def cropFromFloat(x):
x = x * 255
x = np.asarray(x, dtype='uint8')
# y = x.reshape(SMALL_SIZE)
y = facedetection.cropFace(x, rescaleForReconigtion=1)
if y is None:
return None
return y.reshape(SMALL_SIZE)
def cropFromFloat(x):
x = x * 255
x = np.asarray(x, dtype='uint8')
# y = x.reshape(SMALL_SIZE)
y = facedetection.cropFace(x, rescaleForReconigtion=1)
if y is None:
return None
return y.reshape(SMALL_SIZE)
def readCropEqualize(path, extension, crop, doRecognition, equalize=False,
isColoured=False):
if not crop and doRecognition:
raise Exception("you asked for the reading process to crop the images but do no face detection")
if equalize:
dirforres = "detection-cropped-equalized"
else:
dirforres = "detection-cropped"
pathForCropped = os.path.join(path, dirforres)
if crop:
if doRecognition:
if not os.path.exists(pathForCropped):
os.makedirs(pathForCropped)
imageFiles = [(os.path.join(dirpath, f), f)
for dirpath, dirnames, files in os.walk(path)
for f in fnmatch.filter(files, '*.' + extension)]
images = []
for fullPath, shortPath in imageFiles:
# Do not do this for already cropped images
if pathForCropped in fullPath:
continue
print fullPath
print shortPath
# img = Image.open(fullPath)
# img = np.array(img.getdata()).reshape(img.size[0], img.size[1])
# print img.shape
img = cv2.imread(fullPath, 0)
print img == None
face = facedetection.cropFace(img)
if not face == None:
face = resize(face, SMALL_SIZE)
if equalize:
face = equalizeFromFloatCLAHE(face)
face = face.reshape(SMALL_SIZE)
# Only do the resizing once you are done with the cropping of the faces
# Check that you are always saving them in the right format
print "face.min"
print face.min()
print "face.max"
print face.max()
assert face.min() >=0 and face.max() <=1
images += [face.reshape(-1)]
# Save faces as files
croppedFileName = os.path.join(pathForCropped, shortPath)
io.imsave(croppedFileName, face)
# If not doing face detection live
else:
images = []
imageFiles = [os.path.join(dirpath, f)
for dirpath, dirnames, files in os.walk(pathForCropped)
for f in fnmatch.filter(files, '*.' + extension)]
for f in imageFiles:
img = cv2.imread(f, 0)
if type(img[0,0]) == np.uint8:
print "rescaling unit"
img = img / 255.0
img = resize(img, SMALL_SIZE)
images += [img.reshape(-1)]
# If not doing recognition here, just reading from the initial faces
else:
images = []
imageFiles = [os.path.join(dirpath, f)
for dirpath, dirnames, files in os.walk(path) if dirnames not in ["detection-cropped-equalized", "detection-cropped"]
for f in fnmatch.filter(files, '*.' + extension)]
for i in imageFiles:
assert not "detection-cropped" in imageFiles
for f in imageFiles:
img = cv2.imread(f, 0)
if type(img[0,0]) == np.uint8:
print "rescaling unit"
img = img / 255.0
img = resize(img, SMALL_SIZE)
if equalize:
img = equalizeFromFloatCLAHE(img)
images += [img.reshape(-1)]
assert len(images) != 0
print len(images)
return np.array(images)
def readCropEqualize(path,
extension,
crop,
doRecognition,
equalize=False,
isColoured=False):
if not crop and doRecognition:
raise Exception(
"you asked for the reading process to crop the images but do no face detection"
)
if equalize:
dirforres = "detection-cropped-equalized"
else:
dirforres = "detection-cropped"
pathForCropped = os.path.join(path, dirforres)
if crop:
if doRecognition:
if not os.path.exists(pathForCropped):
os.makedirs(pathForCropped)
imageFiles = [(os.path.join(dirpath, f), f)
for dirpath, dirnames, files in os.walk(path)
for f in fnmatch.filter(files, '*.' + extension)]
images = []
for fullPath, shortPath in imageFiles:
# Do not do this for already cropped images
if pathForCropped in fullPath:
continue
print fullPath
print shortPath
# img = Image.open(fullPath)
# img = np.array(img.getdata()).reshape(img.size[0], img.size[1])
# print img.shape
img = cv2.imread(fullPath, 0)
print img == None
face = facedetection.cropFace(img)
if not face == None:
face = resize(face, SMALL_SIZE)
if equalize:
face = equalizeFromFloatCLAHE(face)
face = face.reshape(SMALL_SIZE)
# Only do the resizing once you are done with the cropping of the faces
# Check that you are always saving them in the right format
print "face.min"
print face.min()
print "face.max"
print face.max()
assert face.min() >= 0 and face.max() <= 1
images += [face.reshape(-1)]
# Save faces as files
croppedFileName = os.path.join(pathForCropped, shortPath)
io.imsave(croppedFileName, face)
# If not doing face detection live
else:
images = []
imageFiles = [
os.path.join(dirpath, f)
for dirpath, dirnames, files in os.walk(pathForCropped)
for f in fnmatch.filter(files, '*.' + extension)
]
for f in imageFiles:
img = cv2.imread(f, 0)
if type(img[0, 0]) == np.uint8:
print "rescaling unit"
img = img / 255.0
img = resize(img, SMALL_SIZE)
images += [img.reshape(-1)]
# If not doing recognition here, just reading from the initial faces
else:
images = []
imageFiles = [
os.path.join(dirpath, f)
for dirpath, dirnames, files in os.walk(path) if dirnames not in
["detection-cropped-equalized", "detection-cropped"]
for f in fnmatch.filter(files, '*.' + extension)
]
for i in imageFiles:
assert not "detection-cropped" in imageFiles
for f in imageFiles:
img = cv2.imread(f, 0)
if type(img[0, 0]) == np.uint8:
print "rescaling unit"
img = img / 255.0
img = resize(img, SMALL_SIZE)
if equalize:
img = equalizeFromFloatCLAHE(img)
images += [img.reshape(-1)]
assert len(images) != 0
print len(images)
return np.array(images)