sudo rpi-update
sudo apt-get update
sudo apt-get -y upgrade
sudo apt-get -y install libcv-dev libopencv-dev python-dev python-opencv python-imaging python-pip
sudo pip install numpyfrom picamera.array import PiRGBArray
from picamera import PiCamera
import time
import cv2
camera = PiCamera()
camera.resolution = (640, 480)
camera.framerate = 32
rawCapture = PiRGBArray(camera, size=(640, 480))
time.sleep(0.1)
for frame in camera.capture_continuous(rawCapture, format="bgr", use_video_port=True):
image = frame.array
cv2.imshow("Frame", image)
key = cv2.waitKey(1) & 0xFF
print image
rawCapture.truncate(0)import cv2
capture = cv2.VideoCapture(0)
while True:
ret, img = capture.read()
print img
cv2.imshow('Video', img)
key = cv2.waitKey(1)
# cv2.imwrite('a.jpg', img)
capture.release()#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/core/core.hpp>
using namespace cv;
int main()
{
VideoCapture cap(0);
Mat frame;
while(1)
{
cap.read(frame);
imshow("当前视频",frame);
waitKey(30);
}
return 0;
}import cv2
cap = cv2.VideoCapture(0)
cap.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, 640)
cap.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, 480)
while True:
flag, img = cap.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
edge = cv2.Canny(gray, 500, 2000, apertureSize=5)
# vis = img.copy()
# vis[edge != 0] = (0, 255, 0)
cv2.imshow('edge', edge)
ch = cv2.waitKey(5)
if ch == 27:
break
cv2.destroyAllWindows()PIL（Python Image Library）是一个基于python的图像处理库。
# coding: utf-8
import cv2
import cv2.cv as cv
import PIL.Image as Image
import StringIO
capture = cv2.VideoCapture(-1)
capture.set(cv.CV_CAP_PROP_FRAME_WIDTH, 640)
capture.set(cv.CV_CAP_PROP_FRAME_HEIGHT, 480)
while True:
ret, img = capture.read() # 从摄像头读取图片
cv2.imshow('Video', img) # 显示图片
key = cv2.waitKey(30)&0xFF
if key == 27: # ESC
break
elif key == 32: # Space
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # 将图片从BGR转为RGB
image = Image.fromarray(img) # 将图片转为Image对象
buf = StringIO.StringIO() # 生成StringIO对象
image.save(buf, format="JPEG") # 将图片以JPEG格式写入StringIO
jpeg = buf.getvalue() # 获取JPEG图片内容
buf.close() # 关闭StringIO
print '文件大小：', len(jpeg)/1024, 'KB'
f = open('a.jpg', 'w')
f.write(jpeg)
f.close()
capture.release()
##利用socket传输图像
# coding: utf-8
import cv2
import cv2.cv as cv
import PIL.Image as Image
import StringIO
import socket
import threading
import time
capture = cv2.VideoCapture(0)
capture.set(cv.CV_CAP_PROP_FRAME_WIDTH, 320)
capture.set(cv.CV_CAP_PROP_FRAME_HEIGHT, 240)
frame = 0
lastframe = 0
newimg = 0
class Reader(threading.Thread):
def __init__(self, client):
threading.Thread.__init__(self)
self.client = client
global frame
frame += 1
def run(self):
global newimg
while newimg == 0:
time.sleep(0.001)
newimg = 0
self.client.sendall(jpeg)
self.client.close()
class Listener(threading.Thread):
def __init__(self):
threading.Thread.__init__(self)
self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
self.sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
self.sock.bind(('', 8080))
self.sock.listen(0)
def run(self):
while True:
client = self.sock.accept()
reader = Reader(client[0])
reader.setDaemon(True)
reader.start()
listener = Listener()
listener.setDaemon(True)
listener.start()
def displayfps():
while True:
global frame, lastframe
print "FPS:", frame - lastframe
lastframe = frame
time.sleep(1)
fpsthread = threading.Thread(target=displayfps)
fpsthread.setDaemon(True)
fpsthread.start()
while True:
ret, img = capture.read() # 从摄像头读取图片
cv2.imshow('Video', img) # 显示图片
key = cv2.waitKey(30) & 0xFF
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # 将图片从BGR转为RGB
image = Image.fromarray(img) # 将图片转为Image对象
buf = StringIO.StringIO() # 生成StringIO对象
image.save(buf, format="JPEG") # 将图片以JPEG格式写入StringIO
jpeg = buf.getvalue() # 获取JPEG图片内容
buf.close() # 关闭StringIO
newimg = 1
if key == 27: # ESC
break
elif key == 32: # Space
print 'File Size:', len(jpeg) / 1024, 'KB'
f = open('a.jpg', 'w')
f.write(jpeg)
f.close()
capture.release()##利用opencv实现FaceDetect
import cv2
capture = cv2.VideoCapture(0)
capture.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, 640)
capture.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, 480)
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
while True:
ret, img = capture.read()
gray = cv2.cvtColor(img, cv2.cv.CV_RGB2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
# roi = img[y:y + h, x:x + w]
cv2.imshow('Video', img)
key = cv2.waitKey(30)
key &= 0xFF
if key == 27:
break
elif key == 32:
print img
capture.release()##光流跟踪（OpticalFlow）
import numpy as np
import cv2
lk_params = {'winSize': (15, 15), 'maxLevel': 2,
'criteria': (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03)}
feature_params = {'maxCorners': 500, 'qualityLevel': 0.3, 'minDistance': 7, 'blockSize': 7}
track_len = 40
detect_interval = 1
tracks = []
cam = cv2.VideoCapture(0)
cam.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, 640)
cam.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, 480)
frame_idx = 0
while True:
ret, frame = cam.read()
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
vis = frame.copy()
if len(tracks) > 0:
img0, img1 = prev_gray, frame_gray
p0 = np.float32([tr[-1] for tr in tracks]).reshape(-1, 1, 2)
p1, st, err = cv2.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params)
p0r, st, err = cv2.calcOpticalFlowPyrLK(img1, img0, p1, None, **lk_params)
d = abs(p0 - p0r).reshape(-1, 2).max(-1)
good = d < 1
new_tracks = []
for tr, (x, y), good_flag in zip(tracks, p1.reshape(-1, 2), good):
if not good_flag:
continue
tr.append((x, y))
if len(tr) > track_len:
del tr[0]
new_tracks.append(tr)
cv2.circle(vis, (x, y), 3, (0, 255, 0), 1)
tracks = new_tracks
cv2.polylines(vis, [np.int32(tr) for tr in tracks], False, (0, 255, 0))
# cv2.imshow('track count: %d' % len(tracks), vis)
if frame_idx % detect_interval == 0:
mask = np.zeros_like(frame_gray)
mask[:] = 255
for x, y in [np.int32(tr[-1]) for tr in tracks]:
cv2.circle(mask, (x, y), 5, 0, -1)
p = cv2.goodFeaturesToTrack(frame_gray, mask=mask, **feature_params)
if p is not None:
for x, y in np.float32(p).reshape(-1, 2):
tracks.append([(x, y)])
frame_idx += 1
prev_gray = frame_gray
cv2.imshow('lk_track', vis)
ch = 0xFF & cv2.waitKey(50)
if ch == 27:
break##颜色追踪（HSV）
import cv2
import numpy as np
cap = cv2.VideoCapture(0)
cap.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, 640)
cap.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, 480)
color = np.uint8([[[111, 36, 0]]])
hsv_color = cv2.cvtColor(color, cv2.COLOR_BGR2HSV)
# define range of blue color in HSV
lower = np.array([hsv_color[0][0][0] - 10, 100, 80])
upper = np.array([hsv_color[0][0][0] + 10, 255, 255])
print lower
while True:
# Take each frame
_, frame = cap.read()
# Convert BGR to HSV
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv, lower, upper)
kernel = np.ones((5, 5), np.uint8)
# mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel) # kill the noise
# mask = cv2.medianBlur(mask, 7) # smooth
kernel = np.ones((25, 25), np.uint8)
# mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel) # fill the black
res = cv2.bitwise_and(frame, frame, mask=mask)
cv2.imshow('raw', frame)
cv2.imshow('cut', res)
k = cv2.waitKey(5) & 0xFF
if k == 27:
break
cv2.destroyAllWindows()##寻找轮廓（findContours）
空格键开始寻找
import cv2
cap = cv2.VideoCapture(0)
cap.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, 640)
cap.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, 480)
while True:
_, img = cap.read()
cv2.imshow('raw', img)
k = cv2.waitKey(5) & 0xFF
if k == 27:
break
elif k == 32:
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(img, contours, -1, (0, 0, 255), 3)
cv2.imshow("img", img)##名片矫正
MORPH_CLOSE, Contours, Transform
__author__ = 'ypw'
import cv2
import numpy as np
from matplotlib import pylab as pl
img = cv2.imread('m.jpg')
rawimg = img.copy()
# cv2.imshow('raw', img)
pl.subplot(221), pl.title('raw'), pl.imshow(cv2.cvtColor(img, cv2.cv.CV_BGR2RGB))
kernel = np.ones((20, 20), np.uint8)
img_close = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel) # kill the words
pl.subplot(222), pl.title('Close'), pl.imshow(cv2.cvtColor(img_close, cv2.cv.CV_BGR2RGB))
img = rawimg.copy()
gray = cv2.cvtColor(img_close, cv2.COLOR_BGR2GRAY)
ret, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(img, contours, -1, (0, 0, 255), 3)
cnt = contours[0]
leftmost = tuple(cnt[cnt[:, :, 0].argmin()][0])
rightmost = tuple(cnt[cnt[:, :, 0].argmax()][0])
topmost = tuple(cnt[cnt[:, :, 1].argmin()][0])
bottommost = tuple(cnt[cnt[:, :, 1].argmax()][0])
cv2.circle(img, leftmost, 8, [255, 0, 0], -1) # Blue
cv2.circle(img, rightmost, 8, [0, 255, 0], -1) # Green
cv2.circle(img, topmost, 8, [0, 0, 255], -1) # Red
cv2.circle(img, bottommost, 8, [0, 255, 255], -1) # Yellow
pl.subplot(223), pl.title('Contours'), pl.imshow(cv2.cvtColor(img, cv2.cv.CV_BGR2RGB))
print leftmost, rightmost, topmost, bottommost
def dis(p1, p2):
return np.sqrt((p1[0] - p2[0]) * (p1[0] - p2[0]) + (p1[1] - p2[1]) * (p1[1] - p2[1]))
img = rawimg.copy()
width = int(dis(leftmost, topmost))
height = int(dis(leftmost, bottommost))
pts1 = np.float32([leftmost, topmost, bottommost, rightmost])
pts2 = np.float32([[0, 0], [width, 0], [0, height], [width, height]])
M = cv2.getPerspectiveTransform(pts1, pts2)
print M
img = cv2.warpPerspective(img, M, (width, height))
pl.subplot(224), pl.title('Transform'), pl.imshow(cv2.cvtColor(img, cv2.cv.CV_BGR2RGB))
cv2.imwrite('n.jpg', img)
pl.show()##直线检测（HoughLines）
import cv2
import numpy as np
cap = cv2.VideoCapture(0)
cap.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, 640)
cap.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, 480)
while True:
_, img = cap.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray, 50, 150, apertureSize=3)
lines = cv2.HoughLines(edges, 1, np.pi / 180, 200)
print lines
if lines is not None:
for rho, theta in lines[0]:
a = np.cos(theta)
b = np.sin(theta)
x0 = a * rho
y0 = b * rho
x1 = int(x0 + 1000 * (-b))
y1 = int(y0 + 1000 * a)
x2 = int(x0 - 1000 * (-b))
y2 = int(y0 - 1000 * a)
cv2.line(img, (x1, y1), (x2, y2), (0, 0, 255), 2)
cv2.imshow('cut', img)
k = cv2.waitKey(30) & 0xFF
if k == 27:
break
cv2.destroyAllWindows()import cv2
capture = cv2.VideoCapture(0)
capture.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, 320)
capture.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, 240)
classifier = cv2.CascadeClassifier('haarcascade_fullbody.xml')
while True:
ret, img = capture.read()
gray = cv2.cvtColor(img, cv2.cv.CV_RGB2GRAY)
bodys = classifier.detectMultiScale(gray, 1.2, 5)
for (x, y, w, h) in bodys:
cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
roi = img[y:y + h, x:x + w]
cv2.imshow('body', roi)
cv2.imshow('Video', img)
key = cv2.waitKey(30)
key &= 0xFF
if key == 27:
break
elif key == 32:
print img
capture.release()import numpy as np
import cv2
from matplotlib import pyplot as plt
img1 = cv2.imread('face.png', 0) # queryImage
img2 = cv2.imread('full.png', 0) # trainImage
# Initiate SIFT detector
# sift = cv2.SIFT()
sift = cv2.xfeatures2d.SIFT_create()
# find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)
# BFMatcher with default params
bf = cv2.BFMatcher()
matches = bf.knnMatch(des1, des2, k=2)
# Apply ratio test
good = []
for m, n in matches:
if m.distance < 0.75 * n.distance:
good.append([m])
# cv2.drawMatchesKnn expects list of lists as matches.
img3 = cv2.drawMatchesKnn(img1, kp1, img2, kp2, good, img1, flags=2)
cv2.imwrite('SIFT.png', img3)
plt.imshow(img3), plt.show()