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import math
import mediapipe as mp
from mediapipe.tasks import python
from mediapipe.tasks.python import vision
from mediapipe.framework.formats import landmark_pb2
from mediapipe import solutions
import numpy as np
def calculate_distance(p1, p2):
"""
"""
return math.sqrt((p2[0] - p1[0])**2 + (p2[1] - p1[1])**2)
def to_int_points(points):
ints=[]
for pt in points:
#print(pt)
value = [int(pt[0]),int(pt[1])]
#print(value)
ints.append(value)
return ints
debug = False
def divide_line_to_points(points,divided): # return divided + 1
total_length = 0
line_length_list = []
for i in range(len(points)-1):
pt_length = calculate_distance(points[i],points[i+1])
total_length += pt_length
line_length_list.append(pt_length)
splited_length = total_length/divided
def get_new_point(index,lerp):
pt1 = points[index]
pt2 = points[index+1]
diff = [pt2[0] - pt1[0], pt2[1]-pt1[1]]
new_point = [pt1[0]+diff[0]*lerp,pt1[1]+diff[1]*lerp]
if debug:
print(f"pt1 ={pt1} pt2 ={pt2} diff={diff} new_point={new_point}")
return new_point
if debug:
print(f"{total_length} splitted = {splited_length} line-length-list = {len(line_length_list)}")
splited_points=[points[0]]
for i in range(1,divided):
need_length = splited_length*i
if debug:
print(f"{i} need length = {need_length}")
current_length = 0
for j in range(len(line_length_list)):
line_length = line_length_list[j]
current_length+=line_length
if current_length>need_length:
if debug:
print(f"over need length index = {j} current={current_length}")
diff = current_length - need_length
lerp_point = 1.0 - (diff/line_length)
if debug:
print(f"over = {diff} lerp ={lerp_point}")
new_point = get_new_point(j,lerp_point)
splited_points.append(new_point)
break
splited_points.append(points[-1]) # last one
splited_points=to_int_points(splited_points)
if debug:
print(f"sp={len(splited_points)}")
return splited_points
def expand_bbox(bbox,left=5,top=5,right=5,bottom=5):
left_pixel = bbox[2]*(float(left)/100)
top_pixel = bbox[3]*(float(top)/100)
right_pixel = bbox[2]*(float(right)/100)
bottom_pixel = bbox[3]*(float(bottom)/100)
new_box = list(bbox)
new_box[0] -=left_pixel
new_box[1] -=top_pixel
new_box[2] +=left_pixel+right_pixel
new_box[3] +=top_pixel+bottom_pixel
return new_box
#normalized value index see mp_constants
def get_normalized_cordinate(face_landmarks_list,index):
x=face_landmarks_list[0][index].x
y=face_landmarks_list[0][index].y
return x,y
def get_pixel_cordinate(face_landmarks_list,landmark,width,height):
point = get_normalized_cordinate(face_landmarks_list,landmark)
return int(point[0]*width),int(point[1]*height)
def get_pixel_cordinate_list(face_landmarks_list,indices,width,height):
cordinates = []
for index in indices:
cordinates.append(get_pixel_cordinate(face_landmarks_list,index,width,height))
return cordinates
def extract_landmark(image_data,model_path="face_landmarker.task"):
BaseOptions = mp.tasks.BaseOptions
FaceLandmarker = mp.tasks.vision.FaceLandmarker
FaceLandmarkerOptions = mp.tasks.vision.FaceLandmarkerOptions
VisionRunningMode = mp.tasks.vision.RunningMode
options = FaceLandmarkerOptions(
base_options=BaseOptions(model_asset_path=model_path),
running_mode=VisionRunningMode.IMAGE
,min_face_detection_confidence=0, min_face_presence_confidence=0
)
with FaceLandmarker.create_from_options(options) as landmarker:
if isinstance(image_data,str):
mp_image = mp.Image.create_from_file(image_data)
else:
mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=np.asarray(image_data))
face_landmarker_result = landmarker.detect(mp_image)
return mp_image,face_landmarker_result |