手把手教你利用opencv實現(xiàn)人臉識別功能（附源碼+文檔）

pip install opencv-python

# coding=gbk
"""
作者：川川
@時間  : 2021/9/5 16:38
https://github.com/opencv/opencv/tree/master/data/haarcascades
"""
import cv2

# 待檢測的圖片路徑
imagepath="2.jpg"

image = cv2.imread(imagepath)#讀取圖片
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)#圖像轉(zhuǎn)換為灰度圖：

face_cascade = cv2.CascadeClassifier(r'./haarcascade_frontalface_default.xml')#加載使用人臉識別器

faces = face_cascade.detectMultiScale(gray)#檢測圖像中的所有面孔

#為每個人臉繪制一個藍色矩形
for x, y, width, height in faces:
	# 這里的color是 藍 黃 紅，與rgb相反，thickness設(shè)置寬度
    cv2.rectangle(image, (x, y), (x + width, y + height), color=(255, 0, 0), thickness=2)

# 最后，讓我們保存新圖像
cv2.imwrite("beauty_detected.jpg", image)

# coding=gbk
"""
攝像頭人臉識別
作者：川川
@時間  : 2021/9/5 17:15
Haar級聯(lián)結(jié)合攝像頭
"""
import cv2

#創(chuàng)建新的cam對象
cap = cv2.VideoCapture(0,cv2.CAP_DSHOW)
#初始化人臉識別器（默認的人臉haar級聯(lián)）
face_cascade = cv2.CascadeClassifier(r'./haarcascade_frontalface_default.xml')

while True:
    # 從攝像頭讀取圖像
    _, image = cap.read()
    # 轉(zhuǎn)換為灰度
    image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # 檢測圖像中的所有人臉
    faces = face_cascade.detectMultiScale(image_gray, 1.3, 5)
    # 為每個人臉繪制一個藍色矩形
    for x, y, width, height in faces:
        cv2.rectangle(image, (x, y), (x + width, y + height), color=(255, 0, 0), thickness=2)
    cv2.imshow("image", image)
    if cv2.waitKey(1) == ord("q"):
        break

cap.release()
cv2.destroyAllWindows()

# coding=gbk
"""
圖片人臉識別
作者：川川
@時間  : 2021/9/5 17:22
"""
import cv2
import numpy as np
# 下載鏈接：https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt
prototxt_path = r"./deploy.prototxt.txt"
# 下載鏈接：https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel
model_path =r"./res10_300x300_ssd_iter_140000_fp16.caffemodel"
model = cv2.dnn.readNetFromCaffe(prototxt_path, model_path)
image = cv2.imread("2.jpg")
h, w = image.shape[:2]
blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300),(104.0, 177.0, 123.0))
model.setInput(blob)
output = np.squeeze(model.forward())
font_scale = 1.0
for i in range(0, output.shape[0]):
    confidence = output[i, 2]
    if confidence > 0.5:
        box = output[i, 3:7] * np.array([w, h, w, h])
        start_x, start_y, end_x, end_y = box.astype(np.int)
        cv2.rectangle(image, (start_x, start_y), (end_x, end_y), color=(255, 0, 0), thickness=2)
        cv2.putText(image, f"{confidence*100:.2f}%", (start_x, start_y-5), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (255, 0, 0), 2)
cv2.imshow("image", image)
cv2.waitKey(0)
cv2.imwrite("beauty_detected.jpg", image)

# coding=gbk
"""
作者：川川
@時間  : 2021/9/5 17:26
SSD結(jié)合攝像頭的人臉檢測
"""
import cv2
import numpy as np
prototxt_path = "deploy.prototxt.txt"
model_path = "res10_300x300_ssd_iter_140000_fp16.caffemodel"
model = cv2.dnn.readNetFromCaffe(prototxt_path, model_path)
cap = cv2.VideoCapture(0)
while True:
    _, image = cap.read()
    h, w = image.shape[:2]
    blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300), (104.0, 177.0, 123.0))
    model.setInput(blob)
    output = np.squeeze(model.forward())
    font_scale = 1.0
    for i in range(0, output.shape[0]):
        confidence = output[i, 2]
        if confidence > 0.5:
            box = output[i, 3:7] * np.array([w, h, w, h])
            start_x, start_y, end_x, end_y = box.astype(np.int)
            cv2.rectangle(image, (start_x, start_y), (end_x, end_y), color=(255, 0, 0), thickness=2)
            cv2.putText(image, f"{confidence*100:.2f}%", (start_x, start_y-5), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (255, 0, 0), 2)
    cv2.imshow("image", image)
    if cv2.waitKey(1) == ord("q"):
        break
cv2.destroyAllWindows()
cap.release()