OpenCV Python 人脸识别 只有一种人脸识别
OpenCV Python face recognition only one face recognition
大家好我们正在开发人脸识别应用程序，当我运行下面的代码时，正如您在照片中看到的那样，它只能在一张脸上工作（红色方块）它不扫描训练数据中的其他人脸我猜我的预测函数只有 运行 一次。不要在循环中。
处理后的图像：LINK
# coding: utf-8
import cv2
import os
import numpy as np
suclular = ["Bilinmeyen", "Veli Eroglu", "Ali Eroglu"]
def detect_face(img):
# ALGORİMA için Gri Yapıyoruz.
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# yüz tanımlama için geereken haarcascade
face_cascade = cv2.CascadeClassifier(
'opencv-files/lbpcascade_frontalface.xml')
faces = face_cascade.detectMultiScale(
gray, scaleFactor=1.2, minNeighbors=5) # YÜZ TANIMLAMA
for (x, y, w, h) in faces:
img = cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
if (len(faces) == 0):
return None, None # Yuz bulunamazsa...
(x, y, w, h) = faces[0]
return gray[y:y + w, x:x + h], faces[0]
face_recognizer = cv2.face.LBPHFaceRecognizer_create()
face_recognizer.train(faces, np.array(labels))
def draw_rectangle(img, rect):
(x, y, w, h) = rect
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 2)
def draw_text(img, text, x, y):
cv2.putText(img, text, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 0, 255), 2)
def predict(test_img):
img = test_img.copy()
face, rect = detect_face(img)
label, confidence = face_recognizer.predict(face)
print(confidence)
label_text = suclular[label]
if confidence > 42 and confidence < 70:
label_text = "Tespit Edilemedi."
print(label_text)
elif confidence > 70:
label_text = "Bilinmiyor"
draw_rectangle(img, rect)
draw_text(img, label_text, rect[0], rect[1] - 5)
return img
print("Predicting images...")
test_img1 = cv2.imread("test-data/test8jpg.jpg")
predicted_img1 = predict(test_img1)
print("Prediction complete")
cv2.imshow("SONUC", cv2.resize(predicted_img1, (400, 500)))
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.waitKey(1)
cv2.destroyAllWindows()
为什么要将原始图像传递给 predict() 函数？
检测到这两张脸后，您应该将它们中的每一张 (extracted from the original image using OpenCV functions) 传递给预测函数。
这样你的算法就会工作并且更快。
祝你好运！
你的预测应该在 for 循环中......你只从你的 detect_face 函数返回一张脸，这是最后一张脸，即使你正在遍历每张脸并为每张脸制作一个矩形面对...你应该这样做：
def predict_face(img):
# ALGORİMA için Gri Yapıyoruz.
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# yüz tanımlama için geereken haarcascade
face_cascade = cv2.CascadeClassifier(
'opencv-files/lbpcascade_frontalface.xml')
faces = face_cascade.detectMultiScale(
gray, scaleFactor=1.2, minNeighbors=5) # YÜZ TANIMLAMA
detected_faces = []
i = 0
for (x, y, w, h) in faces:
img = cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
detected_face = gray[y:y + w, x:x + h]
label, confidence = face_recognizer.predict(detected_face) # Prediction inside for loop
draw_rectangle(img, faces[i]) # draw the red rectangles for every predicted face
draw_text(img, label, x, y - 5) # draw the predicted label on top of the box
i += 1
- 查看for循环中的第三行...我的预测在for循环中
- 甚至在for循环中绘制矩形
- 无需存储矩形并再次循环绘制它们
- 在for循环中绘制预测
- 所以你可以在一个函数中完成所有事情
大家好我们正在开发人脸识别应用程序，当我运行下面的代码时，正如您在照片中看到的那样，它只能在一张脸上工作（红色方块）它不扫描训练数据中的其他人脸我猜我的预测函数只有 运行 一次。不要在循环中。
处理后的图像：LINK
# coding: utf-8
import cv2
import os
import numpy as np
suclular = ["Bilinmeyen", "Veli Eroglu", "Ali Eroglu"]
def detect_face(img):
# ALGORİMA için Gri Yapıyoruz.
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# yüz tanımlama için geereken haarcascade
face_cascade = cv2.CascadeClassifier(
'opencv-files/lbpcascade_frontalface.xml')
faces = face_cascade.detectMultiScale(
gray, scaleFactor=1.2, minNeighbors=5) # YÜZ TANIMLAMA
for (x, y, w, h) in faces:
img = cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
if (len(faces) == 0):
return None, None # Yuz bulunamazsa...
(x, y, w, h) = faces[0]
return gray[y:y + w, x:x + h], faces[0]
face_recognizer = cv2.face.LBPHFaceRecognizer_create()
face_recognizer.train(faces, np.array(labels))
def draw_rectangle(img, rect):
(x, y, w, h) = rect
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 2)
def draw_text(img, text, x, y):
cv2.putText(img, text, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 0, 255), 2)
def predict(test_img):
img = test_img.copy()
face, rect = detect_face(img)
label, confidence = face_recognizer.predict(face)
print(confidence)
label_text = suclular[label]
if confidence > 42 and confidence < 70:
label_text = "Tespit Edilemedi."
print(label_text)
elif confidence > 70:
label_text = "Bilinmiyor"
draw_rectangle(img, rect)
draw_text(img, label_text, rect[0], rect[1] - 5)
return img
print("Predicting images...")
test_img1 = cv2.imread("test-data/test8jpg.jpg")
predicted_img1 = predict(test_img1)
print("Prediction complete")
cv2.imshow("SONUC", cv2.resize(predicted_img1, (400, 500)))
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.waitKey(1)
cv2.destroyAllWindows()
为什么要将原始图像传递给 predict() 函数？
检测到这两张脸后，您应该将它们中的每一张 (extracted from the original image using OpenCV functions) 传递给预测函数。
这样你的算法就会工作并且更快。
祝你好运！
你的预测应该在 for 循环中......你只从你的 detect_face 函数返回一张脸，这是最后一张脸，即使你正在遍历每张脸并为每张脸制作一个矩形面对...你应该这样做：
def predict_face(img):
# ALGORİMA için Gri Yapıyoruz.
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# yüz tanımlama için geereken haarcascade
face_cascade = cv2.CascadeClassifier(
'opencv-files/lbpcascade_frontalface.xml')
faces = face_cascade.detectMultiScale(
gray, scaleFactor=1.2, minNeighbors=5) # YÜZ TANIMLAMA
detected_faces = []
i = 0
for (x, y, w, h) in faces:
img = cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
detected_face = gray[y:y + w, x:x + h]
label, confidence = face_recognizer.predict(detected_face) # Prediction inside for loop
draw_rectangle(img, faces[i]) # draw the red rectangles for every predicted face
draw_text(img, label, x, y - 5) # draw the predicted label on top of the box
i += 1
- 查看for循环中的第三行...我的预测在for循环中
- 甚至在for循环中绘制矩形
- 无需存储矩形并再次循环绘制它们
- 在for循环中绘制预测
- 所以你可以在一个函数中完成所有事情