重磅干货,第一时间送达
目
录
content
一.项目介绍前言
二.识别检测方法
本文方法
项目解析
三.完整代码及效果展示
from collections import OrderedDict
import numpy as np
import argparse
import dlib
import cv2
ap = argparse.ArgumentParser()
ap.add_argument('-p', '--shape-predictor', required=True,
help='path to facial landmark predictor')
ap.add_argument('-i', '--image', required=True,
help='path to input image')
args = vars(ap.parse_args())
FACIAL_LANDMARKS_68_IDXS = OrderedDict([
('mouth', (48, 68)),
('right_eyebrow', (17, 22)),
('left_eyebrow', (22, 27)),
('right_eye', (36, 42)),
('left_eye', (42, 48)),
('nose', (27, 36)),
('jaw', (0, 17))
])
FACIAL_LANDMARKS_5_IDXS = OrderedDict([
('right_eye', (2, 3)),
('left_eye', (0, 1)),
('nose', (4))
])
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(args['shape_predictor'])
image = cv2.imread(args['image'])
(h, w) = image.shape[:2]
width=500
r = width / float(w)
dim = (width, int(h * r))
image = cv2.resize(image, dim, interpolation=cv2.INTER_AREA)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
rects = detector(gray, 1)
for (i, rect) in enumerate(rects):
# 对人脸框进行关键点定位
# 转换成ndarray
shape = predictor(gray, rect)
shape = shape_to_np(shape)
def shape_to_np(shape, dtype='int'):
# 创建68*2
coords = np.zeros((shape.num_parts, 2), dtype=dtype)
# 遍历每一个关键点
# 得到坐标
for i in range(0, shape.num_parts):
coords[i] = (shape.part(i).x, shape.part(i).y)
return coords
for (name, (i, j)) in FACIAL_LANDMARKS_68_IDXS.items():
clone = image.copy()
cv2.putText(clone, name, (10, 30), cv2.FONT_HERSHEY_SIMPLEX,
0.7, (0, 0, 255), 2)
# 根据位置画点
for (x, y) in shape[i:j]:
cv2.circle(clone, (x, y), 3, (0, 0, 255), -1)
# 提取ROI区域
(x, y, w, h) = cv2.boundingRect(np.array([shape[i:j]]))
roi = image[y:y + h, x:x + w]
(h, w) = roi.shape[:2]
width=250
r = width / float(w)
dim = (width, int(h * r))
roi = cv2.resize(roi, dim, interpolation=cv2.INTER_AREA)
# 显示每一部分
cv2.imshow('ROI', roi)
cv2.imshow('Image', clone)
cv2.waitKey(0)
output = visualize_facial_landmarks(image, shape)
cv2.imshow('Image', output)
cv2.waitKey(0)
def visualize_facial_landmarks(image, shape, colors=None, alpha=0.75):
# 创建两个copy
# overlay and one for the final output image
overlay = image.copy()
output = image.copy()
# 设置一些颜色区域
if colors is None:
colors = [(19, 199, 109), (79, 76, 240), (230, 159, 23),
(168, 100, 168), (158, 163, 32),
(163, 38, 32), (180, 42, 220)]
# 遍历每一个区域
for (i, name) in enumerate(FACIAL_LANDMARKS_68_IDXS.keys()):
# 得到每一个点的坐标
(j, k) = FACIAL_LANDMARKS_68_IDXS[name]
pts = shape[j:k]
# 检查位置
if name == 'jaw':
# 用线条连起来
for l in range(1, len(pts)):
ptA = tuple(pts[l - 1])
ptB = tuple(pts[l])
cv2.line(overlay, ptA, ptB, colors[i], 2)
# 计算凸包
else:
hull = cv2.convexHull(pts)
cv2.drawContours(overlay, [hull], -1, colors[i], -1)
# 叠加在原图上,可以指定比例
cv2.addWeighted(overlay, alpha, output, 1 - alpha, 0, output)
return output
from collections import OrderedDictimport numpy as npimport argparseimport dlibimport cv2ap = argparse.ArgumentParser()ap.add_argument('-p', '--shape-predictor', required=True, help='path to facial landmark predictor')ap.add_argument('-i', '--image', required=True, help='path to input image')args = vars(ap.parse_args())FACIAL_LANDMARKS_68_IDXS = OrderedDict([ ('mouth', (48, 68)), ('right_eyebrow', (17, 22)), ('left_eyebrow', (22, 27)), ('right_eye', (36, 42)), ('left_eye', (42, 48)), ('nose', (27, 36)), ('jaw', (0, 17))])FACIAL_LANDMARKS_5_IDXS = OrderedDict([ ('right_eye', (2, 3)), ('left_eye', (0, 1)), ('nose', (4))])def shape_to_np(shape, dtype='int'): # 创建68*2 coords = np.zeros((shape.num_parts, 2), dtype=dtype) # 遍历每一个关键点 # 得到坐标 for i in range(0, shape.num_parts): coords[i] = (shape.part(i).x, shape.part(i).y) return coordsdef visualize_facial_landmarks(image, shape, colors=None, alpha=0.75): # 创建两个copy # overlay and one for the final output image overlay = image.copy() output = image.copy() # 设置一些颜色区域 if colors is None: colors = [(19, 199, 109), (79, 76, 240), (230, 159, 23), (168, 100, 168), (158, 163, 32), (163, 38, 32), (180, 42, 220)] # 遍历每一个区域 for (i, name) in enumerate(FACIAL_LANDMARKS_68_IDXS.keys()): # 得到每一个点的坐标 (j, k) = FACIAL_LANDMARKS_68_IDXS[name] pts = shape[j:k] # 检查位置 if name == 'jaw': # 用线条连起来 for l in range(1, len(pts)): ptA = tuple(pts[l - 1]) ptB = tuple(pts[l]) cv2.line(overlay, ptA, ptB, colors[i], 2) # 计算凸包 else: hull = cv2.convexHull(pts) cv2.drawContours(overlay, [hull], -1, colors[i], -1) # 叠加在原图上,可以指定比例 cv2.addWeighted(overlay, alpha, output, 1 - alpha, 0, output) return output# 加载人脸检测与关键点定位detector = dlib.get_frontal_face_detector()predictor = dlib.shape_predictor(args['shape_predictor'])# 读取输入数据,预处理image = cv2.imread(args['image'])(h, w) = image.shape[:2]width=500r = width / float(w)dim = (width, int(h * r))image = cv2.resize(image, dim, interpolation=cv2.INTER_AREA)gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)# 人脸检测rects = detector(gray, 1)# 遍历检测到的框for (i, rect) in enumerate(rects): # 对人脸框进行关键点定位 # 转换成ndarray shape = predictor(gray, rect) shape = shape_to_np(shape) # 遍历每一个部分 for (name, (i, j)) in FACIAL_LANDMARKS_68_IDXS.items(): clone = image.copy() cv2.putText(clone, name, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) # 根据位置画点 for (x, y) in shape[i:j]: cv2.circle(clone, (x, y), 3, (0, 0, 255), -1) # 提取ROI区域 (x, y, w, h) = cv2.boundingRect(np.array([shape[i:j]])) roi = image[y:y + h, x:x + w] (h, w) = roi.shape[:2] width=250 r = width / float(w) dim = (width, int(h * r)) roi = cv2.resize(roi, dim, interpolation=cv2.INTER_AREA) # 显示每一部分 cv2.imshow('ROI', roi) cv2.imshow('Image', clone) cv2.waitKey(0) # 展示所有区域 output = visualize_facial_landmarks(image, shape) cv2.imshow('Image', output) cv2.waitKey(0)
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