movidus sdk升级过一个大的版本，以便支持多个图在同一个棒上运行，但是它给的例子基本都还是1.0的，然后通过一个模拟接口转换过去，这显然有违接口升级的初衷。
在Migrating Python Applications from NCAPI v1 to NCAPI v2曾提及转换的要点:
1.0中的操作步骤基本是打开设备，读取图，加载图，加载张量，获取结果
而到了2.0变成了
打开设备，读取某张图，获取图对应的输入输出队列，加载张量，获取结果
这里给出2.0的例子
#coding=utf-8
#CK-Lab yanyu 2018.09.25
#initial demo with mnvc2 api
from mvnc import mvncapi
import cv2,time,os,sys
import numpy as np
from sys import argv
cv_window_name = "CKCamera"
labels = ('background',
'aeroplane', 'bicycle', 'bird', 'boat',
'bottle', 'bus', 'car', 'cat', 'chair',
'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant',
'sheep', 'sofa', 'train', 'tvmonitor')
# the ssd mobilenet image width and height
NETWORK_IMAGE_WIDTH = 300
NETWORK_IMAGE_HEIGHT = 300
# the minimal score for a box to be shown
min_score_percent = 60
# the resize_window arg will modify these if its specified on the commandline
resize_output = False
resize_output_width = 0
resize_output_height = 0
#app访问的图片保存路径
camera_dir="ckcamera"
#保存图片的序号
captured_num=0
# create a preprocessed image from the source image that complies to the
# network expectations and return it
def preprocess_image(source_image):
resized_image = cv2.resize(source_image, (NETWORK_IMAGE_WIDTH, NETWORK_IMAGE_HEIGHT))
# trasnform values from range 0-255 to range -1.0 - 1.0
resized_image = resized_image - 127.5
resized_image = resized_image * 0.007843
return resized_image
#过滤检测结果,默认的输入会有一些无效值，而且是相对坐标,这个函数负责过滤掉无效值,并且转换为绝对坐标
def filter_output(img_src,output):
num_valid_boxes = int(output[0])
objs = []
for box_index in range(num_valid_boxes):
base_index = 7+ box_index * 7
if (not np.isfinite(output[base_index]) or
not np.isfinite(output[base_index + 1]) or
not np.isfinite(output[base_index + 2]) or
not np.isfinite(output[base_index + 3]) or
not np.isfinite(output[base_index + 4]) or
not np.isfinite(output[base_index + 5]) or
not np.isfinite(output[base_index + 6])):
# boxes with non finite (inf, nan, etc) numbers must be ignored
continue
object_info=output[base_index:base_index + 7]
percentage = int(object_info[2] * 100)
if (percentage <= min_score_percent):
continue
class_id = (int)(object_info[1])
box_left = object_info[3]
box_top = object_info[4]
box_right = object_info[5]
box_bottom = object_info[6]
obj=[box_left,box_top,box_right,box_bottom,class_id,percentage,labels[class_id]]
objs.append(obj)
return objs
#展示检测结果,输入图片和过滤后的检测结果
def show_detections(img_src,objs):
source_image_width = img_src.shape[1]
source_image_height = img_src.shape[0]
display_image=img_src.copy()
for obj in objs:
box_left=(int)(obj[0] * source_image_width)
box_top=(int)(obj[1] * source_image_height)
box_right=(int)(obj[2]* source_image_width)
box_bottom=(int)(obj[3] * source_image_height)
class_id=obj[4]
percentage=obj[5]
label_text = labels[int(class_id)] + " (" + str(percentage) + "%)"
box_color = (255, 128, 0) # box color
box_thickness = 2
cv2.rectangle(display_image, (box_left, box_top), (box_right, box_bottom), box_color, box_thickness)
scale_max = (100.0 - min_score_percent)
scaled_prob = (percentage - min_score_percent)
scale = scaled_prob / scale_max
# draw the classification label string just above and to the left of the rectangle
# label_background_color = (70, 120, 70) # greyish green background for text
label_background_color = (0, int(scale * 175), 75)
label_text_color = (255, 255, 255) # white text
label_size = cv2.getTextSize(label_text, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)[0]
label_left = box_left
label_top = box_top - label_size[1]
if (label_top < 1):
label_top = 1
label_right = label_left + label_size[0]
label_bottom = label_top + label_size[1]
cv2.rectangle(display_image, (label_left - 1, label_top - 1), (label_right + 1, label_bottom + 1),
label_background_color, -1)
# label text above the box
cv2.putText(display_image, label_text, (label_left, label_bottom), cv2.FONT_HERSHEY_SIMPLEX, 0.5, label_text_color,1)
# display text to let user know how to quit
#cv2.rectangle(display_image, (0, 0), (100, 15), (128, 128, 128), -1)
#cv2.putText(display_image, "Q to Quit", (10, 12), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 0, 0), 1)
return display_image
class NCSDetection:
def __init__(self,graph_path="graph"):
device_list = mvncapi.enumerate_devices()
if len(device_list)==0:
print("No device found")
return
self.device = mvncapi.Device(device_list[0])
self.device.open()
with open(graph_path, mode='rb') as f:
graph_buffer = f.read()
self.graph = mvncapi.Graph('detection')
self.input_fifo, self.output_fifo = self.graph.allocate_with_fifos(self.device, graph_buffer)
def __del__(self):
self.close()
def detect(self,img):
input_tensor=preprocess_image(img)
self.graph.queue_inference_with_fifo_elem(self.input_fifo, self.output_fifo, input_tensor.astype(np.float32), None)
output, user_obj = self.output_fifo.read_elem()
objs=filter_output(img,output)
return objs
def close(self):
try:
self.input_fifo.destroy()
self.output_fifo.destroy()
self.graph.destroy()
self.device.close()
self.device.destroy()
except Exception as e:
print(e)
def test_one_image(detection,imgpath):
img=cv2.imread(imgpath)
objs=detection.detect(img)
show=show_detections(img,objs)
cv2.imshow("img",show)
cv2.waitKey()
def test_dir(detection,dir="unittest"):
files=os.listdir(dir)
for file in files:
filepath=dir+"/"+file
test_one_image(detection,filepath)
def test_camera(detection,index=0):
cap=cv2.VideoCapture(index)
while True:
ret,img=cap.read()
if not ret:
break
objs=detection.detect(img)
show=show_detections(img,objs)
cv2.imshow("img",show)
cv2.waitKey(1)
def main():
detection=NCSDetection()
#test_dir(detection)
test_camera(detection)
if __name__=="__main__":
main()