計算機視覺專案-人臉識別與檢測

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))

])

def shape_to_np(shape, dtype="int"):

# 建立 68*2

coords = np.zeros((shape.num_parts, 2), dtype=dtype)

# 遍歷每一個關鍵點

# 得到座標

for i in range(0, shape.num_parts):

coords[i] = (shape.part(i).x, shape.part(i).y)

return coords

def visualize_facial_landmarks(image, shape, colors=None, alpha=0.75):

# 建立兩個 copy

# overlay and one for the final output image

overlay = image.copy()

output = image.copy()

# 設定一些顏色區域

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)]

# 遍歷每一個區域

for (i, name) in enumerate(FACIAL_LANDMARKS_68_IDXS.keys()):

# 得到每一個點的座標

(j, k) = FACIAL_LANDMARKS_68_IDXS[name]

pts = shape[j:k]

# 檢查位置

if name == "jaw":

# 用線條連起來

for l in range(1, len(pts)):

ptA = tuple(pts[l - 1])

ptB = tuple(pts[l])

cv2.line(overlay, ptA, ptB, colors[i], 2)

# 計算凸包

else:

hull = cv2.convexHull(pts)

cv2.drawContours(overlay, [hull], -1, colors[i], -1)

# 疊加在原圖上，可以指定比例

cv2.addWeighted(overlay, alpha, output, 1 - alpha, 0, output)

return output

# 載入人臉檢測與關鍵點定位

detector = dlib.get_frontal_face_detector()

predictor = dlib.shape_predictor(args["shape_predictor"])

# 讀取輸入資料，預處理

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)

# 人臉檢測

rects = detector(gray, 1)

# 遍歷檢測到的框

for (i, rect) in enumerate(rects):

# 對人臉框進行關鍵點定位

# 轉換成 ndarray

shape =外匯跟單gendan5.com predictor(gray, rect)

shape = shape_to_np(shape)

# 遍歷每一個部分

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)

# 根據位置畫點

for (x, y) in shape[i:j]:

cv2.circle(clone, (x, y), 3, (0, 0, 255), -1)

# 提取 ROI 區域

(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)

# 顯示每一部分

cv2.imshow("ROI", roi)

cv2.imshow("Image", clone)

cv2.waitKey(0)

# 展示所有區域

output = visualize_facial_landmarks(image, shape)

cv2.imshow("Image", output)

cv2.waitKey(0)