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haar_cascade_steps.py
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haar_cascade_steps.py
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import numpy as np
import cv2
import os
import sys
import subprocess
#---------------------------Documentation------------------------------
# http://answers.opencv.org/question/4368/traincascade-error-bad-argument-can-not-get-new-positive-sample-the-most-possible-reason-is-insufficient-count-of-samples-in-given-vec-file/#4474
# https://stackoverflow.com/questions/10863560/haar-training-opencv-assertion-failed
# http://www.answers.opencv.org/question/7141/about-traincascade-paremeters-samples-and-other/
# http://answers.opencv.org/question/7141/about-traincascade-paremeters-samples-and-other/
# https://stackoverflow.com/questions/16058080/how-to-train-cascade-properly/16058254
# http://docs.opencv.org/trunk/dc/d88/tutorial_traincascade.html
# http://answers.opencv.org/question/22964/opencv_traincascade-negative-samples-training-method/
# http://www.computer-vision-software.com/blog/2009/11/faq-opencv-haartraining/
# http://answers.opencv.org/question/39160/opencv_traincascade-parameters-explanation-image-sizes-etc/
#----------------------------------------------------------------------
reload(sys)
sys.setdefaultencoding('utf8')
POS_FOLDER = sys.argv[1]
NEG_FOLDER = "neg"
DATA_FOLDER = "data"
BG_TXT_FILE = "bg.txt"
POS_TXT_FILE = "pos.txt"
VECTOR_FILE = "vector.vec"
PROCESS_POS_IMAGES = True
POS_SIZE = (25, 45)
NUM_OF_POS = 20
NUMPOS = int(NUM_OF_POS*0.80) # int((num_in_vec - numNeg)/(1 + (numStages-1)*(1-minHitRate))*0.95)
NUMNEG = 4000 # The total number of negative(background) images
MEMORY_SIZE = str(1024*12)
NUM_STAGES = 12
MIN_HIT_RATE = 0.999
MAX_FALSE_ALARM_RATE = 0.4
def resize_background():
for img in os.listdir(NEG_FOLDER):
_, file_extension = os.path.splitext(img)
file_extension = file_extension.lower()
if file_extension.endswith("jpg") or file_extension.endswith("png"):
try:
# cv2.IMREAD_GRAYSCALE
img_read = cv2.imread("%s/%s" % (NEG_FOLDER, img))
# Resize
resized_image = cv2.resize(img_read, (150, 100))
cv2.imwrite("%s/%s" % (NEG_FOLDER, img), resized_image)
except Exception as e:
print(str(e))
def process_pos_image_and_get_pos_txt():
if not os.path.exists(POS_TXT_FILE):
with open(POS_TXT_FILE, 'a') as pos_txt:
for img_file in os.listdir(POS_FOLDER):
_, file_extension = os.path.splitext(img_file)
file_extension = file_extension.lower()
if file_extension.endswith("jpg") or file_extension.endswith("png"):
try:
# Read gray scale
image_gray = cv2.imread("%s/%s" % (POS_FOLDER, img_file), cv2.IMREAD_GRAYSCALE)
# Write line to pos.txt: object in position x: 0, y: 0, w: 100, h: 150
image_shape = image_gray.shape
line = "%s/%s 1 0 0 %d %d\n" % (POS_FOLDER, img_file, image_shape[1], image_shape[0])
pos_txt.write(line)
### Image Processing: filter
if PROCESS_POS_IMAGES:
image_gray_blurred = cv2.bilateralFilter(image_gray, 5, 10, 10)
cv2.imwrite("%s/%s" % (POS_FOLDER, img_file), image_gray_blurred)
except Exception as e:
print(str(e))
else:
print("Warning: %s already exists!!" % POS_TXT_FILE)
def create_neg():
### create single bg.txt
if not os.path.exists(BG_TXT_FILE):
for img_file in os.listdir(NEG_FOLDER):
_, file_extension = os.path.splitext(img_file)
file_extension = file_extension.lower()
if file_extension.endswith("jpg") or file_extension.endswith("png"):
line = "%s/%s\n" % (NEG_FOLDER, img_file)
with open(BG_TXT_FILE, 'a') as f:
f.write(line)
else:
print("Warning: %s already exists!!" % BG_TXT_FILE)
def get_vector_file():
if not os.path.exists(VECTOR_FILE):
subprocess.check_call(["opencv_createsamples",
"-info", POS_TXT_FILE,
"-vec", VECTOR_FILE,
"-num", str(NUM_OF_POS),
"-w", str(POS_SIZE[0]),
"-h", str(POS_SIZE[1])])
else:
print("Warning: %s already exists!!" % VECTOR_FILE)
def train_haar_cascade():
if not os.path.exists(DATA_FOLDER):
os.makedirs(DATA_FOLDER)
# opencv_traincascade -data data -vec cropped.vec -bg bg.txt -numPos 1220 -numNeg 767 -numStages 12 -minHitRate 0.999 -maxFalseAlarmRate 0.5 -w 48 -h 48
subprocess.check_call(["opencv_traincascade",
"-data", DATA_FOLDER,
"-vec", VECTOR_FILE,
"-bg", BG_TXT_FILE,
"-numPos", str(NUMPOS),
"-numNeg", str(NUMNEG),
"-numStages", str(NUM_STAGES),
"-precalcValBufSize", MEMORY_SIZE,
"-precalcIdxBufSize", MEMORY_SIZE,
"-minHitRate", str(MIN_HIT_RATE),
"-maxFalseAlarmRate", str(MAX_FALSE_ALARM_RATE),
"-w", str(POS_SIZE[0]),
"-h", str(POS_SIZE[1])])
if __name__ == '__main__':
resize_background()
create_neg()
process_pos_image_and_get_pos_txt()
get_vector_file()
# train_haar_cascade()