def process_image(fn, cascade, extract_faces=True):
img = cv2.imread(fn)
h, w = img.shape[:2]
scale = max(h, w) / 512.0
small = cv2.resize(img, (int(w/scale), int(h/scale)), interpolation=cv2.INTER_AREA)
rects = detect_turned(small, cascade)
for i, (x1, y1, x2, y2, vx, vy) in enumerate(rects):
cv2.rectangle(small, (x1, y1), (x2, y2), (0, 255, 0))
cv2.circle(small, (x1, y1), 2, (0, 0, 255), -1)
cv2.putText(small, str(i), ((x1+x2)/2, (y1+y2)/2), cv2.FONT_HERSHEY_PLAIN, 1.0, (0, 255, 0))
rects = np.float32(rects).reshape(-1,6)
rects[:,:4] = np.around(rects[:,:4]*scale)
faces = []
if extract_faces:
path, name, ext = splitfn(fn)
face_sz = 256
for i, r in enumerate(rects):
p1, p2, u = r.reshape(3, 2)
v = np.float32( [-u[1], u[0]] )
w = np.abs(p2-p1).max()
fscale = w / face_sz
p0 = 0.5*(p1+p2 - w*(u+v))
M = np.float32([u*fscale, v*fscale, p0]).T
face = cv2.warpAffine(img, M, (face_sz, face_sz), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_AREA)
faces.append(face)
return small, rects, faces
def processImage(fn):
print('processing %s... ' % fn)
img = cv.imread(fn, 0)
if img is None:
print("Failed to load", fn)
return None
assert w == img.shape[1] and h == img.shape[0], ("size: %d x %d ... " % (img.shape[1], img.shape[0]))
found, corners = cv.findChessboardCorners(img, pattern_size)
if found:
term = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_COUNT, 30, 0.1)
cv.cornerSubPix(img, corners, (5, 5), (-1, -1), term)
if debug_dir:
vis = cv.cvtColor(img, cv.COLOR_GRAY2BGR)
cv.drawChessboardCorners(vis, pattern_size, corners, found)
_path, name, _ext = splitfn(fn)
outfile = os.path.join(debug_dir, name + '_chess.png')
cv.imwrite(outfile, vis)
if not found:
print('chessboard not found')
return None
print(' %s... OK' % fn)
return (corners.reshape(-1, 2), pattern_points)
def __init__(self):
root = tk.Tk()
root.title('OpenCV Demo')
self.win = win = tk.PanedWindow(root, orient=tk.HORIZONTAL, sashrelief=tk.RAISED, sashwidth=4)
self.win.pack(fill=tk.BOTH, expand=1)
left = tk.Frame(win)
right = tk.Frame(win)
win.add(left)
win.add(right)
scrollbar = tk.Scrollbar(left, orient=tk.VERTICAL)
self.demos_lb = demos_lb = tk.Listbox(left, yscrollcommand=scrollbar.set)
scrollbar.config(command=demos_lb.yview)
scrollbar.pack(side=tk.RIGHT, fill=tk.Y)
demos_lb.pack(side=tk.LEFT, fill=tk.BOTH, expand=1)
self.samples = {}
for fn in glob('*.py'):
name = splitfn(fn)[1]
if fn[0] != '_' and name not in exclude_list:
self.samples[name] = fn
for name in sorted(self.samples):
demos_lb.insert(tk.END, name)
demos_lb.bind('<<ListboxSelect>>', self.on_demo_select)
self.cmd_entry = cmd_entry = tk.Entry(right)
cmd_entry.bind('<Return>', self.on_run)
run_btn = tk.Button(right, command=self.on_run, text='Run', width=8)
self.text = text = ScrolledText(right, font=('arial', 12, 'normal'), width = 30, wrap='word')
self.linker = _linker = LinkManager(text, self.on_link)
self.text.tag_config("header1", font=('arial', 14, 'bold'))
self.text.tag_config("header2", font=('arial', 12, 'bold'))
text.config(state='disabled')
text.pack(fill='both', expand=1, side=tk.BOTTOM)
cmd_entry.pack(fill='x', side='left' , expand=1)
run_btn.pack()
def run(self):
for fn in self.img_names:
print('processing %s... ' % fn, end='')
img = cv2.imread(fn, 0)
if img is None:
print("Failed to load", fn)
continue
self.h, self.w = img.shape[:2]
found, corners = cv2.findChessboardCorners(img, self.pattern_size)
if found:
term = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_COUNT, 30, 0.1)
cv2.cornerSubPix(img, corners, (5, 5), (-1, -1), term)
if self.debug_dir:
vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
cv2.drawChessboardCorners(vis, self.pattern_size, corners, found)
path, name, ext = splitfn(fn)
outfile = self.debug_dir + name + '_chess.png'
cv2.imwrite(outfile, vis)
if found:
self.img_names_undistort.append(outfile)
if not found:
print('chessboard not found')
continue
self.img_points.append(corners.reshape(-1, 2))
self.obj_points.append(self.pattern_points)
print('ok')
# calculate camera distortion
rms, camera_matrix, dist_coefs, rvecs, tvecs = cv2.calibrateCamera(
self.obj_points, self.img_points, (self.w, self.h), None, None)
print(self.img_names_undistort)
print("\nRMS:", rms)
print("camera matrix:\n", camera_matrix)
print("distortion coefficients: ", dist_coefs.ravel())
return([self.img_names_undistort, camera_matrix, dist_coefs])
obj_points = []
img_points = []
h, w = 0, 0
for fn in img_names:
print 'processing %s...' % fn,
img = cv2.imread(fn, 0)
h, w = img.shape[:2]
found, corners = cv2.findChessboardCorners(img, pattern_size)
if found:
term = ( cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_COUNT, 30, 0.1 )
cv2.cornerSubPix(img, corners, (5, 5), (-1, -1), term)
if debug_dir:
vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
cv2.drawChessboardCorners(vis, pattern_size, corners, found)
path, name, ext = splitfn(fn)
cv2.imwrite('%s/%s_chess.bmp' % (debug_dir, name), vis)
if not found:
print 'chessboard not found'
continue
img_points.append(corners.reshape(-1, 2))
obj_points.append(pattern_points)
print 'ok'
# Draw and display the corners
cv2.drawChessboardCorners(img, (9,6), corners,found)
cv2.imshow('img',img)
print('processing %s... ' % fn, end='')
img = cv2.imread(os.path.join(proj_root, fn), 0)
if img is None:
print("Failed to load", fn)
continue
h, w = img.shape[:2]
found, corners = cv2.findChessboardCorners(img, pattern_size)
if found:
term = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_COUNT, 30, 0.1)
cv2.cornerSubPix(img, corners, (5, 5), (-1, -1), term)
if out:
vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
cv2.drawChessboardCorners(vis, pattern_size, corners, found)
path, name, ext = splitfn(fn)
outfile = os.path.join(out, name + '_chess.png')
cv2.imwrite(outfile, vis)
if found:
img_names_undistort.append(outfile)
if not found:
print('chessboard not found')
continue
img_points.append(corners.reshape(-1, 2))
obj_points.append(pattern_points)
print('ok')
# calculate camera distortion
def run():
trivim_dir= open(os.path.join(home,"Trivim.txt")).readline()
count=0
os.chdir(trivim_dir)
with open('camera_calibration\\value.txt', 'w') as myFile:
myFile.write("0")
f = open("camera_calibration\\path.txt","r")
#Read whole file into data
photoDir=f.read()
path=photoDir+"\\*.jpg"
print path
args, img_mask = getopt.getopt(sys.argv[1:], '', ['save=', 'debug=', 'square_size='])
args = dict(args)
try: img_mask = img_mask[0]
except: img_mask = path
img_names = glob(img_mask)
debug_dir = args.get('--debug')
square_size = float(args.get('--square_size', 1.0))
pattern_size = (9, 6)
pattern_points = np.zeros( (np.prod(pattern_size), 3), np.float32 )
pattern_points[:,:2] = np.indices(pattern_size).T.reshape(-1, 2)
pattern_points *= square_size
obj_points = []
img_points = []
h, w = 0, 0
width=3872
flag=False
for fn in img_names:
count=count+1
print count
with open('camera_calibration\\value.txt', 'w') as myFile:
myFile.write(str(count))
print 'processing %s...' % fn,
img = cv2.imread(fn, 0)
h, w = img.shape[:2]
found, corners = cv2.findChessboardCorners(img, pattern_size)
if found:
if flag==False:
inputFileName = os.path.join(photoDir, fn)
photoHandle = Image.open(inputFileName)
# get EXIF information as a dictionary
exif = _getExif(photoHandle)
if 'ExifImageWidth' in exif:
width = float(exif['ExifImageWidth'])
Flag=True
term = ( cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_COUNT, 30, 0.1 )
cv2.cornerSubPix(img, corners, (5, 5), (-1, -1), term)
if debug_dir:
vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
cv2.drawChessboardCorners(vis, pattern_size, corners, found)
path, name, ext = splitfn(fn)
cv2.imwrite('%s/%s_chess.bmp' % (debug_dir, name), vis)
if not found:
print 'chessboard not found'
continue
img_points.append(corners.reshape(-1, 2))
obj_points.append(pattern_points)
print 'ok'
try:
rms, camera_matrix, dist_coefs, rvecs, tvecs = cv2.calibrateCamera(obj_points, img_points, (w, h))
f = open(r"camera_calibration\sensor_value.txt","r")
#Read whole file into data
value=f.read()
value=value.split('-')
sensor_value=float(value[len(value)-1])
f.close()
focalPixels=camera_matrix[0][0]
focalLength=(focalPixels*sensor_value)/width;
with open('camera_calibration\\calib_temp.txt', 'w') as myFile:
myFile.write("Camera Name : "+str(value[0])+"\n")
myFile.write("Sensor Width : "+str(sensor_value)+"\n")
myFile.write("Root Mean Square(RMS) value : "+str(rms)+"\n")
myFile.write("Distortion Coefficients : ")
for log in dist_coefs.ravel():
myFile.write(str(log)+"\t")
myFile.write("\n"+"Camera Matrix : "+"\n")
for log in camera_matrix:
myFile.write(str(log)+"\n")
myFile.write("Focal Length(mm) : " + str( focalLength )+"\n")
with open('camera_calibration\\finish.txt', 'w') as myFile:
myFile.write("finish")
except:
with open('camera_calibration\\finish.txt', 'w') as myFile:
myFile.write("failed")
cv2.destroyAllWindows()
cv2.destroyAllWindows()
print('processing %s... ' % fn, end='')
img = cv2.imread(fn, 0)
if img is None:
print("Failed to load", fn)
continue
h, w = img.shape[:2]
found, corners = cv2.findChessboardCorners(img, pattern_size)
if found:
term = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_COUNT, 30, 0.1)
cv2.cornerSubPix(img, corners, (5, 5), (-1, -1), term)
if debug_dir:
vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
cv2.drawChessboardCorners(vis, pattern_size, corners, found)
path, name, ext = splitfn(fn)
outfile = debug_dir + name + '_chess.png'
cv2.imwrite(outfile, vis)
if found:
img_names_undistort.append(outfile)
if not found:
print('chessboard not found')
continue
img_points.append(corners.reshape(-1, 2))
obj_points.append(pattern_points)
print('ok')
# calculate camera distortion
print('processing %s... ' % fn, end='')
img = cv2.imread(os.path.join(proj_root, fn), 0)
if img is None:
print("Failed to load", fn)
continue
h, w = img.shape[:2]
found, corners = cv2.findChessboardCorners(img, pattern_size)
if found:
term = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_COUNT, 30, 0.1)
cv2.cornerSubPix(img, corners, (5, 5), (-1, -1), term)
if out:
vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
cv2.drawChessboardCorners(vis, pattern_size, corners, found)
path, name, ext = splitfn(fn)
outfile = os.path.join(out, name + '_chess.png')
cv2.imwrite(outfile, vis)
if found:
img_names_undistort.append(outfile)
if not found:
print('chessboard not found')
continue
img_points.append(corners.reshape(-1, 2))
obj_points.append(pattern_points)
# write to matrix to be used as input
# img_points_np = np.array(img_points)
# print(corners)
ori_points = corners.reshape(7, 9, 2)
def main():
import sys
import getopt
from glob import glob
args, img_mask = getopt.getopt(sys.argv[1:], '',
['debug=', 'square_size=', 'threads='])
args = dict(args)
args.setdefault('--debug', './output/')
args.setdefault('--square_size', 0.875)
args.setdefault('--threads', 4)
if not img_mask:
img_mask = "*.jpg" # default
else:
img_mask = img_mask[0]
img_names = glob(img_mask)
print(img_names)
debug_dir = args.get('--debug')
if debug_dir and not os.path.isdir(debug_dir):
os.mkdir(debug_dir)
square_size = float(args.get('--square_size'))
pattern_size = (9, 6)
pattern_points = np.zeros((np.prod(pattern_size), 3), np.float32)
pattern_points[:, :2] = np.indices(pattern_size).T.reshape(-1, 2)
pattern_points *= square_size
obj_points = []
img_points = []
h, w = cv.imread(img_names[0], cv.IMREAD_GRAYSCALE
).shape[:2] # TODO: use imquery call to retrieve results
def processImage(fn):
print('processing %s... ' % fn)
img = cv.imread(fn, 0)
if img is None:
print("Failed to load", fn)
return None
assert w == img.shape[1] and h == img.shape[0], (
"size: %d x %d ... " % (img.shape[1], img.shape[0]))
found, corners = cv.findChessboardCorners(img, pattern_size)
if found:
term = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_COUNT, 30, 0.1)
cv.cornerSubPix(img, corners, (5, 5), (-1, -1), term)
if debug_dir:
vis = cv.cvtColor(img, cv.COLOR_GRAY2BGR)
cv.drawChessboardCorners(vis, pattern_size, corners, found)
_path, name, _ext = splitfn(fn)
outfile = os.path.join(debug_dir, name + '_chess.png')
cv.imwrite(outfile, vis)
if not found:
print('chessboard not found')
return None
print(' %s... OK' % fn)
return (corners.reshape(-1, 2), pattern_points)
threads_num = int(args.get('--threads'))
if threads_num <= 1:
chessboards = [processImage(fn) for fn in img_names]
else:
print("Run with %d threads..." % threads_num)
from multiprocessing.dummy import Pool as ThreadPool
pool = ThreadPool(threads_num)
chessboards = pool.map(processImage, img_names)
chessboards = [x for x in chessboards if x is not None]
for (corners, pattern_points) in chessboards:
img_points.append(corners)
obj_points.append(pattern_points)
# calculate camera distortion
rms, camera_matrix, dist_coefs, _rvecs, _tvecs = cv.calibrateCamera(
obj_points, img_points, (w, h), None, None)
print("\nRMS:", rms)
print("camera matrix:\n", camera_matrix)
print("distortion coefficients: ", dist_coefs.ravel())
# undistort the image with the calibration
print('')
for fn in img_names if debug_dir else []:
_path, name, _ext = splitfn(fn)
img_found = os.path.join(debug_dir, name + '_chess.png')
outfile = os.path.join(debug_dir, name + '_undistorted.png')
img = cv.imread(img_found)
if img is None:
continue
h, w = img.shape[:2]
newcameramtx, roi = cv.getOptimalNewCameraMatrix(
camera_matrix, dist_coefs, (w, h), 1, (w, h))
dst = cv.undistort(img, camera_matrix, dist_coefs, None, newcameramtx)
# crop and save the image
x, y, w, h = roi
dst = dst[y:y + h, x:x + w]
print('Undistorted image written to: %s' % outfile)
cv.imwrite(outfile, dst)
print('Done')
def calib():
import sys, getopt
from glob import glob
args, img_mask = getopt.getopt(sys.argv[1:], '', ['save=', 'debug=', 'square_size='])
args = dict(args)
img_mask = 'calib1/left*.jpg'
img_names = glob(img_mask)
debug_dir = args.get('--debug')
square_size = float(args.get('--square_size', 1.0))
pattern_size = (9, 6)#10X7
pattern_points = np.zeros( (np.prod(pattern_size), 3), np.float32 )
pattern_points[:,:2] = np.indices(pattern_size).T.reshape(-1, 2)
pattern_points *= square_size
obj_points = []
img_points = []
h, w = 0, 0
print 'image names: ',img_names
for fn in img_names:
print 'processing %s...' % fn,
img = cv2.imread(fn, 0)
h, w = img.shape[:2]
found, corners = cv2.findChessboardCorners(img, pattern_size)
if found:
term = ( cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_COUNT, 30, 0.1 )
cv2.cornerSubPix(img, corners, (5, 5), (-1, -1), term)
vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
cv2.drawChessboardCorners(vis, pattern_size, corners, found)
path, name, ext = splitfn(fn)
#cv2.imshow('corners',vis)
#cv2.waitKey()
#cv2.destroyAllWindows()
if debug_dir:
vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
cv2.drawChessboardCorners(vis, pattern_size, corners, found)
path, name, ext = splitfn(fn)
cv2.imshow('corners',vis)
cv2.waitKey(25)
cv2.destroyAllWindows()
cv2.imwrite('%s/%s_chess.bmp' % (debug_dir, name), vis)
if not found:
print 'chessboard not found'
continue
img_points.append(corners.reshape(-1, 2))
obj_points.append(pattern_points)
print 'ok'
rms, camera_matrix, dist_coefs, rvecs, tvecs = cv2.calibrateCamera(obj_points, img_points, (w, h),None,None)
print 'dist coefs: ',dist_coefs,len(dist_coefs),len(dist_coefs[0])
raw_input()
print w/2-0.5,h/2-0.5
# axis = np.float32([[3,0,0],[0,3,0],[0,0,-3]]).reshape(-1,3);
axis = np.float32([[0,0,0], [0,3,0], [3,3,0], [3,0,0],[0,0,-3],[0,3,-3],[3,3,-3],[3,0,-3] ])
for fn in img_names:
print 'processing %s...' % fn,
img = cv2.imread(fn, 0)
h, w = img.shape[:2]
found, corners = cv2.findChessboardCorners(img, pattern_size)
print 'corners: ',len(corners),len(corners[0])
if found:
term = ( cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_COUNT, 30, 0.1 )
cv2.cornerSubPix(img, corners, (5, 5), (-1, -1), term)
pattern_points = np.zeros((w*h, 1, 3), np.float32)
pattern_points[:,:,:2] = np.mgrid[0:w, 0:h].T.reshape(-1,1,2)
# print 'patt points: ',len(pattern_points),len(pattern_points[0])
# print 'corners points: ',len(corners),len(corners[0])
# raw_input()
# rv,tv,inliers = cv2.solvePnPRansac(pattern_points,corners,camera_matrix,dist_coefs)
# img_points,jac = cv2.projectPoints(axis,rv,tv,camera_matrix,dist_coefs)
# img = draw(img,corners,img_points)
if debug_dir:
vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
cv2.drawChessboardCorners(vis, pattern_size, corners, found)
path, name, ext = splitfn(fn)
cv2.imwrite('%s/%s_chess.bmp' % (debug_dir, name), vis)
if not found:
print 'chessboard not found'
continue
# img_points.append(corners.reshape(-1, 2))
# obj_points.append(pattern_points)
print 'ok'
return camera_matrix,dist_coefs
def run():
trivim_dir = open(os.path.join(home, "Trivim.txt")).readline()
count = 0
os.chdir(trivim_dir)
with open('camera_calibration\\value.txt', 'w') as myFile:
myFile.write("0")
f = open("camera_calibration\\path.txt", "r")
#Read whole file into data
photoDir = f.read()
path = photoDir + "\\*.jpg"
print path
args, img_mask = getopt.getopt(sys.argv[1:], '',
['save=', 'debug=', 'square_size='])
args = dict(args)
try:
img_mask = img_mask[0]
except:
img_mask = path
img_names = glob(img_mask)
debug_dir = args.get('--debug')
square_size = float(args.get('--square_size', 1.0))
pattern_size = (9, 6)
pattern_points = np.zeros((np.prod(pattern_size), 3), np.float32)
pattern_points[:, :2] = np.indices(pattern_size).T.reshape(-1, 2)
pattern_points *= square_size
obj_points = []
img_points = []
h, w = 0, 0
width = 3872
flag = False
for fn in img_names:
count = count + 1
print count
with open('camera_calibration\\value.txt', 'w') as myFile:
myFile.write(str(count))
print 'processing %s...' % fn,
img = cv2.imread(fn, 0)
h, w = img.shape[:2]
found, corners = cv2.findChessboardCorners(img, pattern_size)
if found:
if flag == False:
inputFileName = os.path.join(photoDir, fn)
photoHandle = Image.open(inputFileName)
# get EXIF information as a dictionary
exif = _getExif(photoHandle)
if 'ExifImageWidth' in exif:
width = float(exif['ExifImageWidth'])
Flag = True
term = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_COUNT, 30, 0.1)
cv2.cornerSubPix(img, corners, (5, 5), (-1, -1), term)
if debug_dir:
vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
cv2.drawChessboardCorners(vis, pattern_size, corners, found)
path, name, ext = splitfn(fn)
cv2.imwrite('%s/%s_chess.bmp' % (debug_dir, name), vis)
if not found:
print 'chessboard not found'
continue
img_points.append(corners.reshape(-1, 2))
obj_points.append(pattern_points)
print 'ok'
try:
rms, camera_matrix, dist_coefs, rvecs, tvecs = cv2.calibrateCamera(
obj_points, img_points, (w, h))
f = open(r"camera_calibration\sensor_value.txt", "r")
#Read whole file into data
value = f.read()
value = value.split('-')
sensor_value = float(value[len(value) - 1])
f.close()
focalPixels = camera_matrix[0][0]
focalLength = (focalPixels * sensor_value) / width
with open('camera_calibration\\calib_temp.txt', 'w') as myFile:
myFile.write("Camera Name : " + str(value[0]) + "\n")
myFile.write("Sensor Width : " + str(sensor_value) + "\n")
myFile.write("Root Mean Square(RMS) value : " + str(rms) + "\n")
myFile.write("Distortion Coefficients : ")
for log in dist_coefs.ravel():
myFile.write(str(log) + "\t")
myFile.write("\n" + "Camera Matrix : " + "\n")
for log in camera_matrix:
myFile.write(str(log) + "\n")
myFile.write("Focal Length(mm) : " + str(focalLength) + "\n")
with open('camera_calibration\\finish.txt', 'w') as myFile:
myFile.write("finish")
except:
with open('camera_calibration\\finish.txt', 'w') as myFile:
myFile.write("failed")
cv2.destroyAllWindows()
cv2.destroyAllWindows()
import getopt
from glob import glob
from common import splitfn, image_extensions
args, img_args = getopt.getopt(sys.argv[1:], '', ['cascade=', 'outdir='])
args = dict(args)
cascade_fn = args.get('--cascade', "../../data/haarcascades/haarcascade_frontalface_alt.xml")
outdir = args.get('--outdir')
img_list = []
if len(img_args) == 0:
img_list = ['../cpp/lena.jpg']
else:
for mask in img_args:
img_list.extend(glob(mask))
img_list = [fn for fn in img_list if splitfn(fn)[-1].lower() in image_extensions]
cascade = cv2.CascadeClassifier(cascade_fn)
for i, fn in enumerate(img_list):
print '%d / %d %s' % (i+1, len(img_list), fn),
vis, rects, faces = process_image(fn, cascade)
if len(faces) > 0 and outdir is not None:
path, name, ext = splitfn(fn)
cv2.imwrite('%s/%s_all.bmp' % (outdir, name), vis)
for face_i, face in enumerate(faces):
cv2.imwrite('%s/%s_obj%02d.bmp' % (outdir, name, face_i), face)
print ' - %d object(s) found' % len(faces)
cv2.imshow('img', vis)
cv2.waitKey(50)
cv2.waitKey()
def calibrate(board_width, board_height, square_size, rectified_dir, debug_dir, save_file, image_patterns):
import glob
from common import splitfn
img_names = glob.glob(image_patterns)
pattern_size = (board_width, board_height)
pattern_points = np.zeros((np.prod(pattern_size), 3), np.float32)
pattern_points[:, :2] = np.indices(pattern_size).T.reshape(-1, 2)
pattern_points *= square_size
if debug_dir:
if not os.path.exists(debug_dir):
os.makedirs(debug_dir)
if rectified_dir:
if not os.path.exists(rectified_dir):
os.makedirs(rectified_dir)
obj_points = []
img_points = []
good_images = []
h, w = 0, 0
for fn in img_names:
print 'processing %s...' % fn,
img = cv2.imread(fn, 0)
h, w = img.shape[:2]
found, corners = cv2.findChessboardCorners(img, pattern_size,
flags=cv2.CALIB_CB_ADAPTIVE_THRESH | cv2.CALIB_CB_NORMALIZE_IMAGE)
if found:
term = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_COUNT, 30, 0.1)
cv2.cornerSubPix(img, corners, (5, 5), (-1, -1), term)
if debug_dir:
vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
cv2.drawChessboardCorners(vis, pattern_size, corners, found)
path, name, ext = splitfn(fn)
cv2.imwrite('%s/%s_chess.png' % (debug_dir, name), vis)
if not found:
print 'chessboard not found in %s' % (fn, )
continue
img_points.append(corners.reshape(-1, 2))
obj_points.append(pattern_points)
good_images.append(fn)
print 'ok'
rms, camera_matrix, dist_coefs, rvecs, tvecs = cv2.calibrateCamera(obj_points, img_points, (w, h))
print "RMS:", rms
print "camera matrix:\n", camera_matrix
print "distortion coefficients: ", dist_coefs.ravel()
if save_file:
with open(save_file, 'wt') as f:
m = camera_matrix
v = [m[0][0], m[1][1], m[0][2], m[1][2]] + dist_coefs[0].tolist()
f.write(' '.join(map(str, v)))
f.write('\n')
f.write(' '.join(map(str, [w, h])))
if rectified_dir:
# new_camera_matrix, valid_roi = cv2.getOptimalNewCameraMatrix(camera_matrix, dist_coefs, (w, h),
# alpha=0.55, centerPrincipalPoint=False)
# print "new camera matrix:\n", new_camera_matrix
# if new_camera_matrix[0, 0] < 0:
# new_camera_matrix[0, 0] *= -1
# map1, map2 = cv2.initUndistortRectifyMap(camera_matrix, dist_coefs, None,
# new_camera_matrix, (w, h),
# m1type=cv2.CV_16SC2)
# for i in xrange(len(good_images)):
# fn = good_images[i]
# img = cv2.imread(fn, 0)
# r_img = cv2.remap(img, map1,map2, cv2.INTER_LINEAR, borderMode=cv2.BORDER_TRANSPARENT)
# _, name, _ = splitfn(fn)
# cv2.imwrite('%s/%s_rectified.png' % (rectified_dir, name), r_img)
for i in xrange(len(good_images)):
fn = good_images[i]
img = cv2.imread(fn, 0)
_, name, _ = splitfn(fn)
r_img2 = cv2.undistort(img, camera_matrix, dist_coefs)
cv2.imwrite('%s/%s_rectified2.png' % (rectified_dir, name), r_img2)
cv2.destroyAllWindows()
def calibrateCamera(DIR):
args, img_mask = getopt.getopt(sys.argv[1:], '', ['debug=', 'square_size='])
args = dict(args)
args.setdefault('--debug', './output/')
args.setdefault('--square_size', 1.0)
if not img_mask:
img_mask =str(DIR + 'image*.jpg') # default
else:
img_mask = img_mask[0]
img_names = glob(img_mask)
debug_dir = args.get('--debug')
if not os.path.isdir(debug_dir):
os.mkdir(debug_dir)
square_size = float(args.get('--square_size'))
pattern_size = (9, 6)
pattern_points = np.zeros((np.prod(pattern_size), 3), np.float32)
pattern_points[:, :2] = np.indices(pattern_size).T.reshape(-1, 2)
pattern_points *= square_size
obj_points = []
img_points = []
h, w = 0, 0
img_names_undistort = []
for fn in img_names:
print('processing %s... ' % fn)#, end='')
img = cv2.imread(fn, 0)
if img is None:
print("Failed to load", fn)
continue
h, w = img.shape[:2]
found, corners = cv2.findChessboardCorners(img, pattern_size)
if found:
term = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_COUNT, 30, 0.1)
cv2.cornerSubPix(img, corners, (5, 5), (-1, -1), term)
if debug_dir:
vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
cv2.drawChessboardCorners(vis, pattern_size, corners, found)
path, name, ext = splitfn(fn)
outfile = debug_dir + name + '_chess.png'
cv2.imwrite(outfile, vis)
if found:
img_names_undistort.append(outfile)
if not found:
print('chessboard not found')
continue
img_points.append(corners.reshape(-1, 2))
obj_points.append(pattern_points)
print('ok')
# calculate camera distortion
rms, camera_matrix, dist_coefs, rvecs, tvecs = cv2.calibrateCamera(obj_points, img_points, (w, h), None, None)
print("\nRMS:", rms)
print("camera matrix:\n", camera_matrix)
print("distortion coefficients: ", dist_coefs.ravel())
# undistort the image with the calibration
print('')
for img_found in img_names_undistort:
img = cv2.imread(img_found)
h, w = img.shape[:2]
newcameramtx, roi = cv2.getOptimalNewCameraMatrix(camera_matrix, dist_coefs, (w, h), 1, (w, h))
dst = cv2.undistort(img, camera_matrix, dist_coefs, None, newcameramtx)
# crop and save the image
x, y, w, h = roi
dst = dst[y:y+h, x:x+w]
outfile = img_found + '_undistorted.png'
print('Undistorted image written to: %s' % outfile)
cv2.imwrite(outfile, dst)
cv2.destroyAllWindows()
return camera_matrix, dist_coefs, (w,h), obj_points, img_points
