【行空板】 关于openCV的十个应用
本帖最后由 Vico 于 2023-11-22 17:32 编辑当我手上拿到行空板之后，我感觉这个应用实在太过于广泛，其中openCV可以做很多视觉相关的应用。限于篇幅，在这里列举出10种常用的应用：
1.实时人脸检测: 使用OpenCV进行实时人脸检测。代码会启动摄像头，使用Haar级联分类器来检测和框选脸部
import cv2
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
cap = cv2.VideoCapture(0)
while True:
_, img = cap.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.1, 4)
for (x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)
cv2.imshow('img', img)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
2.运动检测: 检测和记录视频中的运动。可以用于安全监控系统。
import cv2
cap = cv2.VideoCapture(0)
ret, frame1 = cap.read()
ret, frame2 = cap.read()
while cap.isOpened():
diff = cv2.absdiff(frame1, frame2)
gray = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (5,5), 0)
_, thresh = cv2.threshold(blur, 20, 255, cv2.THRESH_BINARY)
dilated = cv2.dilate(thresh, None, iterations=3)
contours, _ = cv2.findContours(dilated, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
(x, y, w, h) = cv2.boundingRect(contour)
if cv2.contourArea(contour) < 900:
continue
cv2.rectangle(frame1, (x, y), (x+w, y+h), (0, 255, 0), 2)
cv2.putText(frame1, "Status: {}".format('Movement'), (10,20), cv2.FONT_HERSHEY_SIMPLEX,
1, (0, 0, 255), 3)
cv2.imshow("feed", frame1)
frame1 = frame2
ret, frame2 = cap.read()
if cv2.waitKey(40) == 27:
break
cv2.destroyAllWindows()
cap.release()
3.条形码和二维码读取: 使用摄像头扫描并识别条形码和二维码。
import cv2
import pyzbar.pyzbar as pyzbar
cap = cv2.VideoCapture(0)
while True:
_, frame = cap.read()
decodedObjects = pyzbar.decode(frame)
for obj in decodedObjects:
cv2.putText(frame, str(obj.data), (50, 50), cv2.FONT_HERSHEY_SIMPLEX,
2, (255, 0, 0), 3)
cv2.imshow("Frame", frame)
key = cv2.waitKey(1)
if key == 27:
break
cap.release()
cv2.destroyAllWindows()
4.颜色跟踪: 检测并跟踪视频中特定颜色的物体。
import cv2
import numpy as np
cap = cv2.VideoCapture(0)
while True:
_, frame = cap.read()
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
lower_red = np.array()
upper_red = np.array()
mask = cv2.inRange(hsv, lower_red, upper_red)
res = cv2.bitwise_and(frame,frame, mask= mask)
cv2.imshow('frame',frame)
cv2.imshow('mask',mask)
cv2.imshow('res',res)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
5.手势识别: 通过摄像头
import cv2
import numpy as np
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
# 转换为灰度图像
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# 应用阈值化来突出手势
_, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
# 查找轮廓并绘制
contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
if cv2.contourArea(contour) > 1000:
x, y, w, h = cv2.boundingRect(contour)
cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
cv2.imshow('Gesture', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
6.虚拟化妆: 检测面部特征并在脸部应用虚拟化妆。
import cv2
import dlib
# 加载面部特征检测器
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')
cap = cv2.VideoCapture(0)
while True:
_, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = detector(gray)
for face in faces:
landmarks = predictor(gray, face)
# 用landmarks定位眼睛、嘴巴等，然后在上面绘制化妆效果
# 示例：绘制眼线
cv2.line(frame, (landmarks.part(36).x, landmarks.part(36).y),
(landmarks.part(39).x, landmarks.part(39).y), (0, 0, 255), 2)
cv2.imshow("Frame", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
7.车牌识别: 用于识别视频中的车牌号码。
# 这需要复杂的逻辑和可能涉及机器学习模型，以下是简化的概览
import cv2
# 载入预训练的车牌识别模型
plate_cascade = cv2.CascadeClassifier('haarcascade_russian_plate_number.xml')
cap = cv2.VideoCapture(0)
while True:
_, img = cap.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
plates = plate_cascade.detectMultiScale(gray, 1.1, 3)
for (x, y, w, h) in plates:
cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)
cv2.imshow('img', img)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
8.虚拟试衣: 使用摄像头捕捉用户形象并实时试穿不同的服装。以下编程使用了一个静态的图像（如T恤）作为虚拟服装，并简化地假设用户位于摄像头正前方的固定位置。<blockquote>import cv2
9.实现眼动跟踪，可用于用户界面控制或者行为研究。
import cv2
import dlib
# 加载面部特征检测器
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')
cap = cv2.VideoCapture(0)
while True:
_, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = detector(gray)
for face in faces:
landmarks = predictor(gray, face)
left_eye = landmarks.part(36), landmarks.part(37), landmarks.part(38), landmarks.part(39), landmarks.part(40), landmarks.part(41)
right_eye = landmarks.part(42), landmarks.part(43), landmarks.part(44), landmarks.part(45), landmarks.part(46), landmarks.part(47)
# 绘制眼部轮廓
for n in left_eye + right_eye:
cv2.circle(frame, (n.x, n.y), 2, (255, 0, 0), -1)
cv2.imshow("Eye Tracking", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
10.虚拟背景: 更换摄像头捕捉视频的背景，常用于视频会议或者直播。
import cv2
import numpy as np
cap = cv2.VideoCapture(0)
# 预设一个背景图片
background = cv2.imread('background.jpg')
while True:
_, frame = cap.read()
# 应用一些图像处理技术来分离前景和背景
# 示例代码省略
# 结合前景和新背景
combined_frame = np.where(mask, frame, background)
cv2.imshow("Virtual Background", combined_frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows() 老师dlib是个啥库？pip不了啊
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