
|
||||
<tilted>0</tilted></_></features></cascade>
|
||||
</opencv_storage>
|
||||
@@ -0,0 +1,127 @@
|
||||
import cv2
|
||||
import os,sys
|
||||
|
||||
count_good_result = 0
|
||||
count_good_result_base = 0
|
||||
count_bad_result = 0
|
||||
count_bad_result_base = 0
|
||||
|
||||
total_test_run = 0
|
||||
|
||||
bad_result_African = 0
|
||||
bad_result_Beach = 0
|
||||
bad_result_Building = 0
|
||||
bad_result_Bus = 0
|
||||
bad_result_Dinosaur = 0
|
||||
bad_result_Dish = 0
|
||||
bad_result_Elephant = 0
|
||||
bad_result_Flower = 0
|
||||
bad_result_Horse = 0
|
||||
bad_result_Mountain = 0
|
||||
|
||||
# 获取 XML 文件，加载人脸检测器
|
||||
faceCascade = cv2.CascadeClassifier("cascade.xml")
|
||||
|
||||
# remove all jpg files in iut_re folder
|
||||
for f in os.listdir("iut_re"):
|
||||
if f.endswith(".jpg"):
|
||||
os.remove(os.path.join("iut_re", f))
|
||||
|
||||
image_set = []
|
||||
for f in os.listdir("iut"):
|
||||
if f.endswith(".jpg"):
|
||||
file_name = os.path.join("iut", f)
|
||||
image_set.append([cv2.imread(file_name), file_name])
|
||||
|
||||
|
||||
import random
|
||||
|
||||
for i in range(len(image_set)):
|
||||
if random.randint(1,20) == 1:
|
||||
do_test = True
|
||||
else:
|
||||
do_test = False
|
||||
|
||||
if (do_test):
|
||||
image = image_set[i][0]
|
||||
file_name = image_set[i][1]
|
||||
print(file_name)
|
||||
|
||||
re_file_name = file_name.replace(".jpg","_re.jpg").replace('iut/', 'iut_re/')
|
||||
# 色彩转换，转换为灰度图像
|
||||
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
|
||||
|
||||
# 调用函数 detectMultiScale
|
||||
faces = faceCascade.detectMultiScale(gray, scaleFactor=1.15, minNeighbors=5, minSize=(5, 5))
|
||||
|
||||
# print(faces)
|
||||
# 打印输出的测试结果
|
||||
# print("found {0} face in {1}".format(len(faces), os.path.basename(file_name)))
|
||||
# print((re_file_name, len(faces)))
|
||||
|
||||
total_test_run += 1
|
||||
result = os.path.basename(file_name).replace('.jpg','')
|
||||
|
||||
if (int(result) > 100 and int(result) < 200):
|
||||
count_good_result_base += 1
|
||||
if (len(faces) > 0):
|
||||
count_good_result += 1
|
||||
else:
|
||||
count_bad_result_base +=1
|
||||
if (len(faces) > 0):
|
||||
count_bad_result += 1
|
||||
|
||||
if (int(result) >= 0 and int(result) < 100):
|
||||
bad_result_African += 1
|
||||
elif (int(result) >= 100 and int(result) < 200):
|
||||
bad_result_Beach += 1
|
||||
elif (int(result) >= 200 and int(result) < 300):
|
||||
bad_result_Building += 1
|
||||
elif (int(result) >= 300 and int(result) < 400):
|
||||
bad_result_Bus += 1
|
||||
elif (int(result) >= 400 and int(result) < 500):
|
||||
bad_result_Dinosaur += 1
|
||||
elif (int(result) >= 500 and int(result) < 600):
|
||||
bad_result_Elephant += 1
|
||||
elif (int(result) >= 600 and int(result) < 700):
|
||||
bad_result_Flower += 1
|
||||
elif (int(result) >= 700 and int(result) < 800):
|
||||
bad_result_Horse += 1
|
||||
elif (int(result) >= 800 and int(result) < 900):
|
||||
bad_result_Mountain += 1
|
||||
elif (int(result) >= 900 and int(result) < 1000):
|
||||
bad_result_Dish += 1
|
||||
|
||||
|
||||
# 逐个标注人脸
|
||||
for x, y, w, h in faces:
|
||||
cv2.rectangle(image, (x, y), (x + w, y + w), (0, 255, 0), 2) # 矩形标注
|
||||
# cv2.circle(image,(int((x+x+w)/2),int((y+y+h)/2)),int(w/2),(0,255,0),2)
|
||||
|
||||
if (len(faces) > 0):
|
||||
# 显示结果
|
||||
# cv2.imshow("dect", image)
|
||||
# 保存检测结果
|
||||
cv2.imwrite(re_file_name, image)
|
||||
# cv2.waitKey(0)
|
||||
# cv2.destroyAllWindows()
|
||||
|
||||
print("count_good_result : "+ str(count_good_result))
|
||||
print('count_good_result_base : '+ str(count_good_result_base))
|
||||
print('count_bad_result : '+ str(count_bad_result))
|
||||
print('count_bad_result_base : '+ str(count_bad_result_base))
|
||||
|
||||
print('bad result drill down')
|
||||
print('')
|
||||
print('bad_result_African : '+ str(bad_result_African))
|
||||
print('bad_result_Beach : '+ str(bad_result_Beach))
|
||||
print('bad_result_Building : '+ str(bad_result_Building))
|
||||
print('bad_result_Bus : '+ str(bad_result_Bus))
|
||||
print('bad_result_Dinosaur : '+ str(bad_result_Dinosaur))
|
||||
print('bad_result_Dish : '+ str(bad_result_Dish))
|
||||
print('bad_result_Elephant : '+ str(bad_result_Elephant))
|
||||
print('bad_result_Flower : '+ str(bad_result_Flower))
|
||||
print('bad_result_Horse : '+ str(bad_result_Horse))
|
||||
print('bad_result_Mountain : '+ str(bad_result_Mountain))
|
||||
|
||||
print('total test run (good base + bad base) : '+ str(total_test_run))
|
||||
@@ -0,0 +1,5 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
set -ex
|
||||
|
||||
python ./sanity_test.py
|
||||
@@ -0,0 +1,120 @@
|
||||
import cv2
|
||||
import os,sys
|
||||
|
||||
count_good_result = 0
|
||||
count_good_result_base = 0
|
||||
count_bad_result = 0
|
||||
count_bad_result_base = 0
|
||||
|
||||
total_test_run = 0
|
||||
|
||||
bad_result_African = 0
|
||||
bad_result_Beach = 0
|
||||
bad_result_Building = 0
|
||||
bad_result_Bus = 0
|
||||
bad_result_Dinosaur = 0
|
||||
bad_result_Dish = 0
|
||||
bad_result_Elephant = 0
|
||||
bad_result_Flower = 0
|
||||
bad_result_Horse = 0
|
||||
bad_result_Mountain = 0
|
||||
|
||||
# 获取 XML 文件，加载人脸检测器
|
||||
faceCascade = cv2.CascadeClassifier("cascade.xml")
|
||||
|
||||
# remove all jpg files in iut_re folder
|
||||
for f in os.listdir("iut_re"):
|
||||
if f.endswith(".jpg"):
|
||||
os.remove(os.path.join("iut_re", f))
|
||||
|
||||
image_set = []
|
||||
for f in os.listdir("iut"):
|
||||
if f.endswith(".jpg"):
|
||||
file_name = os.path.join("iut", f)
|
||||
image_set.append([cv2.imread(file_name), file_name])
|
||||
|
||||
|
||||
for i in range(len(image_set)):
|
||||
image = image_set[i][0]
|
||||
file_name = image_set[i][1]
|
||||
print(file_name)
|
||||
|
||||
re_file_name = file_name.replace(".jpg","_re.jpg").replace('iut/', 'iut_re/')
|
||||
# 色彩转换，转换为灰度图像
|
||||
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
|
||||
|
||||
# 调用函数 detectMultiScale
|
||||
faces = faceCascade.detectMultiScale(gray, scaleFactor=1.15, minNeighbors=5, minSize=(5, 5))
|
||||
|
||||
# print(faces)
|
||||
# 打印输出的测试结果
|
||||
# print("found {0} face in {1}".format(len(faces), os.path.basename(file_name)))
|
||||
# print((re_file_name, len(faces)))
|
||||
|
||||
total_test_run += 1
|
||||
result = os.path.basename(file_name).replace('.jpg','')
|
||||
|
||||
if (int(result) > 100 and int(result) < 200):
|
||||
count_good_result_base += 1
|
||||
if (len(faces) > 0):
|
||||
count_good_result += 1
|
||||
else:
|
||||
count_bad_result_base +=1
|
||||
if (int(result) >= 0 and int(result) < 100):
|
||||
bad_result_African += 1
|
||||
elif (int(result) >= 100 and int(result) < 200):
|
||||
bad_result_Beach += 1
|
||||
elif (int(result) >= 200 and int(result) < 300):
|
||||
bad_result_Building += 1
|
||||
elif (int(result) >= 300 and int(result) < 400):
|
||||
bad_result_Bus += 1
|
||||
elif (int(result) >= 400 and int(result) < 500):
|
||||
bad_result_Dinosaur += 1
|
||||
elif (int(result) >= 500 and int(result) < 600):
|
||||
bad_result_Elephant += 1
|
||||
elif (int(result) >= 600 and int(result) < 700):
|
||||
bad_result_Flower += 1
|
||||
elif (int(result) >= 700 and int(result) < 800):
|
||||
bad_result_Horse += 1
|
||||
elif (int(result) >= 800 and int(result) < 900):
|
||||
bad_result_Mountain += 1
|
||||
elif (int(result) >= 900 and int(result) < 1000):
|
||||
bad_result_Dish += 1
|
||||
|
||||
|
||||
if (len(faces) > 0):
|
||||
count_bad_result += 1
|
||||
|
||||
|
||||
# 逐个标注人脸
|
||||
for x, y, w, h in faces:
|
||||
cv2.rectangle(image, (x, y), (x + w, y + w), (0, 255, 0), 2) # 矩形标注
|
||||
# cv2.circle(image,(int((x+x+w)/2),int((y+y+h)/2)),int(w/2),(0,255,0),2)
|
||||
|
||||
if (len(faces) > 0):
|
||||
# 显示结果
|
||||
# cv2.imshow("dect", image)
|
||||
# 保存检测结果
|
||||
cv2.imwrite(re_file_name, image)
|
||||
# cv2.waitKey(0)
|
||||
# cv2.destroyAllWindows()
|
||||
|
||||
print("count_good_result : "+ str(count_good_result))
|
||||
print('count_good_result_base : '+ str(count_good_result_base))
|
||||
print('count_bad_result : '+ str(count_bad_result))
|
||||
print('count_bad_result_base : '+ str(count_bad_result_base))
|
||||
|
||||
print('bad result drill down')
|
||||
print('')
|
||||
print('bad_result_African : '+ str(bad_result_African))
|
||||
print('bad_result_Beach : '+ str(bad_result_Beach))
|
||||
print('bad_result_Building : '+ str(bad_result_Building))
|
||||
print('bad_result_Bus : '+ str(bad_result_Bus))
|
||||
print('bad_result_Dinosaur : '+ str(bad_result_Dinosaur))
|
||||
print('bad_result_Dish : '+ str(bad_result_Dish))
|
||||
print('bad_result_Elephant : '+ str(bad_result_Elephant))
|
||||
print('bad_result_Flower : '+ str(bad_result_Flower))
|
||||
print('bad_result_Horse : '+ str(bad_result_Horse))
|
||||
print('bad_result_Mountain : '+ str(bad_result_Mountain))
|
||||
|
||||
print('total test run : '+ str(total_test_run))
|
||||
@@ -0,0 +1,5 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
set -ex
|
||||
|
||||
python ./test.py
|
||||
@@ -0,0 +1,5 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
set -ex
|
||||
|
||||
npx nodemon --ext xml --exec "python ./test.py"
|
||||
@@ -0,0 +1,544 @@
|
||||
<?xml version="1.0"?>
|
||||
<opencv_storage>
|
||||
<cascade>
|
||||
<stageType>BOOST</stageType>
|
||||
<featureType>HAAR</featureType>
|
||||
<height>20</height>
|
||||
<width>20</width>
|
||||
<stageParams>
|
||||
<boostType>GAB</boostType>
|
||||
<minHitRate>9.9500000476837158e-01</minHitRate>
|
||||
<maxFalseAlarm>5.0000000000000000e-01</maxFalseAlarm>
|
||||
<weightTrimRate>9.4999999999999996e-01</weightTrimRate>
|
||||
<maxDepth>1</maxDepth>
|
||||
<maxWeakCount>100</maxWeakCount></stageParams>
|
||||
<featureParams>
|
||||
<maxCatCount>0</maxCatCount>
|
||||
<featSize>1</featSize>
|
||||
<mode>ALL</mode></featureParams>
|
||||
<stageNum>12</stageNum>
|
||||
<stages>
|
||||
<!-- stage 0 -->
|
||||
<_>
|
||||
<maxWeakCount>2</maxWeakCount>
|
||||
<stageThreshold>-2.0792216528207064e-03</stageThreshold>
|
||||
<weakClassifiers>
|
||||
<_>
|
||||
<internalNodes>
|
||||
0 -1 23 3.5195697098970413e-02</internalNodes>
|
||||
<leafValues>
|
||||
-9.7000002861022949e-01 9.5833331346511841e-01</leafValues></_>
|
||||
<_>
|
||||
<internalNodes>
|
||||
0 -1 32 4.9420498311519623e-02</internalNodes>
|
||||
<leafValues>
|
||||
-9.3253821134567261e-01 9.6792078018188477e-01</leafValues></_></weakClassifiers></_>
|
||||
<!-- stage 1 -->
|
||||
<_>
|
||||
<maxWeakCount>3</maxWeakCount>
|
||||
<stageThreshold>-1.2283716201782227e+00</stageThreshold>
|
||||
<weakClassifiers>
|
||||
<_>
|
||||
<internalNodes>
|
||||
0 -1 6 2.1136376261711121e-01</internalNodes>
|
||||
<leafValues>
|
||||
-8.8292682170867920e-01 7.6744186878204346e-01</leafValues></_>
|
||||
<_>
|
||||
<internalNodes>
|
||||
0 -1 27 -3.6786209791898727e-02</internalNodes>
|
||||
<leafValues>
|
||||
6.4631646871566772e-01 -8.5865759849548340e-01</leafValues></_>
|
||||
<_>
|
||||
<internalNodes>
|
||||
0 -1 2 8.2553006708621979e-02</internalNodes>
|
||||
<leafValues>
|
||||
-9.6414572000503540e-01 5.1321285963058472e-01</leafValues></_></weakClassifiers></_>
|
||||
<!-- stage 2 -->
|
||||
<_>
|
||||
<maxWeakCount>2</maxWeakCount>
|
||||
<stageThreshold>-5.2522403001785278e-01</stageThreshold>
|
||||
<weakClassifiers>
|
||||
<_>
|
||||
<internalNodes>
|
||||
0 -1 34 -7.0488560013473034e-03</internalNodes>
|
||||
<leafValues>
|
||||
4.6428570151329041e-01 -9.0625000000000000e-01</leafValues></_>
|
||||
<_>
|
||||
<internalNodes>
|
||||
0 -1 1 2.0252991467714310e-02</internalNodes>
|
||||
<leafValues>
|
||||
-9.2222303152084351e-01 3.8102594017982483e-01</leafValues></_></weakClassifiers></_>
|
||||
<!-- stage 3 -->
|
||||
<_>
|
||||
<maxWeakCount>3</maxWeakCount>
|
||||
<stageThreshold>-1.2258527278900146e+00</stageThreshold>
|
||||
<weakClassifiers>
|
||||
<_>
|
||||
<internalNodes>
|
||||
0 -1 35 2.7621425688266754e-02</internalNodes>
|
||||
<leafValues>
|
||||
-9.3296086788177490e-01 2.7536231279373169e-01</leafValues></_>
|
||||
<_>
|
||||
<internalNodes>
|
||||
0 -1 18 2.4644609540700912e-02</internalNodes>
|
||||
<leafValues>
|
||||
-7.8042775392532349e-01 5.6246817111968994e-01</leafValues></_>
|
||||
<_>
|
||||
<internalNodes>
|
||||
0 -1 28 4.4026337563991547e-02</internalNodes>
|
||||
<leafValues>
|
||||
-7.2078734636306763e-01 5.9029269218444824e-01</leafValues></_></weakClassifiers></_>
|
||||
<!-- stage 4 -->
|
||||
<_>
|
||||
<maxWeakCount>3</maxWeakCount>
|
||||
<stageThreshold>-1.1094360351562500e+00</stageThreshold>
|
||||
<weakClassifiers>
|
||||
<_>
|
||||
<internalNodes>
|
||||
0 -1 13 5.6299664080142975e-02</internalNodes>
|
||||
<leafValues>
|
||||
-8.5567009449005127e-01 3.3333334326744080e-01</leafValues></_>
|
||||
<_>
|
||||
<internalNodes>
|
||||
0 -1 10 2.0412717014551163e-02</internalNodes>
|
||||
<leafValues>
|
||||
-6.9705963134765625e-01 6.2128168344497681e-01</leafValues></_>
|
||||
<_>
|
||||
<internalNodes>
|
||||
0 -1 26 3.3632911741733551e-02</internalNodes>
|
||||
<leafValues>
|
||||
-7.4570971727371216e-01 6.2287092208862305e-01</leafValues></_></weakClassifiers></_>
|
||||
<!-- stage 5 -->
|
||||
<_>
|
||||
<maxWeakCount>4</maxWeakCount>
|
||||
<stageThreshold>-1.0068651437759399e+00</stageThreshold>
|
||||
<weakClassifiers>
|
||||
<_>
|
||||
<internalNodes>
|
||||
0 -1 14 2.7963671088218689e-01</internalNodes>
|
||||
<leafValues>
|
||||
-8.6885243654251099e-01 1.6923077404499054e-01</leafValues></_>
|
||||
<_>
|
||||
<internalNodes>
|
||||
0 -1 25 1.2323195114731789e-02</internalNodes>
|
||||
<leafValues>
|
||||
-7.9252362251281738e-01 3.5135197639465332e-01</leafValues></_>
|
||||
<_>
|
||||
<internalNodes>
|
||||
0 -1 29 1.1619093269109726e-01</internalNodes>
|
||||
<leafValues>
|
||||
-8.5458588600158691e-01 4.3786722421646118e-01</leafValues></_>
|
||||
<_>
|
||||
<internalNodes>
|
||||
0 -1 16 1.1368356645107269e-02</internalNodes>
|
||||
<leafValues>
|
||||
-8.9688044786453247e-01 3.6522126197814941e-01</leafValues></_></weakClassifiers></_>
|
||||
<!-- stage 6 -->
|
||||
<_>
|
||||
<maxWeakCount>3</maxWeakCount>
|
||||
<stageThreshold>-9.4377774000167847e-01</stageThreshold>
|
||||
<weakClassifiers>
|
||||
<_>
|
||||
<internalNodes>
|
||||
0 -1 17 2.8343785554170609e-02</internalNodes>
|
||||
<leafValues>
|
||||
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<!-- stage 2 -->
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||||
<_>
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||||
<_>
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<!-- stage 5 -->
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||||
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<!-- stage 6 -->
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||||
<_>
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<!-- stage 7 -->
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||||
<_>
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||||
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<!-- stage 8 -->
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||||
<_>
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<!-- stage 9 -->
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||||
<_>
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||||
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<!-- stage 10 -->
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||||
<_>
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||||
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<!-- stage 11 -->
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||||
<_>
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||||
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||||
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<!-- stage 12 -->
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||||
<_>
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<!-- stage 13 -->
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||||
<_>
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<!-- stage 14 -->
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<tilted>0</tilted></_></features></cascade>
|
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</opencv_storage>
|
||||
@@ -0,0 +1,41 @@
|
||||
import cv2
|
||||
import os,sys
|
||||
|
||||
# 获取 XML 文件，加载人脸检测器
|
||||
faceCascade = cv2.CascadeClassifier("cascade.xml")
|
||||
|
||||
|
||||
# 读取待检测的图像
|
||||
image_set = []
|
||||
for i in range(1,12+1):
|
||||
file_name = "iut/{}.jpg".format(i)
|
||||
image_set.append([cv2.imread(file_name), file_name])
|
||||
|
||||
for i in range(len(image_set)):
|
||||
image = image_set[i][0]
|
||||
file_name = image_set[i][1]
|
||||
re_file_name = file_name.replace(".jpg","_re.jpg").replace('iut/', 'iut_re/')
|
||||
# 色彩转换，转换为灰度图像
|
||||
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
|
||||
|
||||
# 调用函数 detectMultiScale
|
||||
faces = faceCascade.detectMultiScale(gray, scaleFactor=1.15, minNeighbors=5, minSize=(5, 5))
|
||||
|
||||
# print(faces)
|
||||
# 打印输出的测试结果
|
||||
# print("found {0} face in {1}".format(len(faces), os.path.basename(file_name)))
|
||||
print((re_file_name, len(faces)))
|
||||
|
||||
# 逐个标注人脸
|
||||
for x, y, w, h in faces:
|
||||
cv2.rectangle(image, (x, y), (x + w, y + w), (0, 255, 0), 2) # 矩形标注
|
||||
# cv2.circle(image,(int((x+x+w)/2),int((y+y+h)/2)),int(w/2),(0,255,0),2)
|
||||
|
||||
# 显示结果
|
||||
cv2.imshow("dect", image)
|
||||
# 保存检测结果
|
||||
cv2.imwrite(re_file_name, image)
|
||||
# cv2.waitKey(0)
|
||||
cv2.destroyAllWindows()
|
||||
|
||||
|
||||
@@ -0,0 +1,5 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
set -ex
|
||||
|
||||
npx nodemon --ext xml --exec "python ./test.py"
|
||||
@@ -0,0 +1,544 @@
|
||||
<?xml version="1.0"?>
|
||||
<opencv_storage>
|
||||
<cascade>
|
||||
<stageType>BOOST</stageType>
|
||||
<featureType>HAAR</featureType>
|
||||
<height>20</height>
|
||||
<width>20</width>
|
||||
<stageParams>
|
||||
<boostType>GAB</boostType>
|
||||
<minHitRate>9.9500000476837158e-01</minHitRate>
|
||||
<maxFalseAlarm>5.0000000000000000e-01</maxFalseAlarm>
|
||||
<weightTrimRate>9.4999999999999996e-01</weightTrimRate>
|
||||
<maxDepth>1</maxDepth>
|
||||
<maxWeakCount>100</maxWeakCount></stageParams>
|
||||
<featureParams>
|
||||
<maxCatCount>0</maxCatCount>
|
||||
<featSize>1</featSize>
|
||||
<mode>ALL</mode></featureParams>
|
||||
<stageNum>12</stageNum>
|
||||
<stages>
|
||||
<!-- stage 0 -->
|
||||
<_>
|
||||
<maxWeakCount>2</maxWeakCount>
|
||||
<stageThreshold>-2.0792216528207064e-03</stageThreshold>
|
||||
<weakClassifiers>
|
||||
<_>
|
||||
<internalNodes>
|
||||
0 -1 23 3.5195697098970413e-02</internalNodes>
|
||||
<leafValues>
|
||||
-9.7000002861022949e-01 9.5833331346511841e-01</leafValues></_>
|
||||
<_>
|
||||
<internalNodes>
|
||||
0 -1 32 4.9420498311519623e-02</internalNodes>
|
||||
<leafValues>
|
||||
-9.3253821134567261e-01 9.6792078018188477e-01</leafValues></_></weakClassifiers></_>
|
||||
<!-- stage 1 -->
|
||||
<_>
|
||||
<maxWeakCount>3</maxWeakCount>
|
||||
<stageThreshold>-1.2283716201782227e+00</stageThreshold>
|
||||
<weakClassifiers>
|
||||
<_>
|
||||
<internalNodes>
|
||||
0 -1 6 2.1136376261711121e-01</internalNodes>
|
||||
<leafValues>
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<_>
|
||||
18 9 2 4 -1.</_>
|
||||
<_>
|
||||
18 9 1 2 2.</_>
|
||||
<_>
|
||||
19 11 1 2 2.</_></rects>
|
||||
<tilted>0</tilted></_>
|
||||
<_>
|
||||
<rects>
|
||||
<_>
|
||||
19 15 1 3 -1.</_>
|
||||
<_>
|
||||
19 16 1 1 3.</_></rects>
|
||||
<tilted>0</tilted></_></features></cascade>
|
||||
</opencv_storage>
|
||||
@@ -0,0 +1,29 @@
|
||||
import cv2
|
||||
|
||||
# 读取待检测的图像
|
||||
image = cv2.imread("12.jpg")
|
||||
|
||||
# 获取 XML 文件，加载人脸检测器
|
||||
faceCascade = cv2.CascadeClassifier("cascade.xml")
|
||||
|
||||
# 色彩转换，转换为灰度图像
|
||||
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
|
||||
|
||||
# 调用函数 detectMultiScale
|
||||
faces = faceCascade.detectMultiScale(gray, scaleFactor=1.15, minNeighbors=5, minSize=(5, 5))
|
||||
|
||||
# print(faces)
|
||||
# 打印输出的测试结果
|
||||
print("发现{0}个人脸!".format(len(faces)))
|
||||
|
||||
# 逐个标注人脸
|
||||
for x, y, w, h in faces:
|
||||
cv2.rectangle(image, (x, y), (x + w, y + w), (0, 255, 0), 2) # 矩形标注
|
||||
# cv2.circle(image,(int((x+x+w)/2),int((y+y+h)/2)),int(w/2),(0,255,0),2)
|
||||
|
||||
# 显示结果
|
||||
cv2.imshow("dect", image)
|
||||
# 保存检测结果
|
||||
cv2.imwrite("re.jpg", image)
|
||||
# cv2.waitKey(0)
|
||||
cv2.destroyAllWindows()
|
||||
@@ -0,0 +1,5 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
set -ex
|
||||
|
||||
npx nodemon --ext xml --exec "python ./face_test.py"
|
||||
Reference in New Issue
Block a user