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louiscklaw
2025-02-01 01:19:51 +08:00
commit 3b0b154910
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3487
task1/_lab/007-train-bus-test/test_case/beach_test/cascade.xml Normal file
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task1/_lab/007-train-bus-test/test_case/beach_test/cascade_best_beach.xml Normal file
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task1/_lab/007-train-bus-test/test_case/beach_test/cascade_working_example.xml Normal file
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<?xml version="1.0"?>
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<stageType>BOOST</stageType>
<featureType>HAAR</featureType>
<height>20</height>
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<minHitRate>9.9500000476837158e-01</minHitRate>
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<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>
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task1/_lab/007-train-bus-test/test_case/beach_test/sanity_test.py Normal file
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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 i % 2 == 0:
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))

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task1/_lab/007-train-bus-test/test_case/beach_test/sanity_test.sh Executable file
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#!/usr/bin/env bash
set -ex
python ./sanity_test.py

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task1/_lab/007-train-bus-test/test_case/beach_test/sanity_test_result.log (Stored with Git LFS) Normal file
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task1/_lab/007-train-bus-test/test_case/beach_test/test.py Normal file
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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))

5
task1/_lab/007-train-bus-test/test_case/beach_test/test.sh Executable file
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#!/usr/bin/env bash
set -ex
python ./test.py

5
task1/_lab/007-train-bus-test/test_case/beach_test/test_w.sh Executable file
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#!/usr/bin/env bash
set -ex
npx nodemon --ext xml --exec "python ./test.py"