def __init__(self, res, path, size=util.MAX_THUMBNAIL_SIZE):
super(Face, self).__init__()
self.path = path
img = util.rotate_image(path)
self.bmp = img.ConvertToBitmap()
self.name = None
if res.get('faceId'):
self.id = res['faceId']
if res.get('persistedFaceId'):
self.persisted_id = res['persistedFaceId']
if res.get('faceRectangle'):
self.rect = Rect(res['faceRectangle'])
self.bmp = self.bmp.GetSubBitmap(
wx.Rect(
self.rect.left,
self.rect.top,
self.rect.width,
self.rect.height,
))
if res.get('faceAttributes'):
self.attr = Attribute(res['faceAttributes'])
self.bmp = util.scale_image(
self.bmp.ConvertToImage(),
size=size,
).ConvertToBitmap()
def render(self, card_dims, text):
""" Generate a transparent PIL card layer with the text on it """
# If the user has set a max width, respect that.
# If not, we use the edge of the card.
if (self.rotation == 0):
maxwidth, maxheight = util.aligned_maxdims((self.x, self.y),
(self.width, self.height),
card_dims,
self.x_align,
self.y_align)
else:
# Due to rotation of the text, we don't fully take the card's edges into account.
maxdim = max(card_dims)
maxwidth = min(self.width, maxdim)
maxheight = min(self.height, maxdim)
# Split the text into lines, in a way that fits our width
lines = [text]
if (self.wordwrap):
lines = wrap_pixel_width(text, maxwidth, self.font, linesep='\\n')
if (lines is None):
sys.stderr.write("Warning: Unable to wrap text label \"%s\"\n" % text)
return None
# Render the text, one line at a time
label = render_lines(lines,
font=self.font,
color=self.color,
justify=self.justify,
spacing=self.spacing)
if (label.width > maxwidth):
sys.stderr.write("Warning: Text label overflows max width: \"%s\"\n" % text)
return None
if (label.height > maxheight):
sys.stderr.write("Warning: Text label overflows max height: \"%s\"\n" % text)
return None
if (self.rotation != 0):
label = util.rotate_image(label, self.rotation)
# Figure out where to place the top-left corner of the label
x,y = util.alignment_to_absolute((self.x, self.y), label.size, self.x_align, self.y_align)
if (x < 0 or y < 0 or
x + label.width > card_dims[0] or
y + label.height > card_dims[1]):
sys.stderr.write("Warning: Text label overflows card boundary: \"%s\"\n" % text)
return None
image = Image.new("RGBA", card_dims, (0,0,0,0))
image.paste(label, (x,y), mask=label)
return image
def set_path(self, path):
"""Set the image path."""
img = util.rotate_image(path)
width = img.GetWidth()
img = util.scale_image(img, size=self.size)
new_width = img.GetWidth()
self.scale = 1.0 * new_width / width
self.bmp = img.ConvertToBitmap()
self.bitmap.SetBitmap(self.bmp)
self.sizer.Layout()
def set_path(self, path):
"""Set the image path."""
img = util.rotate_image(path)
width = img.GetWidth()
img = util.scale_image(img, size=self.size)
new_width = img.GetWidth()
self.scale = 1.0 * new_width / width
self.bmp = img.ConvertToBitmap()
self.bitmap.SetBitmap(self.bmp)
self.sizer.Layout()
def stitch(directory,
n_x: int,
n_y: int,
x_size: int,
y_size: int,
rotation: float,
filter: int = 0,
unsharp=0):
out = zeros((n_x * x_size, n_y * y_size, 3), dtype=uint8)
# Filenames don't start at zero, picXXXX.jpg
filenames = [
filename for filename in os.listdir(directory)
if filename.endswith(".jpg")
]
filename_offset = int(sorted(filenames)[0][3:7])
layout = layouter(n_x, n_y, True, 1)
for i in range(n_x):
for j in range(n_y):
#a = n_x - i - 1
a = i
#b = n_y - j - 1
b = j
fname = directory + ("/pic%04i.jpg" %
(layout(a, b) + filename_offset))
#print("Reading '%s'"%fname)
image = cv.imread(fname)
if filter != 0:
image = cv.GaussianBlur(image,
(2 * filter + 1, 2 * filter + 1),
filter)
#if unsharp != 0:
# blurred = cv.GaussianBlur(image, (2*unsharp+1, 2*unsharp+1), unsharp)
# image = cv.subtract(image, blurred)
image = rotate_image(image, rotation)
centre_image = get_centre(image, x_size, y_size)
out[i * x_size:(i + 1) * x_size,
j * y_size:(j + 1) * y_size, :] = centre_image
return out
def test_rotate_image(image_path, item_size=(256, 256), scale=0.2, item_num=6):
image_list = util.load_images(image_path)
image = util.build_image(image_list, item_size, scale, item_num)
pixels = image.reshape(-1, image.shape[2])
unique_elements, counts_elements = np.unique(pixels, axis=0, return_counts=True)
counts_elements = np.sort(counts_elements)[::-1]
angles = [0, 90, 180, 270]
while True:
angle = angles[np.random.randint(0, len(angles))]
rot_image = np.clip(image * 255, 0, 255).astype(np.uint8)
rot_image = util.rotate_image(rot_image, angle)
util.draw_text(rot_image, 'angle: %d' % angle, color=(255, 0, 0))
cv2.imshow('image', rot_image)
key = cv2.waitKey(1000000) & 0xFF
if key == 27: # 'esc
break
def render(self, card_dims, image):
""" Generate a transparent PIL card layer with the image on it """
# Since images aren't wrapped, we simply accept the user's scaling
# settings or (if they are 0), use the image's own dimensions.
# If the image falls outside the card boundaries, we warn but allow it.
w, h = image.size
aspect = w/h
if (self.width == 0 and self.height == 0):
# No scaling, use image as-is
pass
else:
scalew,scaleh = self.width, self.height
if (scalew == 0):
# Proportional scaling to given height
scalew = round(scaleh * aspect)
if (scaleh == 0):
scaleh = round(scalew / aspect)
image = image.resize((scalew, scaleh), Image.ANTIALIAS)
if (self.rotation != 0):
image = util.rotate_image(image, self.rotation)
# Figure out where to place the top-left corner of the label
x,y = util.alignment_to_absolute((self.x, self.y), image.size, self.x_align, self.y_align)
if (x < 0 or y < 0 or
x + image.width > card_dims[0] or
y + image.height > card_dims[1]):
sys.stderr.write("Warning: Image label overflows card boundary")
card = Image.new("RGBA", card_dims, (0,0,0,0))
card.paste(image, (x,y), mask=image)
return card
def __init__(self, res, path, size=util.MAX_THUMBNAIL_SIZE):
super(Face, self).__init__()
self.path = path
img = util.rotate_image(path)
self.bmp = img.ConvertToBitmap()
self.name = None
if res.get('faceId'):
self.id = res['faceId']
if res.get('persistedFaceId'):
self.persisted_id = res['persistedFaceId']
if res.get('faceRectangle'):
self.rect = Rect(res['faceRectangle'])
self.bmp = self.bmp.GetSubBitmap(wx.Rect(
self.rect.left,
self.rect.top,
self.rect.width,
self.rect.height,
))
if res.get('faceAttributes'):
self.attr = Attribute(res['faceAttributes'])
self.bmp = util.scale_image(
self.bmp.ConvertToImage(),
size=size,
).ConvertToBitmap()
for filepath in glob.glob(os.path.join(map_images_dir, 'D*')):
filename = filepath.split('/')[-1]
if test_only:
if filename.split('.')[0] not in test_filenames:
continue
print(filepath)
map_img = cv2.imread(filepath)
original_shape = map_img.shape
preds = []
confs = []
for angle in angles:
rot_img, rot_mat, bounds = rotate_image(map_img, angle, original_shape)
height = rot_img.shape[0]
width = rot_img.shape[1]
current_x = 0
current_y = 0
while current_y + crop_h < height:
while current_x + crop_w < width:
crop_img = rot_img[current_y:current_y + crop_h,
current_x:current_x + crop_w]
if do_preprocess:
crop_img = preprocess(crop_img, (512, 512))
model_output = model.predict(np.array([crop_img]),
# print os.path.join(work_dir, mat_name)
print os.path.join(data_dir, im_name)
im = cv2.imread(os.path.join(data_dir, im_name))
#im, translation = adjust_image_size(im, padding_amount=500)
translation = (0, 0)
#print(im.shape)
all_boxes, all_scores, all_rotations = [], [], []
for angle in range(0, 95, 5):
#print("Running detection at angle: " + str(angle))
image_center = tuple(np.array((im.shape[0],im.shape[1]))/2)
R = cv2.getRotationMatrix2D(image_center, angle, scale=1.0)
#rot_img = cv2.warpAffine(im, R, (im.shape[0], im.shape[1]), flags=cv2.INTER_LINEAR)
rot_img, rot_mat, bounds = rotate_image(im, angle, im.shape)
# # Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
# scores, boxes = im_detect(net, im)
scores, boxes = im_detect_sliding_crop(net, rot_img, crop_h, crop_w, step)
print(np.max(scores))
print("Boxes for angle " + str(angle) + ": " + str(boxes.shape[0]))
Rinv = cv2.getRotationMatrix2D(image_center, -angle, scale=1.0)
all_boxes.append(boxes)
all_scores.append(scores)
all_rotations.append( {'angle':angle, 'center':image_center, 'R':R, 'Rinv': Rinv} )
def tbpp_raw_generate_data(map_images_dir,
image_paths,
regions,
batch_size,
prior_util,
encode=True,
do_rotate=False,
do_preprocess=False):
crop_h = 512
crop_w = 512
step = 400
angles = range(-90, 95, 5) if do_rotate else [0]
inputs, targets = [], []
mean = np.array([104, 117, 123])
idxs = np.arange(len(image_paths))
np.random.shuffle(idxs)
for _, i in enumerate(idxs):
filepath = os.path.join(map_images_dir, image_paths[i])
map_img = cv2.imread(filepath)
original_shape = map_img.shape
for angle in angles:
rot_img, rot_mat, _ = rotate_image(map_img, angle, original_shape)
height = rot_img.shape[0]
width = rot_img.shape[1]
current_x = 0
current_y = 0
while current_y + crop_h < height:
while current_x + crop_w < width:
crop_img = rot_img[current_y:current_y + crop_h,
current_x:current_x + crop_w]
if do_preprocess:
crop_img = preprocess(crop_img, (512, 512))
crop_boxes = []
for region in regions:
# rotate to orientation when image is not rotated
image_center = (original_shape[1] // 2,
original_shape[0] // 2)
rot_mat = cv2.getRotationMatrix2D(image_center,
angle,
scale=1.0)
# add col for rotation
region = np.concatenate(
[region, np.ones([region.shape[0], 1])], axis=1)
# rotate
transformed_points = rot_mat.dot(region.T).T
pt1 = [
int(transformed_points[0][0]),
int(transformed_points[0][1])
]
pt2 = [
int(transformed_points[1][0]),
int(transformed_points[1][1])
]
pt3 = [
int(transformed_points[2][0]),
int(transformed_points[2][1])
]
pt4 = [
int(transformed_points[3][0]),
int(transformed_points[3][1])
]
region = np.array([pt1, pt2, pt3, pt4])
xmin = np.min(region[:, 0])
xmax = np.max(region[:, 0])
ymin = np.min(region[:, 1])
ymax = np.max(region[:, 1])
if xmin > current_x and xmax < (
current_x + crop_w) and ymin < (
current_y + crop_h) and ymax > current_y:
crop_xmin = xmin - current_x
crop_ymin = ymin - current_y
crop_xmax = xmax - current_x
crop_ymax = ymax - current_y
crop_boxes.append([
crop_xmin, crop_ymax, crop_xmax, crop_ymax,
crop_xmax, crop_ymin, crop_xmin, crop_ymin
])
crop_boxes = np.array(crop_boxes)
crop_boxes[:, 0::2] /= crop_img.shape[1]
crop_boxes[:, 1::2] /= crop_img.shape[0]
# append classes
crop_boxes = np.concatenate(
[crop_boxes,
np.ones([crop_boxes.shape[0], 1])],
axis=1)
crop_img -= mean[np.newaxis, np.newaxis, :]
#img = img / 25.6
inputs.append(crop_img)
targets.append(crop_boxes)
#if len(targets) == batch_size or j == len(idxs)-1: # last batch in epoch can be smaller then batch_size
if len(targets) == batch_size:
if encode:
targets = [prior_util.encode(y) for y in targets]
targets = np.array(targets, dtype=np.float32)
tmp_inputs = np.array(inputs, dtype=np.float32)
tmp_targets = np.array(targets, dtype=np.float32)
inputs, targets = [], []
yield tmp_inputs, tmp_targets
current_x += step
current_x = 0
current_y += step
print('NEW epoch')
print('EXIT generator')