Spaces:
Sleeping
Sleeping
File size: 7,588 Bytes
1f72938 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 |
import cv2
import mediapipe as mp
from mediapipe import solutions
from mediapipe.framework.formats import landmark_pb2
import numpy as np
import math
# visualization libraries
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from matplotlib import style
def draw_eyes_on_image(rgb_image, detection_result):
# return rgb_image, 0, 0
# canonical_face_model_uv_visualization in the below link
# https://github.com/google/mediapipe/blob/a908d668c730da128dfa8d9f6bd25d519d006692/mediapipe/modules/face_geometry/data/canonical_face_model_uv_visualization.png
left_eyes_bottom_list = [33, 7, 163, 144, 145, 153, 154, 155, 133]
left_eyes_top_list = [246, 161, 160, 159, 158, 157, 173]
right_eyes_bottom_list = [362, 382, 381, 380, 374, 373, 390, 249, 263]
right_eyes_top_list = [398, 384, 385, 386, 387, 388, 466]
face_landmarks_list = detection_result.face_landmarks
annotated_image = np.copy(rgb_image)
# We resize image to 640 * 360
height, width, channels = rgb_image.shape
# Loop through the detected faces to visualize. Actually, if we detect more than two faces, we will require user closer to the camera
for idx in range(len(face_landmarks_list)):
face_landmarks = face_landmarks_list[idx]
mlist = []
for landmark in face_landmarks:
mlist.append([int(landmark.x * width), int(landmark.y * height), landmark.z])
narray = np.copy(mlist)
# Vertical line
#
#
# Pick the largest difference (middle of the eyes)
leftUp = narray[159]
leftDown = narray[145]
rightUp = narray[386]
rightDown = narray[374]
# compute left eye distance (vertical)
leftUp_x = int(leftUp[0])
leftUp_y = int(leftUp[1])
leftDown_x = int(leftDown[0])
leftDown_y = int(leftDown[1])
leftVerDis = math.dist([leftUp_x, leftUp_y],[leftDown_x, leftDown_y])
# compute right eye distance (vertical)
rightUp_x = int(rightUp[0])
rightUp_y = int(rightUp[1])
rightDown_x = int(rightDown[0])
rightDown_y = int(rightDown[1])
rightVerDis = math.dist([rightUp_x, rightUp_y],[rightDown_x, rightDown_y])
# print(f'leftVerDis: {leftVerDis} and rightVerDis: {rightVerDis}')
# draw a line from left eye top to bottom
annotated_image = cv2.line(rgb_image, (int(leftUp_x), int(leftUp_y)), (int(leftDown_x), int(leftDown_y)), (0, 200, 0), 1)
# draw a line from right eye top to bottom
annotated_image = cv2.line(rgb_image, (int(rightUp_x), int(rightUp_y)), (int(rightDown_x), int(rightDown_y)), (0, 200, 0), 1)
#
#
# Horizontonal line
#
#
# Pick the largest difference (middle of the eyes)
leftLeft = narray[33]
leftRight = narray[133]
rightLeft = narray[362]
rightRight = narray[263]
# compute left eye distance (horizontal)
leftLeft_x = int(leftLeft[0])
leftLeft_y = int(leftLeft[1])
leftRight_x = int(leftRight[0])
leftRight_y = int(leftRight[1])
leftHorDis = math.dist([leftLeft_x, leftLeft_y],[leftRight_x, leftRight_y])
# compute right eye distance (horizontal)
rightLeft_x = int(rightLeft[0])
rightLeft_y = int(rightLeft[1])
rightRight_x = int(rightRight[0])
rightRight_y = int(rightRight[1])
rightHorDis = math.dist([rightLeft_x, rightLeft_y],[rightRight_x, rightRight_y])
# print(f'leftHorDis: {leftHorDis} and rightHorDis: {rightHorDis}')
# draw a line from left eye top to bottom
annotated_image = cv2.line(rgb_image, (int(leftLeft_x), int(leftLeft_y)), (int(leftRight_x), int(leftRight_y)), (0, 200, 0), 1)
# draw a line from right eye top to bottom
annotated_image = cv2.line(rgb_image, (int(rightLeft_x), int(rightLeft_y)), (int(rightRight_x), int(rightRight_y)), (0, 200, 0), 1)
#
#
#
#
# print(f'leftRatio: {leftVerDis/leftHorDis} and rightRatio: {rightVerDis/rightHorDis}')
leftRatio = leftVerDis/leftHorDis*100
rightRatio = rightVerDis/rightHorDis*100
# left_eyes_bottom = [narray[x] for x in left_eyes_bottom_list]
# left_eyes_top = [narray[x] for x in left_eyes_top_list]
# right_eyes_bottom = [narray[x] for x in right_eyes_bottom_list]
# right_eyes_top = [narray[x] for x in right_eyes_top_list]
# for p in left_eyes_bottom:
# annotated_image = cv2.circle(rgb_image, (int(p[0]), int(p[1])), radius=1, color=(0,0,255), thickness=1)
# for p in left_eyes_top:
# annotated_image = cv2.circle(rgb_image, (int(p[0]), int(p[1])), radius=1, color=(0,0,255), thickness=1)
# for p in right_eyes_bottom:
# annotated_image = cv2.circle(rgb_image, (int(p[0]), int(p[1])), radius=1, color=(0,0,255), thickness=1)
# for p in right_eyes_top:
# annotated_image = cv2.circle(rgb_image, (int(p[0]), int(p[1])), radius=1, color=(0,0,255), thickness=1)
return annotated_image, leftRatio, rightRatio
def draw_landmarks_on_image(rgb_image, detection_result):
face_landmarks_list = detection_result.face_landmarks
annotated_image = np.copy(rgb_image)
# Loop through the detected faces to visualize. Actually, if we detect more than two faces, we will require user closer to the camera
for idx in range(len(face_landmarks_list)):
face_landmarks = face_landmarks_list[idx]
# Draw the face landmarks.
face_landmarks_proto = landmark_pb2.NormalizedLandmarkList()
face_landmarks_proto.landmark.extend([
landmark_pb2.NormalizedLandmark(x=landmark.x, y=landmark.y, z=landmark.z) for landmark in face_landmarks
])
solutions.drawing_utils.draw_landmarks(
image=annotated_image,
landmark_list=face_landmarks_proto,
connections=mp.solutions.face_mesh.FACEMESH_TESSELATION,
landmark_drawing_spec=None,
connection_drawing_spec=mp.solutions.drawing_styles
.get_default_face_mesh_tesselation_style())
solutions.drawing_utils.draw_landmarks(
image=annotated_image,
landmark_list=face_landmarks_proto,
connections=mp.solutions.face_mesh.FACEMESH_CONTOURS,
landmark_drawing_spec=None,
connection_drawing_spec=mp.solutions.drawing_styles
.get_default_face_mesh_contours_style())
solutions.drawing_utils.draw_landmarks(
image=annotated_image,
landmark_list=face_landmarks_proto,
connections=mp.solutions.face_mesh.FACEMESH_IRISES,
landmark_drawing_spec=None,
connection_drawing_spec=mp.solutions.drawing_styles
.get_default_face_mesh_iris_connections_style())
return annotated_image
def plot_face_blendshapes_bar_graph(face_blendshapes):
# Extract the face blendshapes category names and scores.
face_blendshapes_names = [face_blendshapes_category.category_name for face_blendshapes_category in face_blendshapes]
face_blendshapes_scores = [face_blendshapes_category.score for face_blendshapes_category in face_blendshapes]
# The blendshapes are ordered in decreasing score value.
face_blendshapes_ranks = range(len(face_blendshapes_names))
fig, ax = plt.subplots(figsize=(12, 12))
bar = ax.barh(face_blendshapes_ranks, face_blendshapes_scores, label=[str(x) for x in face_blendshapes_ranks])
ax.set_yticks(face_blendshapes_ranks, face_blendshapes_names)
ax.invert_yaxis()
# Label each bar with values
for score, patch in zip(face_blendshapes_scores, bar.patches):
plt.text(patch.get_x() + patch.get_width(), patch.get_y(), f"{score:.4f}", va="top")
ax.set_xlabel('Score')
ax.set_title("Face Blendshapes")
plt.tight_layout()
plt.show() |