def draw_box(
out_img: np.ndarray,
box: Box,
labels: Iterable[Tuple[str, Point]],
color: Color = Color.red(),
line_thickness: int = 2,
) -> np.ndarray:
cv.rectangle(
img=out_img,
pt1=box.top_left,
pt2=box.bottom_right,
color=color.to_bgr(),
thickness=line_thickness,
)
for text, translation in labels:
text_loc: Point = translate_point(
Point(box.top_left_x, box.bottom_right_y), translation)
cv.putText(
img=out_img,
text=text,
org=text_loc,
fontFace=cv.FONT_HERSHEY_SIMPLEX,
fontScale=0.5,
color=Color.orange().to_bgr(),
thickness=2,
)
return out_img
def create_color(name):
"""Create color"""
color = Color(name=name)
db.session.add(color)
db.session.commit()
return color
def detect(
self,
img: np.ndarray,
out_img: np.ndarray = None,
color: Color = Color.yellow(),
line_thickness: int = 2,
) -> np.ndarray:
"""
Detect self.template in img and draw a box around it.
:param img: The image to detect the template in
:param out_img: The image to draw on (should be at least as large as img)
:param color: The color of the bounding box
:param line_thickness: The thickness of he bounding box line
:return: out_img
"""
# TODO: Make this scale invariant.
# see https://www.pyimagesearch.com/2015/01/26/multi-scale-template-matching-using-python-opencv/
if out_img is None:
out_img = img.copy()
else:
assert all(
img_dim <= out_img_dim
for img_dim, out_img_dim in zip(img.shape, out_img.shape))
similarity_map: np.ndarray = self._compute_similarity_map(img)
match_xy_indices: Iterable[Tuple[
int, int]] = self._get_match_xy_indices(similarity_map)
# For each match, draw the bounding box
for x, y in match_xy_indices:
top_left = x, y
bottom_right = x + self.temp_w, y + self.temp_h
cv.rectangle(
img=out_img,
pt1=top_left,
pt2=bottom_right,
color=color.to_bgr(),
thickness=line_thickness,
)
return out_img
def add_color_data(hex_code, color_name):
"""Assume hex_code is a 7-character string that's a hex code.
Add it to the database."""
color = Color(hex_code=hex_code.rstrip().lower(),
color_name=color_name.rstrip().lower())
db.session.add(color)
try:
db.session.commit()
except (Exception, exc.SQLAlchemyError, exc.InvalidRequestError, exc.IntegrityError) as e:
print(hex_code + '\n' + str(e))
def write_boxes(
out_path: str,
out_img: np.ndarray,
boxes: Iterable[Box],
labelss: Iterable[Iterable[Tuple[str, Point]]] = tuple(),
color: Color = Color.red(),
line_thickness: int = 2,
):
for box, labels in zip_longest(boxes, labelss):
draw_box(out_img, box, labels, color, line_thickness)
cv.imwrite(filename=out_path, img=out_img)
def load_colors():
"""Load colors/hex from css3 dict into database."""
print "Color"
for key, value in css3_hex_to_names.items():
color_hex, color_name = key, value
color = Color(color_hex=color_hex, color_name=color_name)
db.session.add(color)
db.session.commit()
def load_color():
"Load colors from colors into database"
print "Colors"
for row in open("seed_data/colors"):
color_name = row.rstrip()
color_name = Color(color=color_name)
db.session.add(color_name)
db.session.commit()
def __init__(self, view):
self.view = view
self.draw_mode = 'select'
self.viewable_size = 512
self.selected_shape = None
self.draw_color = Color(0.0, 0.0, 0.0, 1.0)
self.selected_draw_color = Color(0.0, 0.0, 0.0, 1.0)
self.view.update_color_indicator(*self.draw_color.rgba())
self.zoom_list = [.25, .5, 1.0, 2.0, 4.0]
self.zoom_list.sort(reverse=True)
self.zoom_level = 2
self.set_v_scrollbar_size(512)
self.set_h_scrollbar_size(512)
self.camera = Camera(2048, 2048)
self.threeD_mode = False
self.img_mode = False
self.t = None
self.waiters = []
self.current_image = None
for w in QApplication.topLevelWidgets():
if isinstance(w, QMainWindow):
self.main_window = w
break
def load_colors(color_filename):
"""Load colors from seed_data/generic_colors into database."""
# Delete all rows in table, so if we need to run this a second time,
# we won't be trying to add duplicate users
Color.query.delete()
#Read generic_colors file and insert data
for row in open(color_filename):
row = row.rstrip()
color_id, color = row.split("|")
colors = Color(color=color)
# We need to add to the session or it won't ever be stored
db.session.add(colors)
# Once we're done, we should commit our work
db.session.commit()
#finished the function
print("Colors inserted")
def track_faces(
self,
clip_dir: str,
out_base_dir: str,
draw_on_dir: str = None,
detect_only: bool = False,
):
# Setup
# load image paths
frames: List[os.DirEntry] = load_and_sort_dir(clip_dir)
draw_on_frames: List[os.DirEntry] = load_and_sort_dir(draw_on_dir)
assert len(draw_on_frames) in (0, len(frames))
# create output directory
out_dir: str = create_output_dir(out_base_dir)
# initialize variables required for object tracking
new_face_id: Iterator[int] = count(start=1)
tracked_faces: Dict[int, TrackedFace] = {}
# Iterate Through Video Frames
for frame, draw_on_frame in zip_longest(frames, draw_on_frames):
# load new frame
img = cv.imread(frame.path)
# load out_img
out_img: np.ndarray = (img.copy() if draw_on_frame is None else
cv.imread(draw_on_frame.path))
# ensure out_img is at least as large as img
assert len(img.shape) == len(out_img.shape) and all(
out_dim >= in_dim
for in_dim, out_dim in zip(img.shape, out_img.shape))
detected_face_boxes: List[Box] = self.detect_face_boxes(img)
# If tracking is disabled, draw the boxes and move to next frame
if detect_only:
write_boxes(
out_path=os.path.join(out_dir, frame.name),
out_img=out_img,
boxes=detected_face_boxes,
)
continue
detected_faces: List[GenderedFace] = gender_faces(
img=img,
faces=[
self.recognize_face(img, detected_face_box)
for detected_face_box in detected_face_boxes
],
)
current_face_ids: Set[int] = set()
lost_face_ids: Set[int] = set()
# Iterate over the known (tracked) faces
for tracked_face in tracked_faces.values():
matched_detected_faces: List[GenderedFace] = [
detected_face for detected_face in detected_faces
if self.faces_match(tracked_face, detected_face)
]
if not matched_detected_faces:
# Tracked face was not matched to and detected face
# Increment staleness since we didn't detect this face
tracked_face.staleness += 1
# Update tracker with img and get confidence
tracked_confidence: float = tracked_face.tracker.update(
img)
if (tracked_face.staleness < self.tracking_expiry
and tracked_confidence >= self.tracking_threshold):
# Assume face is still in frame but we failed to detect
# Update box with predicted location box
predicted_box: Box = Box.from_dlib_rect(
tracked_face.tracker.get_position())
tracked_face.box = predicted_box
current_face_ids.add(tracked_face.id_)
else:
# Assume face has left frame because either it is too stale or confidence is too low
if self.remember_identities:
# Set effectively infinite staleness to force tracker reset if face is found again later
tracked_face.staleness = sys.maxsize
else:
lost_face_ids.add(tracked_face.id_)
continue
# Tracked face was matched to one or more detected faces
# Multiple matches should rarely happen if faces in frame are distinct. We take closest to prev location
# TODO: Handle same person multiple times in frame
matched_detected_face = min(
matched_detected_faces,
key=lambda face: tracked_face.box.distance_to(face.box),
)
# Update tracked_face
tracked_face.descriptor = matched_detected_face.descriptor
tracked_face.shape = matched_detected_face.descriptor
tracked_face.box = matched_detected_face.box
if tracked_face.staleness >= self.tracking_expiry:
# Face was not present in last frame so reset tracker
tracked_face.tracker = dlib.correlation_tracker()
tracked_face.tracker.start_track(
image=img,
bounding_box=tracked_face.box.to_dlib_rect())
else:
# Face was present in last frame so just update guess
tracked_face.tracker.update(
image=img, guess=tracked_face.box.to_dlib_rect())
tracked_face.staleness = 0
tracked_face.gender = matched_detected_face.gender
tracked_face.gender_confidence = matched_detected_face.gender_confidence
# Add tracked_face to current_ids to reflect that it is in the frame
current_face_ids.add(tracked_face.id_)
# remove matched_detected_face from detected_faces
detected_faces.remove(matched_detected_face)
# Delete all faces that were being tracked but are now lost
# lost_face_ids will always be empty if self.remember_identities is True
for id_ in lost_face_ids:
del tracked_faces[id_]
for new_face in detected_faces:
# This is a new face (previously unseen)
id_ = next(new_face_id)
tracker: dlib.correlation_tracker = dlib.correlation_tracker()
tracker.start_track(image=img,
bounding_box=new_face.box.to_dlib_rect())
tracked_faces[id_] = TrackedFace(
box=new_face.box,
descriptor=new_face.descriptor,
shape=new_face.shape,
id_=id_,
tracker=tracker,
gender=new_face.gender,
gender_confidence=new_face.gender_confidence,
)
current_face_ids.add(id_)
write_boxes(
out_path=os.path.join(out_dir, frame.name),
out_img=out_img,
boxes=[tracked_faces[id_].box for id_ in current_face_ids],
labelss=[[
(
f'Person {id_}',
Point(3, 14),
),
(
f'{tracked_faces[id_].gender.name[0].upper()}: {round(100 * tracked_faces[id_].gender_confidence, 1)}%',
Point(3, 30),
),
] for id_ in current_face_ids],
color=Color.yellow(),
)
print(
f"Processed {frame.name}. Currently tracking {len(tracked_faces)} faces"
)
return out_dir
import cv2 as cv
import dlib
import numpy as np
from gender_clasification import gender_faces
from logo_detector import run_template_detector
from model import Color, Box, Point, Face, GenderedFace
from utils import create_output_dir, load_and_sort_dir, write_boxes
DETECTION_MODEL = "models/face_detection/res10_300x300_ssd_iter_140000.caffemodel"
DETECTION_MODEL_CONFIG = "models/face_detection/deploy.prototxt.txt"
RECOGNITION_MODEL = 'models/face_recognition/dlib_face_recognition_resnet_model_v1.dat'
SHAPE_MODEL = 'models/face_recognition/shape_predictor_5_face_landmarks.dat'
DETECTION_NETWORK_INPUT_SIZE = 300, 300
# Value from https://towardsdatascience.com/face-detection-models-which-to-use-and-why-d263e82c302c
BLOB_MEAN_SUBTRACTION: Color = Color(r=123, b=104, g=117)
"""
Challenge track robust to cuts
"""
@dataclass
class TrackedFace(GenderedFace):
id_: int = None
tracker: dlib.correlation_tracker = None
staleness: int = 0
class FaceTracker:
def __init__(
self,
class Controller():
def __init__(self, view):
self.view = view
self.draw_mode = 'select'
self.viewable_size = 512
self.selected_shape = None
self.draw_color = Color(0.0, 0.0, 0.0, 1.0)
self.selected_draw_color = Color(0.0, 0.0, 0.0, 1.0)
self.view.update_color_indicator(*self.draw_color.rgba())
self.zoom_list = [.25, .5, 1.0, 2.0, 4.0]
self.zoom_list.sort(reverse=True)
self.zoom_level = 2
self.set_v_scrollbar_size(512)
self.set_h_scrollbar_size(512)
self.camera = Camera(2048, 2048)
self.threeD_mode = False
self.img_mode = False
self.t = None
self.waiters = []
self.current_image = None
for w in QApplication.topLevelWidgets():
if isinstance(w, QMainWindow):
self.main_window = w
break
def close_down(self):
if self.t:
self.t.quit()
for w in self.waiters:
w.quit()
return True
def update_progress(self, v):
self.main_window.update_progress.emit(v)
def process_finished(self, img):
if img:
self.main_window.process_finished.emit(True)
self.view.draw_image(img)
else:
self.main_window.process_finished.emit(False)
self.view.draw_image()
def color_button_hit(self, r, g, b, a):
color = color = Color(r, g, b, a)
if self.selected_shape:
self.view.clear()
self.selected_draw_color = color
self.selected_shape.color = color
self.view.update_color_indicator(*self.selected_shape.color.rgba())
print 'selected color', self.selected_shape.color, id(self.selected_shape)
self.view.canvas.updateGL()
else:
self.draw_color = color
self.view.update_color_indicator(*self.draw_color.rgba())
print 'draw color', self.draw_color
def alpha_slider_changed(self, alpha):
if self.selected_shape:
self.selected_draw_color.a = alpha
self.color_button_hit(*self.selected_draw_color.rgba())
else:
self.draw_color.a = alpha
self.color_button_hit(*self.draw_color.rgba())
def triangle_button_hit(self):
self.view.clear_state()
self.draw_mode = 'triangle'
def square_button_hit(self):
self.view.clear_state()
self.draw_mode = 'square'
def rectangle_button_hit(self):
self.view.clear_state()
self.draw_mode = 'rectangle'
def circle_button_hit(self):
self.view.clear_state()
self.draw_mode = 'circle'
def ellipse_button_hit(self):
self.view.clear_state()
self.draw_mode = 'ellipse'
def line_button_hit(self):
self.view.clear_state()
self.draw_mode = 'line'
def select_button_hit(self):
self.view.clear_state()
self.draw_mode = 'select'
def zoomIn_button_hit(self):
if self.zoom_in():
self.update_zoom()
zoom_width = (512/self.zoom_amount())/2
newPos = min(self.h_scrollbar_position()+zoom_width, 2048)
self.set_h_scrollbar_position(newPos)
self.set_v_scrollbar_position(newPos)
def zoomOut_button_hit(self):
if self.zoom_out():
self.update_zoom()
zoom_width = (512/self.zoom_amount())/4
newPos = max(self.h_scrollbar_position()-zoom_width, 0)
self.set_h_scrollbar_position(newPos)
self.set_v_scrollbar_position(newPos)
def update_zoom(self):
self.view.viewport.set_scale(self.zoom_amount())
view_zoom = 512*1/self.zoom_amount()
self.set_h_maximum(2048-view_zoom)
self.set_v_maximum(2048-view_zoom)
self.set_v_scrollbar_size(view_zoom)
self.set_h_scrollbar_size(view_zoom)
def set_h_maximum(self, m):
self.view.parent().ui.horizontalScrollBar.setMaximum(m)
def set_v_maximum(self, m):
self.view.parent().ui.verticalScrollBar.setMaximum(m)
def h_scrollbar_changed(self, value):
self.view.viewport.set_offset(value, self.view.viewport.y)
self.view.draw()
self.view.canvas.updateGL()
def v_scrollbar_changed(self, value):
if self.img_mode:
self.view.viewport.set_offset(self.view.viewport.x,
self.view.parent().ui.horizontalScrollBar.maximum()-value)
else:
self.view.viewport.set_offset(self.view.viewport.x, value)
self.view.draw()
self.view.canvas.updateGL()
def toggle_3D_model_display(self):
self.threeD_mode = self.threeD_mode is False
if self.threeD_mode:
self.view.canvas.clear_color = (0, 0, 0, 0)
else:
self.view.canvas.clear_color = (1, 1, 1, 1)
self.view.draw()
self.view.canvas.updateGL()
def key_pressed(self, event):
if not self.threeD_mode:
return
key = event.key()
move_amount = .5
if key == Qt.Key_A:
self.camera.move_left(move_amount)
elif key == Qt.Key_D:
self.camera.move_right(move_amount)
elif key == Qt.Key_W:
self.camera.move_forward(move_amount)
elif key == Qt.Key_S:
self.camera.move_backward(move_amount)
elif key == Qt.Key_Q:
self.camera.turn_left(move_amount+2)
elif key == Qt.Key_E:
self.camera.turn_right(move_amount+2)
elif key == Qt.Key_R:
self.camera.move_up(move_amount)
elif key == Qt.Key_F:
self.camera.move_down(move_amount)
elif key == Qt.Key_H:
self.camera.reset()
self.camera.set_camera()
self.view.canvas.updateGL()
def do_edge_detection(self):
img = QImage(self.view.image.qimage.width(), self.view.image.qimage.height(), QImage.Format_ARGB32)
img_hor = QImage(self.view.image.qimage.width(), self.view.image.qimage.height(), QImage.Format_ARGB32)
img_ver = QImage(self.view.image.qimage.width(), self.view.image.qimage.height(), QImage.Format_ARGB32)
args = (img_hor, img_ver)
limg = QImage(self.view.image.qimage.width(), self.view.image.qimage.height(), QImage.Format_ARGB32)
# make luminence
self.apply_filter('Applying luminence for edge detect',
limg, self.luminance_filter)
self.apply_filter('Doing edge detect', img, self.edge_detect_filter,
args=args, read_image=limg)
def edge_detect_filter(self, read_image, x, y, img_hor, img_ver):
xconstants = [-1, 0, 1,
-2, 0, 1,
-1, 0, 1]
xr, xg, xb = self.spacial_filter(read_image, x, y, xconstants)
yconstants = [-1, -2, -1,
0, 0, 0,
1, 2, 1]
yr, yg, yb = self.spacial_filter(read_image, x, y, yconstants)
v = sqrt(xr**2 + yr**2)
return self.clip(v, v, v)
def luminance_filter(self, read_image, x, y):
c = self.pixel_at(read_image, x, y)
v = (c.red() + c.green() + c.blue())/3
return v, v, v
def do_sharpen(self):
constants = (0, -1, 0,
-1, 6, -1,
0, -1, 0)
args = (constants,)
kwargs = {'scale': 2}
img = QImage(self.view.image.qimage.width(), self.view.image.qimage.height(), QImage.Format_ARGB32)
self.main_window.track_progress('Doing sharpen', img.height())
self.apply_filter('Doing sharpen', img, self.spacial_filter, args=args, kwargs=kwargs)
def do_median_blur(self):
img = QImage(self.view.image.qimage.width(), self.view.image.qimage.height(), QImage.Format_ARGB32)
self.apply_filter('Doing median blur', img, self.median_filter)
def do_uniform_blur(self):
constants = (1, 1, 1,
1, 1, 1,
1, 1, 1)
args = (constants,)
kwargs = {'scale': 9}
img = QImage(self.view.image.qimage.width(), self.view.image.qimage.height(), QImage.Format_ARGB32)
self.apply_filter('Doing uniform blur', img, self.spacial_filter, args, kwargs)
def do_change_contrast(self, contrast_amount_num):
print 'changing contrast by', contrast_amount_num
img = QImage(self.view.image.qimage.width(), self.view.image.qimage.height(), QImage.Format_ARGB32)
args = (float(contrast_amount_num),)
self.apply_filter('Doing contrast change', img, self.contrast_operation, args, {})
def do_change_brightness(self, brightness_amount_num):
print 'changing brightness by', brightness_amount_num
constants = [0, 0, 0,
0, 1, 0,
0, 0, 0]
img = QImage(self.view.image.qimage.width(), self.view.image.qimage.height(), QImage.Format_ARGB32)
args = (constants,)
kwargs = {'offset': brightness_amount_num}
self.apply_filter('Doing change brightness', img, self.spacial_filter, args, kwargs)
def do_load_image(self, open_image):
self.view.image = Image(None, open_image, Point(1080, 1080), open_image.width(), open_image.height())
if self.img_mode:
self.view.draw_image(open_image)
def apply_filter(self, label, write_img, func, args=(), kwargs={}, read_image=None):
read_image = read_image or self.view.image.qimage
# print write_img, self.view.image.qimage, write_img == self.view.image.qimage
t = Filter_Thread(self, label, range(write_img.height()),
read_image, write_img, func, args, kwargs)
startnew = False
if self.t:
if self.t.isRunning():
w = Waiter(self, t)
self.main_window.process_finished.connect(w.wake_up)
self.waiters.append(w)
else:
startnew = True
else:
startnew = True
if startnew:
self.t = t
self.t.start()
return write_img
def spacial_filter(self, read_image, x, y, constants, scale=1, offset=0):
rtot = 0
gtot = 0
btot = 0
for i, e in enumerate(self.neighbors(read_image, x, y)):
if e:
rtot += e.red()*constants[i]
gtot += e.green()*constants[i]
btot += e.blue()*constants[i]
rtot = rtot/scale+offset
gtot = gtot/scale+offset
btot = btot/scale+offset
return self.clip(rtot, gtot, btot)
def median_filter(self, read_image, x, y):
r = []
g = []
b = []
for n in self.neighbors(read_image, x, y):
if n:
r.append(n.red())
g.append(n.green())
b.append(n.blue())
r.sort()
g.sort()
b.sort()
return r[len(r)/2], g[len(g)/2], b[len(b)/2]
def clip(self, rtot, gtot, btot):
rtot = max(0, rtot)
rtot = min(255, rtot)
gtot = max(0, gtot)
gtot = min(255, gtot)
btot = max(0, btot)
btot = min(255, btot)
return rtot, gtot, btot
def contrast_operation(self, read_image, x, y, c):
p = self.pixel_at(read_image, x, y)
r = ((c+100.0)/100.0)**4*(p.red()-128)+128
g = ((c+100.0)/100.0)**4*(p.green()-128)+128
b = ((c+100.0)/100.0)**4*(p.blue()-128)+128
return self.clip(r, g, b)
def neighbors(self, read_image, x, y):
nw = self.pixel_at(read_image, x-1, y+1)
nn = self.pixel_at(read_image, x, y+1)
ne = self.pixel_at(read_image, x+1, y+1)
ww = self.pixel_at(read_image, x-1, y)
cc = self.pixel_at(read_image, x, y)
ee = self.pixel_at(read_image, x+1, y)
sw = self.pixel_at(read_image, x-1, y-1)
ss = self.pixel_at(read_image, x, y-1)
se = self.pixel_at(read_image, x+1, y-1)
return nw, nn, ne, ww, cc, ee, sw, ss, se
def pixel_at(self, read_image, x, y):
# if self.view.image.qimage.valid(x, y):
if (x >= 0) and (x < self.view.image.fullw) and (y >= 0) and (y < self.view.image.fullh):
return QColor(read_image.pixel(x, y))
else:
return None
def toggle_background_display(self):
self.img_mode = self.img_mode is False
if self.img_mode:
self.view.canvas.hide()
self.view.draw_image()
else:
self.view.canvas.show()
self.view.canvas.updateGL()
# scroll bar interaction
def v_scrollbar_size(self):
return self.view.parent().ui.verticalScrollBar.pageStep()
def h_scrollbar_size(self):
return self.view.parent().ui.horizontalScrollBar.pageStep()
def v_scrollbar_position(self):
return self.view.parent().ui.verticalScrollBar.sliderPosition()
def h_scrollbar_position(self):
return self.view.parent().ui.horizontalScrollBar.sliderPosition()
def set_v_scrollbar_size(self, size):
self.view.parent().ui.verticalScrollBar.setPageStep(size)
def set_h_scrollbar_size(self, size):
self.view.parent().ui.horizontalScrollBar.setPageStep(size)
def set_v_scrollbar_position(self, position):
self.view.parent().ui.verticalScrollBar.setSliderPosition(position)
self.v_scrollbar_changed(position)
def set_h_scrollbar_position(self, position):
self.view.parent().ui.horizontalScrollBar.setSliderPosition(position)
self.h_scrollbar_changed(position)
def zoom_amount(self):
return self.zoom_list[self.zoom_level]
def zoom_in(self):
if self.zoom_level > 0:
self.zoom_level -= 1
return True
else:
return False
def zoom_out(self):
if self.zoom_level < len(self.zoom_list)-1:
self.zoom_level += 1
return True
else:
return False
def house_lines(self):
ret = []
for i in range(-64, 64, 8):
lines = house_lines(offset=(i, 0, 0))
ret_lines = []
for l in lines:
ret_lines.append([self.camera.to_camera(*l[0]), self.camera.to_camera(*l[1])])
ret.append(ret_lines)
return ret