ControlNetMediaPipeFace /
Joseph Catrambone
First import. Add ControlNetSD21 Laion Face (full, pruned, and safetensors). Add README and samples. Add surrounding tools for example use.
from typing import Mapping
import mediapipe as mp
import numpy
from PIL import Image
mp_drawing =
mp_drawing_styles =
mp_face_detection = # Only for counting faces.
mp_face_mesh =
mp_face_connections =
mp_hand_connections =
mp_body_connections =
DrawingSpec =
PoseLandmark =
f_thick = 2
f_rad = 1
right_iris_draw = DrawingSpec(color=(10, 200, 250), thickness=f_thick, circle_radius=f_rad)
right_eye_draw = DrawingSpec(color=(10, 200, 180), thickness=f_thick, circle_radius=f_rad)
right_eyebrow_draw = DrawingSpec(color=(10, 220, 180), thickness=f_thick, circle_radius=f_rad)
left_iris_draw = DrawingSpec(color=(250, 200, 10), thickness=f_thick, circle_radius=f_rad)
left_eye_draw = DrawingSpec(color=(180, 200, 10), thickness=f_thick, circle_radius=f_rad)
left_eyebrow_draw = DrawingSpec(color=(180, 220, 10), thickness=f_thick, circle_radius=f_rad)
mouth_draw = DrawingSpec(color=(10, 180, 10), thickness=f_thick, circle_radius=f_rad)
head_draw = DrawingSpec(color=(10, 200, 10), thickness=f_thick, circle_radius=f_rad)
# mp_face_mesh.FACEMESH_CONTOURS has all the items we care about.
face_connection_spec = {}
for edge in mp_face_mesh.FACEMESH_FACE_OVAL:
face_connection_spec[edge] = head_draw
for edge in mp_face_mesh.FACEMESH_LEFT_EYE:
face_connection_spec[edge] = left_eye_draw
for edge in mp_face_mesh.FACEMESH_LEFT_EYEBROW:
face_connection_spec[edge] = left_eyebrow_draw
# for edge in mp_face_mesh.FACEMESH_LEFT_IRIS:
# face_connection_spec[edge] = left_iris_draw
for edge in mp_face_mesh.FACEMESH_RIGHT_EYE:
face_connection_spec[edge] = right_eye_draw
for edge in mp_face_mesh.FACEMESH_RIGHT_EYEBROW:
face_connection_spec[edge] = right_eyebrow_draw
# for edge in mp_face_mesh.FACEMESH_RIGHT_IRIS:
# face_connection_spec[edge] = right_iris_draw
for edge in mp_face_mesh.FACEMESH_LIPS:
face_connection_spec[edge] = mouth_draw
iris_landmark_spec = {468: right_iris_draw, 473: left_iris_draw}
def draw_pupils(image, landmark_list, drawing_spec, halfwidth: int = 2):
"""We have a custom function to draw the pupils because the mp.draw_landmarks method requires a parameter for all
landmarks. Until our PR is merged into mediapipe, we need this separate method."""
if len(image.shape) != 3:
raise ValueError("Input image must be H,W,C.")
image_rows, image_cols, image_channels = image.shape
if image_channels != 3: # BGR channels
raise ValueError('Input image must contain three channel bgr data.')
for idx, landmark in enumerate(landmark_list.landmark):
if (
(landmark.HasField('visibility') and landmark.visibility < 0.9) or
(landmark.HasField('presence') and landmark.presence < 0.5)
if landmark.x >= 1.0 or landmark.x < 0 or landmark.y >= 1.0 or landmark.y < 0:
image_x = int(image_cols*landmark.x)
image_y = int(image_rows*landmark.y)
draw_color = None
if isinstance(drawing_spec, Mapping):
if drawing_spec.get(idx) is None:
draw_color = drawing_spec[idx].color
elif isinstance(drawing_spec, DrawingSpec):
draw_color = drawing_spec.color
image[image_y-halfwidth:image_y+halfwidth, image_x-halfwidth:image_x+halfwidth, :] = draw_color
def reverse_channels(image):
"""Given a numpy array in RGB form, convert to BGR. Will also convert from BGR to RGB."""
# im[:,:,::-1] is a neat hack to convert BGR to RGB by reversing the indexing order.
# im[:,:,::[2,1,0]] would also work but makes a copy of the data.
return image[:, :, ::-1]
def generate_annotation(
input_image: Image.Image,
max_faces: int,
min_face_size_pixels: int = 0,
return_annotation_data: bool = False
Find up to 'max_faces' inside the provided input image.
If min_face_size_pixels is provided and nonzero it will be used to filter faces that occupy less than this many
pixels in the image.
If return_annotation_data is TRUE (default: false) then in addition to returning the 'detected face' image, three
additional parameters will be returned: faces before filtering, faces after filtering, and an annotation image.
The faces_before_filtering return value is the number of faces detected in an image with no filtering.
faces_after_filtering is the number of faces remaining after filtering small faces.
If 'return_annotation_data==True', returns (numpy array, numpy array, int, int).
If 'return_annotation_data==False' (default), returns a numpy array.
with mp_face_mesh.FaceMesh(
) as facemesh:
img_rgb = numpy.asarray(input_image)
results = facemesh.process(img_rgb).multi_face_landmarks
faces_found_before_filtering = len(results)
# Filter faces that are too small
filtered_landmarks = []
for lm in results:
landmarks = lm.landmark
face_rect = [
] # Left, up, right, down.
for i in range(len(landmarks)):
face_rect[0] = min(face_rect[0], landmarks[i].x)
face_rect[1] = min(face_rect[1], landmarks[i].y)
face_rect[2] = max(face_rect[2], landmarks[i].x)
face_rect[3] = max(face_rect[3], landmarks[i].y)
if min_face_size_pixels > 0:
face_width = abs(face_rect[2] - face_rect[0])
face_height = abs(face_rect[3] - face_rect[1])
face_width_pixels = face_width * input_image.size[0]
face_height_pixels = face_height * input_image.size[1]
face_size = min(face_width_pixels, face_height_pixels)
if face_size >= min_face_size_pixels:
faces_remaining_after_filtering = len(filtered_landmarks)
# Annotations are drawn in BGR for some reason, but we don't need to flip a zero-filled image at the start.
empty = numpy.zeros_like(img_rgb)
# Draw detected faces:
for face_landmarks in filtered_landmarks:
draw_pupils(empty, face_landmarks, iris_landmark_spec, 2)
# Flip BGR back to RGB.
empty = reverse_channels(empty)
# We might have to generate a composite.
if return_annotation_data:
# Note that we're copying the input image AND flipping the channels so we can draw on top of it.
annotated = reverse_channels(numpy.asarray(input_image)).copy()
for face_landmarks in filtered_landmarks:
draw_pupils(empty, face_landmarks, iris_landmark_spec, 2)
annotated = reverse_channels(annotated)
if not return_annotation_data:
return empty
return empty, annotated, faces_found_before_filtering, faces_remaining_after_filtering