使用imageimq 来做这个传输图片的功能，github地址如下
https://github.com/jeffbass/imagezmq.git
我们使用清华的源来加速下载该库
pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple imagezmq
pip install pyzmq
pip install imutils
imutils
import cv2
import imagezmq
image_hub = imagezmq.ImageHub()
while True:
name , image = image_hub.recv_image()
print(image.shape[:2])
cv2.imshow(name , image)
# 处理图像
#...
key = cv2.waitKey(25)
if key == 27:
break
image_hub.send_reply(b'RET')
树莓派上写代码，树莓派我们可以读取它的摄像头来进行图片获取，然后发送给服务端，不过这个暂时真不实用，因为一个摄像头不一定有一个树莓派贵，后面我们说改进方法。
import sys
import socket
import time
import cv2
from imutils.video import VideoStream
import imagezmq
#use either of the formats below to specifiy address of display computer
#sender = imagezmq.ImageSender(connect_to='tcp://jeff-macbook:5555')
sender = imagezmq.ImageSender(connect_to='tcp://localhost:5555')
rpi_name = socket.gethostname() # send RPi hostname with each image
picam = VideoStream(usePiCamera=True).start()
time.sleep(2.0) # allow camera sensor to warm up
while True: # send images as stream until Ctrl-C
image = picam.read()
sender.send_image(rpi_name, image)
这样就可以接收树莓派摄像头的图像了，但是这样没有压缩，我们可以压缩以后再发送过去
压缩方法使用cv2的imencode方法，
ret_code, jpg_buffer = cv2.imencode(
".jpg", image, [int(cv2.IMWRITE_JPEG_QUALITY), jpeg_quality])
同时可以控制GPIO来使得LED灯来亮和灭。
import sys
import socket
import time
import traceback
import cv2
from imutils.video import VideoStream
import imagezmq
import RPi.GPIO as GPIO
#use either of the formats below to specifiy address of display computer
sender = imagezmq.ImageSender(connect_to='tcp://jeff-macbook:5555')
#sender = imagezmq.ImageSender(connect_to='tcp://192.168.1.190:5555')
#optionally, turn on the LED area lighting
use_led = true # set to True or False as needed
#optionally, filp the image vertically
flip = True # set to True of False as needed
if use_led:
GPIO.setmode(GPIO.BCM)
GPIO.setup(18, GPIO.OUT)
GPIO.output(18, True) # turn on LEDs
rpi_name = socket.gethostname() # send RPi hostname with each image
picam = VideoStream(usePiCamera=True).start()
time.sleep(2.0) # allow camera sensor to warm up
jpeg_quality = 95 # 0 to 100, higher is better quality, 95 is cv2 default
try:
while True: # send images as stream until Ctrl-C
image = picam.read()
# processing of image before sending would go here.
# for example, rotation, ROI selection, conversion to grayscale, etc.
if flip:
image = cv2.flip(image, -1)
ret_code, jpg_buffer = cv2.imencode(
".jpg", image, [int(cv2.IMWRITE_JPEG_QUALITY), jpeg_quality])
reply_from_mac = sender.send_jpg(rpi_name, jpg_buffer)
# above line shows how to capture REP reply text from Mac
except (KeyboardInterrupt, SystemExit):
pass # Ctrl-C was pressed to end program
except Exception as ex:
print('Python error with no Exception handler:')
print('Traceback error:', ex)
traceback.print_exc()
finally:
if use_led:
GPIO.output(18, False) # turn off LEDs
GPIO.cleanup() # close GPIO channel and release it
picam.stop() # stop the camera thread
sender.close() # close the ZMQ socket and context
sys.exit()
这样接收就要修改了,接收要使用numpy，np.frombuffer 函数来把数据交给cv2 的 imdecode函数来进行jpg解码，这样，程序就可以拿到BGR数据，来进行下一步数据处理。
import sys
import time
import traceback
import numpy as np
import cv2
from collections import defaultdict
from imutils.video import FPS
import imagezmq
#instantiate image_hub
image_hub = imagezmq.ImageHub()
image_count = 0
sender_image_counts = defaultdict(int) # dict for counts by sender
first_image = True
try:
while True: # receive images until Ctrl-C is pressed
sent_from, jpg_buffer = image_hub.recv_jpg()
if first_image:
fps = FPS().start() # start FPS timer after first image is received
first_image = False
image = cv2.imdecode(np.frombuffer(jpg_buffer, dtype='uint8'), -1)
# see opencv docs for info on -1 parameter
fps.update()
image_count += 1 # global count of all images received
sender_image_counts[sent_from] += 1 # count images for each RPi name
cv2.imshow(sent_from, image) # display images 1 window per sent_from
cv2.waitKey(1)
# other image processing code, such as saving the image, would go here.
# often the text in "sent_from" will have additional information about
# the image that will be used in processing the image.
image_hub.send_reply(b'OK') # REP reply
except (KeyboardInterrupt, SystemExit):
pass # Ctrl-C was pressed to end program; FPS stats computed below
except Exception as ex:
print('Python error with no Exception handler:')
print('Traceback error:', ex)
traceback.print_exc()
finally:
# stop the timer and display FPS information
print()
print('Test Program: ', __file__)
print('Total Number of Images received: {:,g}'.format(image_count))
if first_image: # never got images from any RPi
sys.exit()
fps.stop()
print('Number of Images received from each RPi:')
for RPi in sender_image_counts:
print(' ', RPi, ': {:,g}'.format(sender_image_counts[RPi]))
compressed_size = len(jpg_buffer)
print('Size of last jpg buffer received: {:,g} bytes'.format(compressed_size))
image_size = image.shape
print('Size of last image received: ', image_size)
uncompressed_size = 1
for dimension in image_size:
uncompressed_size *= dimension
print(' = {:,g} bytes'.format(uncompressed_size))
print('Compression ratio: {:.2f}'.format(compressed_size / uncompressed_size))
print('Elasped time: {:,.2f} seconds'.format(fps.elapsed()))
print('Approximate FPS: {:.2f}'.format(fps.fps()))
cv2.destroyAllWindows() # closes the windows opened by cv2.imshow()
image_hub.close() # closes ZMQ socket and context
sys.exit()
前面说到数据处理，比如使用caffe来进行图像分类，使用cv2的dnn模块来进行推理
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])
frame = imutils.resize(frame, width=400)
(h, w) = frame.shape[:2]
blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)),
0.007843, (300, 300), 127.5)
# 检测和预测
net.setInput(blob)
detections = net.forward()
# 其他代码.....
当然，我们还可以使用其他比如yolo来处理接收到的图像。
使用树莓派我们可以预处理一些图像，比如使用yolo或者tensorflow来检测一些图像，然后把检测到我们感兴趣的图像发送到服务器端进行处理，树莓派本身具有GPU，有硬件编码的功能，我们也可以使用他来发送h264或者h265编码格式，进行RTP传输，这个以后再改进，写第二篇吧