def main():
vs=VideoStream(usePiCamera=True).start()
time.sleep(1)
print('Running NCS Caffe TinyYolo example')
# Set logging level to only log errors
mvnc.global_set_option(mvnc.GlobalOption.RW_LOG_LEVEL, 3)
devices = #TODO mvnc.enumerate_devices()
if len(devices) == 0:
print('No devices found')
return 1
device = #TODO:mvnc.Device(devices[0]) use the mvnc API to assign the first device in devices to the device variable.
device.open()
#Load graph from disk and allocate graph via API
with open(tiny_yolo_graph_file, mode='rb') as f:
graph_from_disk = f.read()
graph = mvnc.Graph("Tiny Yolo Graph")
fifo_in, fifo_out = #TODO: graph.allocate_with_fifos(device, graph_file_buffer)
#may need another line here
#Instantiate fifo_in and fifo_out using the graph file above.
# Read image from file, resize it to network width and height
# save a copy in display_image for display, then convert to float32, normalize (divide by 255),
# and finally convert to convert to float16 to pass to LoadTensor as input for an inference
while True:
frame=vs.read()# Get a frame from a video stream.
input_image=frame.copy()# copy frame to an input image.
display_image = cv2.resize(input_image, (NETWORK_IMAGE_WIDTH, NETWORK_IMAGE_HEIGHT), cv2.INTER_LINEAR)
input_image = cv2.resize(input_image, (NETWORK_IMAGE_WIDTH, NETWORK_IMAGE_HEIGHT), cv2.INTER_LINEAR)
input_image = input_image.astype(np.float32)
input_image = # TODO: Scale values between 0 and 255 *= (255.0/image.max())
input_image = input_image[:, :, ::-1] # convert to RGB
#TODO: graph.queue_inference_with_fifo_elem(input_fifo, output_fifo, input_image, 'user object')
#Use the queue_inference_with_fifo_elem to load the image and get the result from the NCS. This should be one line of code.
output, userobj = fifo_out.read_elem()
# filter out all the objects/boxes that don't meet thresholds
filtered_objs = filter_objects(output.astype(np.float32), input_image.shape[1], input_image.shape[0])
print('Displaying image with objects detected in GUI')
print('Click in the GUI window and hit any key to exit')
#display the filtered objects/boxes in a GUI window
display_objects_in_gui(display_image, filtered_objs)
fifo_in.destroy()
fifo_out.destroy()
graph.destroy()
device.close()
device.destroy()
print('Finished')
def get_mvnc_device():
mvncapi.global_set_option(mvncapi.GlobalOption.RW_LOG_LEVEL, 0)
devices = mvncapi.enumerate_devices()
if (len(devices) < 1):
print("Error - no NCS devices detected")
quit()
dev = mvncapi.Device(devices[0])
try:
dev.open()
except:
print("Error - Could not open NCS device.")
quit()
return dev
def inferencer(results, frameBuffer):
graph = None
graphHandle0 = None
graphHandle1 = None
mvnc.global_set_option(mvnc.GlobalOption.RW_LOG_LEVEL, 4)
devices = mvnc.enumerate_devices()
if len(devices) == 0:
print("No NCS devices found")
sys.exit(1)
print(len(devices))
with open(join(graph_folder, "graph"), mode="rb") as f:
graph_buffer = f.read()
graph = mvnc.Graph('MobileNet-SSD')
devopen = False
for devnum in range(len(devices)):
try:
device = mvnc.Device(devices[devnum])
device.open()
graphHandle0, graphHandle1 = graph.allocate_with_fifos(
device, graph_buffer)
devopen = True
break
except:
continue
if devopen == False:
print("NCS Devices open Error!!!")
sys.exit(1)
print("Loaded Graphs!!! " + str(devnum))
while True:
try:
if frameBuffer.empty():
continue
color_image = frameBuffer.get()
prepimg = preprocess_image(color_image)
graph.queue_inference_with_fifo_elem(graphHandle0, graphHandle1,
prepimg.astype(np.float32),
color_image)
out, _ = graphHandle1.read_elem()
results.put(out)
except:
import traceback
traceback.print_exc()
def assign_ncs_devices():
'''
use the first NCS device for tiny YOLO processing, and the rest for GoogLeNet processing
'''
global ty_device
mvnc.global_set_option(mvnc.GlobalOption.RW_LOG_LEVEL, 3)
devices = mvnc.enumerate_devices()
ty_device = mvnc.Device(devices[0])
ty_device.open()
if not init_gn_lists(devices[1:]):
print('Error while initializing NCS devices for GoogLeNet')
return True
def open_ncs_device(number_of_devices=1):
# Configure the NCS verbosity reports
mvnc.global_set_option(mvnc.GlobalOption.RW_LOG_LEVEL, 3)
# Look for enumerated NCS device(s); quit program if none found.
device_list = mvnc.enumerate_devices()
if len(device_list) < number_of_devices:
messagebox.showerror("NCS Error", "Not enough devices available")
return
device1 = mvnc.Device(device_list[0])
device1.open()
if number_of_devices == 1:
return device1
elif number_of_devices == 2:
device2 = mvnc.Device(device_list[1])
device2.open()
return device1, device2
def assign_ncs_devices():
'''
use the first NCS device for tiny YOLO processing, and the rest for GoogLeNet processing
'''
global device
mvnc.global_set_option(mvnc.GlobalOption.RW_LOG_LEVEL, 3)
devices = mvnc.enumerate_devices()
if len(devices) < 1:
print('This application requires an NCS device.')
print('Insert one and try again!')
return 1
device = mvnc.Device(devices[0])
device.open()
def init():
global g_device
global g_graph
global g_fifo_in
global g_fifo_out
print('init start\n')
# Set logging level to only log errors
mvnc.global_set_option(mvnc.GlobalOption.RW_LOG_LEVEL, 3)
devices = mvnc.enumerate_devices()
if len(devices) == 0:
print('No devices found')
return 1
g_device = mvnc.Device(devices[0])
g_device.open()
#Load graph from disk and allocate graph via API
with open(tiny_yolo_graph_file, mode='rb') as f:
graph_from_disk = f.read()
g_graph = mvnc.Graph("Tiny Yolo Graph")
g_fifo_in, g_fifo_out = g_graph.allocate_with_fifos(g_device, graph_from_disk)
print('init end')
def __init__(self, _gp, _dt, _iou, _lab):
"""
Example arguments:
GRAPH_PATH = "../files/main.graph"
DETECTION_THRESHOLD = 0.20
IOU_THRESHOLD = 0.20
LABELS = {0: "bg", 1: "dandelion"}
"""
self.GRAPH_PATH = _gp
self.DETECTION_THRESHOLD = _dt
self.IOU_THRESHOLD = _iou
self.LABELS = _lab
mvnc.global_set_option(mvnc.GlobalOption.RW_LOG_LEVEL, 2)
# enumerate all devices
devices = mvnc.enumerate_devices()
if len(devices) == 0:
print("No devices found") if DEBUG else 0
quit()
# use the first device found
self.device = mvnc.Device(devices[0])
# open the device
self.device.open()
# load the model from the disk
with open(self.GRAPH_PATH, mode="rb") as f:
graph_in_memory = f.read()
self.graph = mvnc.Graph(self.GRAPH_PATH)
# create the input and output fifos
self.fifo_in, self.fifo_out = self.graph.allocate_with_fifos(
self.device, graph_in_memory)
self.cap = cv2.VideoCapture(0)
def do_initialize():
# Set logging level to only log errors
mvnc.global_set_option(mvnc.GlobalOption.RW_LOG_LEVEL, 3)
devices = mvnc.enumerate_devices()
if len(devices) == 0:
print('No devices found')
return 1
device = mvnc.Device(devices[0])
device.open()
graph_filename = "inference.graph"
# Load graph file
try:
with open(graph_filename, mode='rb') as f:
in_memory_graph = f.read()
except:
print("Error reading graph file: " + graph_filename)
graph = mvnc.Graph("mnist graph")
fifo_in, fifo_out = graph.allocate_with_fifos(device, in_memory_graph)
return device, graph, fifo_in, fifo_out
def __init__(self):
# set the logging level for the NC API
mvncapi.global_set_option(mvncapi.GlobalOption.RW_LOG_LEVEL,
mvncapi.LogLevel.DEBUG)
# get a list of names for all the devices plugged into the system
device_list = mvncapi.enumerate_devices()
if not device_list:
raise Exception("Error - No neural compute devices detected.")
else:
print(len(device_list), "neural compute devices found!")
# Get a list of valid device identifiers
device_list = mvncapi.enumerate_devices()
# Create a Device instance for the first device found
self._device = mvncapi.Device(device_list[0])
# Open communication with the device
# try to open the device. this will throw an exception if someone else
# has it open already
try:
self._device.open()
print("Hello NCS! Device opened normally.")
except Exception:
raise Exception("Error - Could not open NCS device.")
def main():
"""Main function for the program. Everything starts here.
:return: None
"""
global resize_output, resize_output_width, resize_output_height, \
obj_detector_proc, resize_output, resize_output_width, resize_output_height, video_proc, \
last_num_persons, asyncImWriter
if (not handle_args()):
print_usage()
return 1
# Set logging level to only log errors
mvnc.global_set_option(mvnc.GlobalOption.RW_LOG_LEVEL, 3)
devices = mvnc.enumerate_devices()
if len(devices) < 1:
print('No NCS device detected.')
print('Insert device and try again!')
return 1
# Pick the first stick to run the network
# use the first NCS device that opens for the object detection.
dev_count = 0
for one_device in devices:
try:
obj_detect_dev = mvnc.Device(one_device)
obj_detect_dev.open()
print("opened device " + str(dev_count))
break
except:
print("Could not open device " + str(dev_count) +
", trying next device")
pass
dev_count += 1
cv2.namedWindow(cv_window_name)
cv2.moveWindow(cv_window_name, 10, 10)
cv2.waitKey(1)
obj_detector_proc = SsdMobileNetProcessor(
NETWORK_GRAPH_FILENAME,
obj_detect_dev,
inital_box_prob_thresh=min_score_percent / 100.0,
classification_mask=object_classifications_mask)
exit_app = False
while (True):
# video processor that will put video frames images on the object detector's input FIFO queue
video_proc = CameraProcessor(CAMERA_INDEX,
1920,
1080,
network_processor=obj_detector_proc)
video_proc.start_processing()
frame_count = 0
start_time = time.time()
end_time = start_time
while (True):
try:
(filtered_objs, display_image
) = obj_detector_proc.get_async_inference_result()
except:
print("exception caught in main")
raise
# check if the window is visible, this means the user hasn't closed
# the window via the X button
prop_val = cv2.getWindowProperty(cv_window_name,
cv2.WND_PROP_ASPECT_RATIO)
if (prop_val < 0.0):
end_time = time.time()
video_proc.stop_processing()
exit_app = True
break
agg_results = overlay_on_image(display_image, filtered_objs)
num_persons = len(agg_results)
if (show_output):
if (resize_output):
display_image = cv2.resize(
display_image,
(resize_output_width, resize_output_height),
cv2.INTER_LINEAR)
cv2.imshow(cv_window_name, display_image)
raw_key = cv2.waitKey(1)
if (raw_key != -1):
if (handle_keys(raw_key, obj_detector_proc) == False):
end_time = time.time()
exit_app = True
video_proc.stop_processing()
break
frame_count += 1
# if (obj_detector_proc.is_input_queue_empty()):
# end_time = time.time()
# print('Neural Network Processor has nothing to process, assuming video is finished.')
# break
frames_per_second = frame_count / (end_time - start_time)
print('Frames per Second: ' + str(frames_per_second))
throttling = obj_detect_dev.get_option(
mvnc.DeviceOption.RO_THERMAL_THROTTLING_LEVEL)
if (throttling > 0):
print("\nDevice is throttling, level is: " + str(throttling))
print("Sleeping for a few seconds....")
cv2.waitKey(2000)
#video_proc.stop_processing()
cv2.waitKey(1)
if (exit_app):
video_proc.cleanup()
break
# Clean up the graph and the device
obj_detector_proc.cleanup()
obj_detect_dev.close()
obj_detect_dev.destroy()
cv2.destroyAllWindows()
from mvnc import mvncapi as mvnc
import sys
import argparse
import os
import numpy as np
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
mvnc.global_set_option(mvnc.GlobalOption.RW_LOG_LEVEL, mvnc.LogLevel.DEBUG)
def process_args():
parser = argparse.ArgumentParser()
parser.add_argument("--graph", required=True, help="graph file")
parser.add_argument("--input",
required=True,
help="path to a input bin file")
parser.add_argument("--output_dir",
required=True,
help="where to put output file")
return parser.parse_args()
def load_input_tensor(file):
#Load data
inputs = np.fromfile(file, dtype=np.float32)
inputs = np.reshape(inputs, [2, 1024])
def main():
print("Loading system...")
mvnc.global_set_option(mvnc.GlobalOption.RW_LOG_LEVEL, 2)
# enumerate all devices
devices = mvnc.enumerate_devices()
if len(devices) == 0:
print("No devices found")
quit()
# use the first device found
device = mvnc.Device(devices[0])
# open the device
device.open()
# load the model from the disk
with open(GRAPH_PATH, mode="rb") as f:
graph_in_memory = f.read()
graph = mvnc.Graph(GRAPH_PATH)
# create the input and output fifos
fifo_in, fifo_out = graph.allocate_with_fifos(device, graph_in_memory)
cap = cv2.VideoCapture(0)
print("Starting capture...")
try:
while True:
global start_time
start_time = time.time()
# read an image in bgr format
ret, img = cap.read()
original_img = img
# bgr input scaling
img = np.divide(img, 255.0)
resized_img = cv2.resize(img, (416, 416), cv2.INTER_LINEAR)
# transpose the image to rgb
resized_img = resized_img[:, :, ::-1]
resized_img = resized_img.astype(np.float32)
# make an inference
graph.queue_inference_with_fifo_elem(fifo_in, fifo_out,
resized_img, "user object")
# get the result
output, userobj = fifo_out.read_elem()
# Tiny Yolo V2 requires post processing to filter out duplicate objects and low score objects
# After post processing, the app will display the image and any detected objects
post_processing(output, original_img)
except KeyboardInterrupt:
print("Closing, please wait...")
pass
# clean up
cv2.destroyAllWindows()
cv2.VideoCapture(0).release()
fifo_in.destroy()
fifo_out.destroy()
graph.destroy()
device.close()
device.destroy()
print("All done!")
class ObjectWrapper():
# open device
mvnc.global_set_option(mvnc.GlobalOption.RW_LOG_LEVEL, 2)
devices = mvnc.enumerate_devices()
devNum = len(devices) # will have more than one probably
print("Number of Movidius Sticks detected : ", devNum)
if len(devices) == 0:
print('No MVNC devices found')
quit()
devHandle = [] # used as device list - store devices
graphHandle = [] # used as graph list - store graphs
inputHandle = []
outputHandle = []
def __init__(self, graphfile):
select = 1
self.detector = YoloDetector(select)
for i in range(
ObjectWrapper.devNum): # will loop for each device detected
ObjectWrapper.devHandle.append(
mvnc.Device(ObjectWrapper.devices[i])
) # pass in list of devices, append that device to device list.
ObjectWrapper.devHandle[i].open() # open that device.
#opt = ObjectWrapper.devHandle[i].GetDeviceOption(mvnc.DeviceOption.OPTIMISATION_LIST)
# load blob
with open(graphfile, mode='rb') as f:
blob = f.read()
graph = mvnc.Graph('graph1') # creates a graph instance
# Allocate the graph and store to array
#ObjectWrapper.graphHandle.append(graph.allocate(ObjectWrapper.devHandle[i], blob))
input_fifo, output_fifo = graph.allocate_with_fifos(
ObjectWrapper.devHandle[i], blob)
ObjectWrapper.graphHandle.append(graph)
ObjectWrapper.inputHandle.append(input_fifo)
ObjectWrapper.outputHandle.append(output_fifo)
self.dim = (416, 416)
self.blockwd = 12
self.wh = self.blockwd * self.blockwd
self.targetBlockwd = 13
self.classes = 1
self.threshold = 0.2
self.nms = 0.4
#def __del__(self):
def PrepareImage(self, img, dim):
imgw = img.shape[1]
imgh = img.shape[0]
imgb = np.empty((dim[0], dim[1], 3))
imgb.fill(0.5)
if imgh / imgw > dim[1] / dim[0]:
neww = int(imgw * dim[1] / imgh)
newh = dim[1]
else:
newh = int(imgh * dim[0] / imgw)
neww = dim[0]
offx = int((dim[0] - neww) / 2)
offy = int((dim[1] - newh) / 2)
imgb[offy:offy + newh,
offx:offx + neww, :] = resize(img.copy() / 255.0, (newh, neww), 1)
im = imgb[:, :, (2, 1, 0)]
return im, int(offx * imgw / neww), int(
offy * imgh / newh), neww / dim[0], newh / dim[1]
def Reshape(self, out, dim):
shape = out.shape
out = np.transpose(out.reshape(self.wh, int(shape[0] / self.wh)))
out = out.reshape(shape)
return out
def Detect(self, img):
print("DOING SINGLE DETECT")
imgw = img.shape[1]
imgh = img.shape[0]
im, offx, offy, xscale, yscale = self.PrepareImage(img, self.dim)
####Edit
ObjectWrapper.graphHandle[0].queue_inference_with_fifo_elem(
ObjectWrapper.inputHandle[0], ObjectWrapper.outputHandle[0],
im.astype(np.float32), 'user object')
out, userobj = ObjectWrapper.outputHandle[0].read_elem(
) # Get result from output queue
####
out = self.Reshape(out, self.dim)
internalresults = self.detector.Detect(out.astype(np.float32),
int(out.shape[0] / self.wh),
self.blockwd, self.blockwd,
self.classes, imgw, imgh,
self.threshold, self.nms,
self.targetBlockwd)
pyresults = [
BBox(x, xscale, yscale, offx, offy) for x in internalresults
]
print(pyresults)
return pyresults
def Parallel(self, img):
print("DOING PARALLEL")
pyresults = {}
for i in range(ObjectWrapper.devNum):
im, offx, offy, w, h = self.PrepareImage(img[i], self.dim)
# Edit
ObjectWrapper.graphHandle[i].queue_inference_with_fifo_elem(
ObjectWrapper.inputHandle[i], ObjectWrapper.outputHandle[0],
im.astype(np.float32), 'user object')
#ObjectWrapper.graphHandle[i].LoadTensor(im.astype(np.float16), 'user object')
for i in range(ObjectWrapper.devNum):
# Edit
out, userobj = ObjectWrapper.outputHandle[i].read_elem(
) # Get result from output queue
#out, userobj = ObjectWrapper.graphHandle[i].GetResult()
out = self.Reshape(out, self.dim)
imgw = img[i].shape[1]
imgh = img[i].shape[0]
internalresults = self.detector.Detect(out.astype(np.float32),
int(out.shape[0] / self.wh),
self.blockwd, self.blockwd,
self.classes, imgw, imgh,
self.threshold, self.nms,
self.targetBlockwd)
res = [BBox(x, w, h, offx, offy) for x in internalresults]
if i not in pyresults:
pyresults[i] = res
return pyresults
class ObjectWrapper():
mvnc.global_set_option(mvnc.GlobalOption.RW_LOG_LEVEL, 2)
devices = mvnc.enumerate_devices()
devNum = len(devices)
if len(devices) == 0:
print('No MVNC devices found')
quit()
devHandle = []
graphHandle = []
fifoInHandle = []
fifoOutHandle = []
def __init__(self, graphfile):
select = 1
self.detector = YoloDetector(select)
for i in range(ObjectWrapper.devNum):
ObjectWrapper.devHandle.append(
mvnc.Device(ObjectWrapper.devices[i])) ##------get devices
ObjectWrapper.devHandle[i].open() ##------ open device_i
# load blob
with open(graphfile, mode='rb') as f:
blob = f.read()
# create graph instance
ObjectWrapper.graphHandle.append(mvnc.Graph('inst' + str(i)))
# allocate resources
fifoIn, fifoOut = ObjectWrapper.graphHandle[i].allocate_with_fifos(
ObjectWrapper.devHandle[i], blob)
ObjectWrapper.fifoInHandle.append(fifoIn)
ObjectWrapper.fifoOutHandle.append(fifoOut)
if (graphfile.endswith('416')):
self.dim = (416, 416)
elif (graphfile.endswith('288')):
self.dim = (288, 288)
elif (graphfile.endswith('352')):
self.dim = (352, 352)
else:
self.dim = (416, 416)
self.blockwd = int(self.dim[0] / 32)
self.wh = self.blockwd * self.blockwd
self.targetBlockwd = int(self.dim[0] / 32)
self.classes = 6
self.threshold = 0.35
self.nms = 0.45
def __del__(self):
for i in range(ObjectWrapper.devNum):
ObjectWrapper.fifoInHandle[i].destroy()
ObjectWrapper.fifoOutHandle[i].destroy()
ObjectWrapper.graphHandle[i].destroy()
ObjectWrapper.devHandle[i].close()
def PrepareImage(self, img, dim):
'''
imgw = img.shape[1]
imgh = img.shape[0]
imgb = np.empty((dim[0], dim[1], 3))
imgb.fill(0.5)
if imgh/imgw > dim[1]/dim[0]:
neww = int(imgw * dim[1] / imgh)
newh = dim[1]
else:
newh = int(imgh * dim[0] / imgw)
neww = dim[0]
offx = int((dim[0] - neww)/2)
offy = int((dim[1] - newh)/2)
imgb[offy:offy+newh,offx:offx+neww,:] = resize(img.copy()/255.0,(newh,neww),1)
im = imgb[:,:,(2,1,0)]
'''
imgw = img.shape[1]
imgh = img.shape[0]
imgb = np.empty((dim[0], dim[1], 3))
imgb.fill(0.5)
#neww = 416
#newh = 416
neww = dim[0]
newh = dim[1]
offx = int((dim[0] - neww) / 2)
offy = int((dim[1] - newh) / 2)
start_time = datetime.now()
imgb[offy:offy + newh,
offx:offx + neww, :] = resize(img.copy() / 255.0, (newh, neww), 1)
#imgb[offy:offy+newh,offx:offx+neww,:] = resize(img/255.0,(newh,neww),1)
#imgb[offy:offy+newh,offx:offx+neww,:] = cv2.resize(img/255.0, (newh, neww))
print(datetime.now() - start_time)
im = imgb[:, :, (2, 1, 0)]
#im = imgb
return im, int(offx * imgw / neww), int(
offy * imgh / newh), neww / dim[0], newh / dim[1]
def Reshape(self, out, dim):
shape = out.shape
out = np.transpose(out.reshape(self.wh, int(shape[0] / self.wh)))
out = out.reshape(shape)
return out
def non_max_suppress(self, predicts_dict, threshold=0.3):
"""
implement non-maximum supression on predict bounding boxes.
Args:
predicts_dict: {"stick": [[x1, y1, x2, y2, scores1], [...]]}.
threshhold: iou threshold
Return:
predicts_dict processed by non-maximum suppression
"""
for object_name, bbox in predicts_dict.items(): #对每一个类别的目标分别进行NMS
#if(len(bbox)<2):
#continue
bbox_array = np.array(
bbox, dtype=np.float
) ## 获取当前目标类别下所有矩形框（bounding box,下面简称bbx）的坐标和confidence,并计算所有bbx的面积
#print('bbox_array:{0}'.format(bbox_array))
x1, y1, x2, y2, scores = bbox_array[:,
0], bbox_array[:,
1], bbox_array[:,
2], bbox_array[:,
3], bbox_array[:,
4]
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
#print "areas shape = ", areas.shape
## 对当前类别下所有的bbx的confidence进行从高到低排序（order保存索引信息）
order = scores.argsort()[::-1]
#print ("order = {0}".format(order))
keep = [
] #用来存放最终保留的bbx的索引信息 ## 依次从按confidence从高到低遍历bbx，移除所有与该矩形框的IOU值大于threshold的矩形框
while order.size > 0:
i = order[0]
keep.append(
i
) #保留当前最大confidence对应的bbx索引 ## 获取所有与当前bbx的交集对应的左上角和右下角坐标，并计算IOU（注意这里是同时计算一个bbx与其他所有bbx的IOU）
xx1 = np.maximum(x1[i], x1[
order[1:]]) #当order.size=1时，下面的计算结果都为np.array([]),不影响最终结果
yy1 = np.maximum(y1[i], y1[order[1:]])
xx2 = np.minimum(x2[i], x2[order[1:]])
yy2 = np.minimum(y2[i], y2[order[1:]])
inter = np.maximum(0.0, xx2 - xx1 + 1) * np.maximum(
0.0, yy2 - yy1 + 1)
iou = inter / (areas[i] + areas[order[1:]] - inter)
#print("iou = {0}".format(iou))
#print(np.where(iou<=threshold)) #输出没有被移除的bbx索引（相对于iou向量的索引）
indexs = np.where(
iou <= threshold
)[0] + 1 #获取保留下来的索引(因为没有计算与自身的IOU，所以索引相差１，需要加上)
#print ("indexs = {0}".format(type(indexs)))
order = order[indexs] #更新保留下来的索引
#print ("order = {0}".format(order))
bbox = bbox_array[keep]
predicts_dict[object_name] = bbox.tolist()
#predicts_dict = predicts_dict
return predicts_dict
def non_max_suppress_(self,
predicts_dict,
nms_tuple=(3, 5),
threshold=0.7):
has_key1 = False
has_key2 = False
for key, value in predicts_dict.items():
if (key == nms_tuple[0]):
has_key1 = True
elif (key == nms_tuple[1]):
has_key2 = True
if ((has_key1 == True) and (has_key2 == True)):
bbx_array = np.array(predicts_dict[nms_tuple[1]], dtype=np.float)
x1, y1, x2, y2, scores = bbx_array[:,
0], bbx_array[:,
1], bbx_array[:,
2], bbx_array[:,
3], bbx_array[:,
4]
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
keep = []
for bbx in predicts_dict[nms_tuple[0]]:
xx1 = np.maximum(bbx[0], x1)
yy1 = np.maximum(bbx[1], y1)
xx2 = np.minimum(bbx[2], x2)
yy2 = np.minimum(bbx[3], y2)
inter = np.maximum(0.0, xx2 - xx1 + 1) * np.maximum(
0.0, yy2 - yy1 + 1)
iou = inter / ((bbx[2] - bbx[0] + 1) *
(bbx[3] - bbx[1] + 1) + areas - inter)
print('iou:{0}'.format(iou))
print('keep:{0}'.format(
np.where(iou <= threshold))) #输出没有被移除的bbx索引（相对于iou向量的索引）
indexs = np.where(iou > threshold)[0] #获取保留下来的索引
print('keep index:{0}'.format(indexs))
keep.append(indexs)
print('keep:{0}'.format(keep))
#bbox = bbox_array[keep]
#predicts_dict[object_name] = bbox.tolist()
#predicts_dict = predicts_dict
#return predicts_dict
def Detect(self, img, idx=0):
"""Send image for inference on a single compute stick
Args:
img: openCV image type
idx: index of the compute stick to use for inference
Returns:
[<BBox>]: array of BBox type objects for each result in the detection
"""
imgw = img.shape[1]
imgh = img.shape[0]
im, offx, offy, xscale, yscale = self.PrepareImage(img, self.dim)
#print('xscale = {}, yscale = {}'.format(xscale, yscale))
ObjectWrapper.graphHandle[idx].queue_inference_with_fifo_elem(
ObjectWrapper.fifoInHandle[idx], ObjectWrapper.fifoOutHandle[idx],
im.astype(np.float32), 'user object')
out, userobj = ObjectWrapper.fifoOutHandle[idx].read_elem()
###################################################################
'''
reshaped_out = out.reshape(13, 165, 13)
transposed_out = np.transpose(reshaped_out, (2, 0, 1))
'''
reshaped_out = out.reshape(self.blockwd, 165, self.blockwd)
transposed_out = np.transpose(reshaped_out, (2, 0, 1))
###################################################################
transposed_out = transposed_out.reshape(165, self.blockwd,
self.blockwd)
first_132 = transposed_out[:132]
first_132 = first_132.reshape(33, self.blockwd * 2, self.blockwd * 2)
last_33 = transposed_out[132:]
#print('layer23-conv:\n{0}'.format(first_132))
#print('layer16-conv:\n{0}'.format(last_33))
###################################################################
###out = self.Reshape(out, self.dim)
out1 = last_33.reshape(self.blockwd * self.blockwd * 33)
internalresults1 = self.detector.Detect(out1.astype(np.float32), 33,
self.blockwd, self.blockwd,
self.classes, imgw, imgh,
self.threshold, self.nms,
self.targetBlockwd)
pyresults1 = [
BBox(x, xscale, yscale, offx, offy) for x in internalresults1
]
out2 = first_132.reshape(self.blockwd * 2 * self.blockwd * 2 * 33)
internalresults2 = self.detector.Detect(out2.astype(np.float32), 33,
self.blockwd * 2,
self.blockwd * 2, self.classes,
imgw, imgh, self.threshold,
self.nms, self.blockwd * 2)
pyresults2 = [
BBox(x, xscale, yscale, offx, offy) for x in internalresults2
]
pyresults3 = pyresults1 + pyresults2
#return pyresults3
pre_dic = {}
list_all = []
for i in np.arange(6):
list_temp = []
for bbx in pyresults3:
if (bbx.objType == i):
list_temp.append([
bbx.left, bbx.top, bbx.right, bbx.bottom,
bbx.confidence
])
#print(list_temp)
if (len(list_temp) == 0):
continue
else:
pre_dic[i] = list_temp
#--list_all.append(list_temp)
#--list_key = np.arange(6)
#--predict_dicts = dict(zip(list_key, list_all))
#--print('predict_dicts:{0}'.format(predict_dicts))
#print('pre_dic:{0}'.format(pre_dic))
nms_pred_dict = self.non_max_suppress(pre_dic)
#print('nmsed_dict:{0}'.format(nms_pred_dict))
if (nms_pred_dict == None):
return []
##-------------------------test start
#self.non_max_suppress_(nms_pred_dict)
##-------------------------test end
nmsed_between_layer_results = []
for object_id, bboxes in nms_pred_dict.items():
#print('object_id:{0}'.format(object_id))
#print('bboxes:{0}'.format(bboxes))
for bbox in bboxes:
bbox.append(object_id)
#print('bbox:{0}'.format(bbox))
BBox__ = BBox_(bbox, xscale, yscale, offx, offy)
nmsed_between_layer_results.append(BBox__)
return nmsed_between_layer_results
'''out1 = last_33.reshape(13*13*33)
#internalresults1 = self.detector.Detect(out1.astype(np.float32), 33, self.blockwd, self.blockwd, self.classes, imgw, imgh, self.threshold, self.nms, self.targetBlockwd)
#pyresults1 = [BBox(x,xscale,yscale, offx, offy) for x in internalresults1]
out2 = first_132.reshape(26*26*33)
internalresults2 = self.detector.Detect(out2.astype(np.float32), 33, 26, 26, self.classes, imgw, imgh, self.threshold, self.nms, 26)
pyresults2 = [BBox(x,xscale,yscale, offx, offy) for x in internalresults2]
pyresults3 = pyresults1 + pyresults2
return pyresults3'''
def Parallel(self, img):
"""Send array of images for inference on multiple compute sticks
Args:
img: array of images to run inference on
Returns:
{ <int>:[<BBox] }: A dict with key-value pairs mapped to compute stick device numbers and arrays of the detection boxs (BBox)
"""
pyresults = {}
for i in range(ObjectWrapper.devNum):
im, offx, offy, w, h = self.PrepareImage(img[i], self.dim)
ObjectWrapper.graphHandle[i].queue_inference_with_fifo_elem(
ObjectWrapper.fifoInHandle[i], ObjectWrapper.fifoOutHandle[i],
im.astype(np.float32), 'user object')
for i in range(ObjectWrapper.devNum):
out, userobj = ObjectWrapper.fifoOutHandle[i].read_elem()
out = self.Reshape(out, self.dim)
imgw = img[i].shape[1]
imgh = img[i].shape[0]
internalresults = self.detector.Detect(out.astype(np.float32),
int(out.shape[0] / self.wh),
self.blockwd, self.blockwd,
self.classes, imgw, imgh,
self.threshold, self.nms,
self.targetBlockwd)
res = [BBox(x, w, h, offx, offy) for x in internalresults]
if i not in pyresults:
pyresults[i] = res
return pyresults
def main():
print('test start')
func()
return
print('Running NCS Caffe TinyYolo example')
# Set logging level to only log errors
mvnc.global_set_option(mvnc.GlobalOption.RW_LOG_LEVEL, 3)
devices = mvnc.enumerate_devices()
if len(devices) == 0:
print('No devices found')
return 1
device = mvnc.Device(devices[0])
device.open()
#Load graph from disk and allocate graph via API
with open(tiny_yolo_graph_file, mode='rb') as f:
graph_from_disk = f.read()
graph = mvnc.Graph("Tiny Yolo Graph")
fifo_in, fifo_out = graph.allocate_with_fifos(device, graph_from_disk)
# Read image from file, resize it to network width and height
# save a copy in display_image for display, then convert to float32, normalize (divide by 255),
# and finally convert to convert to float16 to pass to LoadTensor as input for an inference
cv_window_name='123'
while (True):
video_device = cv2.VideoCapture("./" + input_image_file)
actual_frame_width = video_device.get(cv2.CAP_PROP_FRAME_WIDTH)
actual_frame_height = video_device.get(cv2.CAP_PROP_FRAME_HEIGHT)
print ('actual video resolution: ' + str(actual_frame_width) + ' x ' + str(actual_frame_height))
# if ((video_device == None) or (not video_device.isOpened())):
# print ('Could not open video device. Make sure file exists:')
# print ('file name:' + input_video_file)
# print ('Also, if you installed python opencv via pip or pip3 you')
# print ('need to uninstall it and install from source with -D WITH_V4L=ON')
# print ('Use the provided script: install-opencv-from_source.sh')
# frame_count = 0
# start_time = time.time()
#frame_count = frame_count + 1
#frames_per_second = frame_count / (end_time - start_time)
while True :
print ('Use the provided script: install-opencv-from_source.sh')
time1 = time.time()
ret_val, input_image = video_device.read()
time2 = time.time()
start_time = time.time()
display_image = input_image
input_image = cv2.resize(input_image, (NETWORK_IMAGE_WIDTH, NETWORK_IMAGE_HEIGHT), cv2.INTER_LINEAR)
input_image = input_image.astype(np.float32)
input_image = np.divide(input_image, 255.0)
input_image = input_image[:, :, ::-1] # convert to RGB
time3 = time.time()
# Load tensor and get result. This executes the inference on the NCS
graph.queue_inference_with_fifo_elem(fifo_in, fifo_out, input_image.astype(np.float32), None)
output, userobj = fifo_out.read_elem()
# filter out all the objects/boxes that don't meet thresholds
filtered_objs = filter_objects(output.astype(np.float32), input_image.shape[1], input_image.shape[0])
print('Displaying image with objects detected in GUI')
print('Click in the GUI window and hit any key to exit')
#display the filtered objects/boxes in a GUI window
display_objects_in_gui(display_image, filtered_objs)
time4 = time.time()
end_time = time.time()
time_used=end_time-start_time
print("time used : "+ str(time_used)+"time point:"+str(time1)+" "+str(time2)+" "+str(time3)+" "+str(time4))
cv2.imshow(cv_window_name, input_image)
#raw_key = cv2.waitKey(1)
if (not ret_val):
# end_time = time.time()
print("No image from from video device, exiting")
break
# resize image to network width and height
# then convert to float32, normalize (divide by 255),
# and finally convert to float16 to pass to LoadTensor as input
# for an inference
# input_image = cv2.resize(input_image, (TY_NETWORK_IMAGE_WIDTH, TY_NETWORK_IMAGE_HEIGHT), cv2.INTER_LINEAR)
# save a display image as read from video device.
# close video device
video_device.release()
input_image = cv2.imread(input_image_file)
display_image = input_image
input_image = cv2.resize(input_image, (NETWORK_IMAGE_WIDTH, NETWORK_IMAGE_HEIGHT), cv2.INTER_LINEAR)
input_image = input_image.astype(np.float32)
input_image = np.divide(input_image, 255.0)
input_image = input_image[:, :, ::-1] # convert to RGB
# Load tensor and get result. This executes the inference on the NCS
graph.queue_inference_with_fifo_elem(fifo_in, fifo_out, input_image.astype(np.float32), None)
output, userobj = fifo_out.read_elem()
# filter out all the objects/boxes that don't meet thresholds
filtered_objs = filter_objects(output.astype(np.float32), input_image.shape[1], input_image.shape[0])
print('Displaying image with objects detected in GUI')
print('Click in the GUI window and hit any key to exit')
#display the filtered objects/boxes in a GUI window
display_objects_in_gui(display_image, filtered_objs)
fifo_in.destroy()
fifo_out.destroy()
graph.destroy()
device.close()
device.destroy()
print('Finished')
def inferencer(results, frameBuffer, ssd_detection_mode, face_detection_mode, devnum, mp_active_stick_number, mp_stick_temperature):
graphs = []
graph_buffers = []
graphHandles = []
graphHandle0 = None
graphHandle1 = None
mvnc.global_set_option(mvnc.GlobalOption.RW_LOG_LEVEL, 4)
devices = mvnc.enumerate_devices()
if len(devices) == 0:
print("No NCS devices found")
sys.exit(1)
print(len(devices))
# 1:= Enabled MobileNet-SSD Model
if ssd_detection_mode == 1:
with open(join(graph_folder, "graph"), mode="rb") as f:
graph_buffers.append(f.read())
graphs.append(mvnc.Graph('MobileNet-SSD'))
# 1:= Enabled Fullweight FaceDetection Model
if face_detection_mode == 1:
with open(join(graph_folder, "graph.fullfacedetection"), mode="rb") as f:
graph_buffers.append(f.read())
graphs.append(mvnc.Graph('FullFaceDetection'))
# 2:= Enabled Lightweight FaceDetection Model
if face_detection_mode == 2:
with open(join(graph_folder, "graph.shortfacedetection"), mode="rb") as f:
graph_buffers.append(f.read())
graphs.append(mvnc.Graph('ShortFaceDetection'))
devopen = False
for device in devices:
try:
device = mvnc.Device(device)
device.open()
for (graph, graph_buffer) in zip(graphs, graph_buffers):
graphHandles.append(graph.allocate_with_fifos(device, graph_buffer))
devopen = True
break
except:
continue
if devopen == False:
print("NCS Devices open Error!!!")
sys.exit(1)
print("Loaded Graphs!!! ")
THERMAL_STATS = mvnc.DeviceOption.RO_THERMAL_STATS
temperature = device.get_option
while True:
# 0:= Inactive stick, 1:= Active stick
if mp_active_stick_number[devnum] == 0:
continue
# Measure the temperature inside the stick
mp_stick_temperature[devnum] = temperature(THERMAL_STATS)[0]
try:
if frameBuffer.empty():
continue
color_image = frameBuffer.get()
prepimg = preprocess_image(color_image)
res = None
for (graph, graphHandle) in zip(graphs, graphHandles):
graphHandle0 = graphHandle[0]
graphHandle1 = graphHandle[1]
graph.queue_inference_with_fifo_elem(graphHandle0, graphHandle1, prepimg.astype(np.float32), None)
out, _ = graphHandle1.read_elem()
num_valid_boxes = int(out[0])
if num_valid_boxes > 0:
if isinstance(res, type(None)):
res = [out]
else:
res = np.append(res, [out], axis=0)
results.put(res)
except:
import traceback
traceback.print_exc()
def main():
# 設定程式參數
arg_parser = argparse.ArgumentParser(description='使用 Movidius 進行預測')
arg_parser.add_argument(
'--graph-file',
required=True,
help='Movidius 模型檔',
)
arg_parser.add_argument(
'--video-type',
choices=['file', 'camera'],
default='camera',
help='影片類型',
)
arg_parser.add_argument(
'--source',
default='/dev/video0',
help='影片來源檔',
)
arg_parser.add_argument(
'--input-width',
type=int,
default=48,
help='模型輸入影像寬度',
)
arg_parser.add_argument(
'--input-height',
type=int,
default=48,
help='模型輸入影像高度',
)
arg_parser.add_argument(
'--gui',
action='store_true',
help='啓用圖像界面',
)
# 解讀程式參數
args = arg_parser.parse_args()
assert args.input_width > 0 and args.input_height > 0
# 設置 Movidius 裝置
mvnc.global_set_option(mvnc.GlobalOption.RW_LOG_LEVEL, 2)
mvnc_devices = mvnc.enumerate_devices()
if not mvnc_devices:
print('找不到 Movidius 裝置')
exit(1)
mvnc_dev = mvnc.Device(mvnc_devices[0])
mvnc_dev.open()
# 載入模型檔
try:
with open(args.graph_file, mode='rb') as file_graph:
graph_buffer = file_graph.read()
except (FileNotFoundError, IOError):
print('無法載入模型檔')
exit(1)
graph = mvnc.Graph('graph')
fifo_in, fifo_out = graph.allocate_with_fifos(mvnc_dev, graph_buffer)
# 開啓影片來源
if args.video_type == 'file': # 檔案
video_dev = cv2.VideoCapture(args.source)
video_width = video_dev.get(cv2.CAP_PROP_FRAME_WIDTH)
video_height = video_dev.get(cv2.CAP_PROP_FRAME_HEIGHT)
elif args.video_type == 'camera': # 攝影機
video_dev = cv2.VideoCapture(0)
# 主迴圈
try:
prev_timestamp = time.time()
while True:
ret, orig_image = video_dev.read()
curr_time = time.localtime()
# 檢查串流是否結束
if ret is None or orig_image is None:
break
# 縮放爲模型輸入的維度、調整數字範圍爲 0～1 之間的數值
resized_image = cv2.resize(
orig_image,
(args.input_width, args.input_height),
).astype(np.float32)
normalized_image = resized_image / 255.0
# 執行預測
graph.queue_inference_with_fifo_elem(
fifo_in,
fifo_out,
normalized_image,
None,
)
result_onehot, _ = fifo_out.read_elem()
left_score, right_score, stop_score, other_score = result_onehot
class_id = np.argmax(result_onehot)
if class_id == 0:
class_str = 'left'
elif class_id == 1:
class_str = 'right'
elif class_id == 2:
class_str = 'stop'
elif class_id == 3:
class_str = 'other'
# 計算執行時間
recent_timestamp = time.time()
period = recent_timestamp - prev_timestamp
prev_timestamp = recent_timestamp
print('時間：%02d:%02d:%02d ' %
(curr_time.tm_hour, curr_time.tm_min, curr_time.tm_sec))
print('輸出：%.2f %.2f %.2f %.2f' %
(left_score, right_score, stop_score, other_score))
print('類別：%s' % class_str)
print('費時：%f' % period)
print()
# 顯示圖片
if args.gui:
cv2.imshow('', orig_image)
cv2.waitKey(1)
except KeyboardInterrupt:
print('使用者中斷')
# 終止影像裝置
video_dev.release()
# 終止 Movidius 裝置
fifo_in.destroy()
fifo_out.destroy()
graph.destroy()
mvnc_dev.close()
mvnc_dev.destroy()
def main():
print('Running NCS Caffe TinyYolo example')
# Set logging level to only log errors
mvnc.global_set_option(mvnc.GlobalOption.RW_LOG_LEVEL, 3)
devices = mvnc.enumerate_devices()
if len(devices) == 0:
print('No devices found')
return 1
device = mvnc.Device(devices[0])
device.open()
#Load graph from disk and allocate graph via API
with open(tiny_yolo_graph_file, mode='rb') as f:
graph_from_disk = f.read()
graph = mvnc.Graph("Tiny Yolo Graph")
fifo_in, fifo_out = graph.allocate_with_fifos(device, graph_from_disk)
#------------------------------
# capture
#------------------------------
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
# Wait key
key = cv2.waitKey(1)
if key != -1:
break
# Read image from file, resize it to network width and height
# save a copy in display_image for display, then convert to float32, normalize (divide by 255),
# and finally convert to convert to float16 to pass to LoadTensor as input for an inference
#input_image = cv2.imread(input_image_file)
input_image = frame
display_image = input_image
input_image = cv2.resize(input_image,
(NETWORK_IMAGE_WIDTH, NETWORK_IMAGE_HEIGHT),
cv2.INTER_LINEAR)
input_image = input_image.astype(np.float32)
input_image = np.divide(input_image, 255.0)
input_image = input_image[:, :, ::-1] # convert to RGB
# Load tensor and get result. This executes the inference on the NCS
graph.queue_inference_with_fifo_elem(fifo_in, fifo_out,
input_image.astype(np.float32),
None)
output, userobj = fifo_out.read_elem()
# filter out all the objects/boxes that don't meet thresholds
filtered_objs = filter_objects(output.astype(np.float32),
input_image.shape[1],
input_image.shape[0])
print('Displaying image with objects detected in GUI')
print('Click in the GUI window and hit any key to exit')
#display the filtered objects/boxes in a GUI window
display_objects_in_gui(display_image, filtered_objs)
# Display
cv2.imshow("window", display_image)
fifo_in.destroy()
fifo_out.destroy()
graph.destroy()
device.close()
device.destroy()
print('Finished')
# Clean up capture
cap.release()
cv2.destroyAllWindows()
def main():
vs=VideoStream(usePiCamera=True).start()
time.sleep(1)
# print('Running NCS Caffe TinyYolo example')
i = 0
# Set logging level to only log errors
mvnc.global_set_option(mvnc.GlobalOption.RW_LOG_LEVEL, 3)
devices = mvnc.enumerate_devices() # TODO use the mvnc API to querydevices.
if len(devices) == 0:
#print('No devices found')
return 1
device = mvnc.Device(devices[0]) # TODO: use the mvnc API to assign the first device in devices to the device variable.
device.open()
#Load graph from disk and allocate graph via API
with open(tiny_yolo_graph_file, mode='rb') as f:
graph_from_disk = f.read()
graph = mvnc.Graph("Tiny Yolo Graph")
fifo_in, fifo_out = graph.allocate_with_fifos(device, graph_from_disk) # TODO: Instantiate fifo_in and fifo_out using the graph file above.
# Read image from file, resize it to network width and height
# save a copy in display_image for display, then convert to float32, normalize (divide by 255),
# and finally convert to convert to float16 to pass to LoadTensor as input for an inference
prev_time = 0
while True:
frame=vs.read()# Get a frame from a video stream.
input_image=frame.copy()# copy frame to an input image.
display_image = cv2.resize(input_image, (NETWORK_IMAGE_WIDTH, NETWORK_IMAGE_HEIGHT), cv2.INTER_LINEAR)
input_image = cv2.resize(input_image, (NETWORK_IMAGE_WIDTH, NETWORK_IMAGE_HEIGHT), cv2.INTER_LINEAR)
input_image = input_image.astype(np.float32)
input_image = (input_image*(1.0/255.0))# TODO: Scale values between 0 and 255
input_image = input_image[:, :, ::-1] # convert to RGB
#TODO: Use the queue_inference_with_fifo_elem to load the image and get the result from the NCS. This should be one line of code.
graph.queue_inference_with_fifo_elem(fifo_in, fifo_out, input_image.astype(np.float32), None)
output, userobj = fifo_out.read_elem()
# filter out all the objects/boxes that don't meet thresholds
filtered_objs = filter_objects(output.astype(np.float32), input_image.shape[1], input_image.shape[0])
current_time = time.time()
source_image_width = display_image.shape[1]
source_image_height = display_image.shape[0]
x_ratio = float(source_image_width) / NETWORK_IMAGE_WIDTH
y_ratio = float(source_image_height) / NETWORK_IMAGE_HEIGHT
if prev_time < current_time - 0.5:
prev_time = current_time
print('saving images');
for obj_index in range(0,min(len(filtered_objs),5)):
print(obj_index);
center_x = int(filtered_objs[obj_index][1] * x_ratio)
center_y = int(filtered_objs[obj_index][2] * y_ratio)
half_width = int(filtered_objs[obj_index][3] * x_ratio)//2
half_height = int(filtered_objs[obj_index][4] * y_ratio)//2
# calculate box (left, top) and (right, bottom) coordinates
box_left = max(center_x - half_width, 0)
box_top = max(center_y - half_height, 0)
box_right = min(center_x + half_width, source_image_width)
box_bottom = min(center_y + half_height, source_image_height)
image = display_image[box_top:box_bottom, box_left:box_right]
cv2.imwrite('task7_images/'+str(obj_index)+'.jpg', image)
# print('Displaying image with objects detected in GUI')
# print('Click in the GUI window and hit any key to exit')
#display the filtered objects/boxes in a GUI window
display_objects_in_gui(display_image, filtered_objs)
fifo_in.destroy()
fifo_out.destroy()
graph.destroy()
device.close()
device.destroy()
print('Finished')
def main():
print('Running NCS Caffe TinyYolo example')
# Set logging level to only log errors
mvnc.global_set_option(mvnc.GlobalOption.RW_LOG_LEVEL, 3)
devices = mvnc.enumerate_devices()
if len(devices) == 0:
print('No devices found')
return 1
device = mvnc.Device(devices[0])
device.open()
#Load graph from disk and allocate graph via API
with open(tiny_yolo_graph_file, mode='rb') as f:
graph_from_disk = f.read()
graph = mvnc.Graph("Tiny Yolo Graph")
fifo_in, fifo_out = graph.allocate_with_fifos(device, graph_from_disk)
#------------------------------
# capture
#------------------------------
cap = cv2.VideoCapture("cars.mp4")
#------------------------------
# pygame init
#------------------------------
pygame.init()
screen = pygame.display.set_mode((640, 360))
pygame.display.set_caption("TinyYolo VR")
background = pygame.image.load("aquarium.jpg")
player = pygame.image.load("jellyfish.png").convert_alpha()
x_ratio = float(screen.get_width()) / NETWORK_IMAGE_WIDTH
y_ratio = float(screen.get_height()) / NETWORK_IMAGE_HEIGHT
while True:
ret, frame = cap.read()
if ret == False:
break
# Wait key
key = cv2.waitKey(1)
if key != -1:
break
# Read image from file, resize it to network width and height
# save a copy in display_image for display, then convert to float32, normalize (divide by 255),
# and finally convert to convert to float16 to pass to LoadTensor as input for an inference
#input_image = cv2.imread(input_image_file)
input_image = frame
display_image = input_image
input_image = cv2.resize(input_image,
(NETWORK_IMAGE_WIDTH, NETWORK_IMAGE_HEIGHT),
cv2.INTER_LINEAR)
input_image = input_image.astype(np.float32)
input_image = np.divide(input_image, 255.0)
input_image = input_image[:, :, ::-1] # convert to RGB
# Load tensor and get result. This executes the inference on the NCS
graph.queue_inference_with_fifo_elem(fifo_in, fifo_out,
input_image.astype(np.float32),
None)
output, userobj = fifo_out.read_elem()
# filter out all the objects/boxes that don't meet thresholds
filtered_objs = filter_objects(output.astype(np.float32),
input_image.shape[1],
input_image.shape[0])
print('Displaying image with objects detected in GUI')
print('Click in the GUI window and hit any key to exit')
#display the filtered objects/boxes in a GUI window
display_objects_in_gui(display_image, filtered_objs)
# Display
cv2.imshow("window", display_image)
#------------------------------
# pygame draw
#------------------------------
#draw background
screen.blit(background, (0, 0))
#draw player
for obj_index in range(len(filtered_objs)):
center_x = int(filtered_objs[obj_index][1] * x_ratio)
center_y = int(filtered_objs[obj_index][2] * y_ratio)
screen.blit(player, (center_x - player.get_width() / 2,
center_y - player.get_height() / 2))
#update display
pygame.display.flip()
fifo_in.destroy()
fifo_out.destroy()
graph.destroy()
device.close()
device.destroy()
print('Finished')
# Clean up capture
cap.release()
cv2.destroyAllWindows()
def compare():
mvnc.global_set_option(mvnc.GlobalOption.RW_LOG_LEVEL, 2)
# enumerate all devices
devices = mvnc.enumerate_devices()
if len(devices) == 0:
print('No devices found')
quit()
# use the first device found
device = mvnc.Device(devices[0])
# open the device
device.open()
# load the model from the disk
with open(GRAPH_PATH, mode='rb') as f:
graph_in_memory = f.read()
graph = mvnc.Graph(GRAPH_PATH)
# create the input and output fifos
fifo_in, fifo_out = graph.allocate_with_fifos(device, graph_in_memory)
fps = 0.0
#Webcam mode, else video file mode
if args["video"].isdigit():
args["video"] = int(args["video"])
cap = cv2.VideoCapture(args["video"])
if args["mode"] == "image":
# read an image in bgr format
img = cv2.imread(IMAGE_FROM_DISK)
original_img = img
# bgr input scaling
img = np.divide(img, 255.0)
resized_img = cv2.resize(img, (416, 416), cv2.INTER_LINEAR)
# transpose the image to rgb
resized_img = resized_img[:, :, ::-1]
resized_img = resized_img.astype(np.float32)
# make an inference
graph.queue_inference_with_fifo_elem(fifo_in, fifo_out, resized_img,
'user object')
# get the result
output, userobj = fifo_out.read_elem()
# Tiny Yolo V2 requires post processing to filter out duplicate objects and low score objects
# After post processing, the app will display the image and any detected objects
post_processing(output, original_img)
cv2.imshow('Tiny Yolo V2', original_img)
cv2.waitKey()
while args["mode"] == "video":
start = time.time()
ret, display_image = cap.read()
if not ret:
print("No image found from source, exiting")
break
original_img = display_image
# bgr input scaling
display_image = np.divide(display_image, 255.0)
resized_img = cv2.resize(display_image, (416, 416), cv2.INTER_LINEAR)
# transpose the image to rgb
resized_img = resized_img[:, :, ::-1]
resized_img = resized_img.astype(np.float32)
# make an inference
graph.queue_inference_with_fifo_elem(fifo_in, fifo_out, resized_img,
'user object')
# get the result
output, userobj = fifo_out.read_elem()
# Tiny Yolo V2 requires post processing to filter out duplicate objects and low score objects
# After post processing, the app will display the image and any detected objects
output_image = post_processing(output, original_img)
fps = (fps + (1 / (time.time() - start))) / 2
output_image = cv2.putText(output_image, "fps: {:.1f}".format(fps),
(0, 20), cv2.FONT_HERSHEY_SIMPLEX, 1,
(0, 0, 0), 1, 4)
cv2.imshow(cv_window_name, output_image)
if cv2.getWindowProperty(cv_window_name,
cv2.WND_PROP_ASPECT_RATIO) < 0.0:
print("Window closed")
break
elif cv2.waitKey(1) & 0xFF == ord('q'):
print("Q pressed")
break
cap.release()
cv2.destroyAllWindows()
# clean up
fifo_in.destroy()
fifo_out.destroy()
graph.destroy()
device.close()
device.destroy()
print("Finished")
def main():
# 設定程式參數
arg_parser = argparse.ArgumentParser(description='軌跡車程式。')
arg_parser.add_argument(
'--model-file',
required=True,
help='Movidius 模型檔',
)
arg_parser.add_argument(
'--input-width',
type=int,
default=48,
help='模型輸入影像寬度',
)
arg_parser.add_argument(
'--input-height',
type=int,
default=48,
help='模型輸入影像高度',
)
# 解讀程式參數
args = arg_parser.parse_args()
assert args.input_width > 0 and args.input_height > 0
# 設置 Movidius 裝置
mvnc.global_set_option(mvnc.GlobalOption.RW_LOG_LEVEL, 2)
mvnc_devices = mvnc.enumerate_devices()
if not mvnc_devices:
print('找不到 Movidius 裝置')
exit(1)
mvnc_dev = mvnc.Device(mvnc_devices[0])
mvnc_dev.open()
# 載入模型檔
try:
with open(args.model_file, mode='rb') as file_graph:
graph_buffer = file_graph.read()
except (FileNotFoundError, IOError):
print('無法載入模型檔')
exit(1)
graph = mvnc.Graph('graph')
fifo_in, fifo_out = graph.allocate_with_fifos(mvnc_dev, graph_buffer)
# 開啓影片來源
video_dev = cv2.VideoCapture(0)
# 初始化 GPIO
GPIO.setwarnings(False)
GPIO.setmode(GPIO.BCM)
GPIO.setup(PWM_PIN_left, GPIO.OUT)
GPIO.setup(PWM_PIN_right, GPIO.OUT)
pwm1 = GPIO.PWM(PWM_PIN_left, 500)
pwm2 = GPIO.PWM(PWM_PIN_right, 500)
pwm1.start(0)
pwm2.start(0)
GPIO.setup(IR_RIGHT_PIN, GPIO.IN) #GPIO 2 -> Left IR out
GPIO.setup(IR_MIDDLE_PIN, GPIO.IN) #GPIO 3 -> Right IR out
GPIO.setup(IR_LEFT_PIN, GPIO.IN) #GPIO 4 -> Right IR out
def recognize_image():
# 先丟掉前十張舊的辨識結果
for i in range(10):
image = video_dev.read()
ret, orig_image = video_dev.read()
assert ret is not None
# 縮放爲模型輸入的維度、調整數字範圍爲 0～1 之間的數值
resized_image = cv2.resize(
orig_image,
(args.input_width, args.input_height),
).astype(np.float32)
normalized_image = resized_image / 255.0
# 執行預測
graph.queue_inference_with_fifo_elem(
fifo_in,
fifo_out,
normalized_image,
None,
)
result_onehot, _ = fifo_out.read_elem()
class_id = np.argmax(result_onehot)
left_score, right_score, stop_score, other_score = result_onehot
print('預測：%.2f %.2f %.2f %.2f' %
(left_score, right_score, stop_score, other_score))
# print(result_onehot)
if class_id == 0:
return 'left'
elif class_id == 1:
return 'right'
elif class_id == 2:
return 'stop'
elif class_id == 3:
return 'other'
def forward():
pwm1.ChangeDutyCycle(DUTY_CYCLE)
pwm2.ChangeDutyCycle(DUTY_CYCLE)
def head_left():
pwm1.ChangeDutyCycle(DUTY_CYCLE)
pwm2.ChangeDutyCycle(0)
def head_right():
pwm1.ChangeDutyCycle(0)
pwm2.ChangeDutyCycle(DUTY_CYCLE)
def stop():
pwm1.ChangeDutyCycle(0)
pwm2.ChangeDutyCycle(0)
def cross_left():
time.sleep(1)
pwm1.ChangeDutyCycle(100)
pwm2.ChangeDutyCycle(0)
time.sleep(0.35)
pwm1.ChangeDutyCycle(0)
pwm2.ChangeDutyCycle(0)
time.sleep(1)
pwm1.ChangeDutyCycle(100)
pwm2.ChangeDutyCycle(100)
time.sleep(1)
pwm1.ChangeDutyCycle(0)
pwm2.ChangeDutyCycle(0)
time.sleep(0.5)
def cross_right():
time.sleep(1)
pwm1.ChangeDutyCycle(0)
pwm2.ChangeDutyCycle(100)
time.sleep(0.35)
pwm1.ChangeDutyCycle(0)
pwm2.ChangeDutyCycle(0)
time.sleep(1)
pwm1.ChangeDutyCycle(100)
pwm2.ChangeDutyCycle(100)
time.sleep(1)
pwm1.ChangeDutyCycle(0)
pwm2.ChangeDutyCycle(0)
time.sleep(0.5)
def track_line():
middle_val = GPIO.input(IR_MIDDLE_PIN)
left_val = GPIO.input(IR_LEFT_PIN)
right_val = GPIO.input(IR_RIGHT_PIN)
print('光感:', left_val, middle_val, right_val)
if middle_val:
if left_val and right_val: # 白白白
return 'stop'
elif left_val and not right_val: # 白白黑
return 'left'
elif not left_val and right_val: # 黑白白
return 'right'
else:
return 'forward' # 黑白黑
else:
if left_val and right_val: # 白黑白
return 'stall'
elif left_val and not right_val: # 白黑黑
return 'left'
elif not left_val and right_val: # 黑黑白
return 'right'
else: # 黑黑黑
return 'stall'
try:
while True:
advice = track_line()
if advice == 'left':
print('動作:', '左轉')
head_left()
elif advice == 'right':
print('動作:', '右轉')
head_right()
elif advice == 'stop':
print('動作:', '停止')
stop()
sign = recognize_image()
if sign == 'left':
print('影像:', '左轉標誌')
cross_left()
elif sign == 'right':
print('影像:', '右轉標誌')
cross_right()
elif sign == 'stop':
print('影像:', '停止標誌')
elif sign == 'other':
print('影像:', '無標誌')
elif advice == 'forward':
print('動作:', '前進')
forward()
elif advice == 'stall':
print('動作:', '前進')
forward()
print()
except KeyboardInterrupt:
print('使用者中斷')
# 終止馬達
pwm1.stop()
pwm2.stop()
# 終止影像裝置
video_dev.release()
# 終止 Movidius 裝置
fifo_in.destroy()
fifo_out.destroy()
graph.destroy()
mvnc_dev.close()
mvnc_dev.destroy()
#! /usr/bin/env python3
# Copyright(c) 2017 Intel Corporation.
# License: MIT See LICENSE file in root directory.
# Python script to open and close a single NCS device
import mvnc.mvncapi as fx
# main entry point for the program
if __name__ == "__main__":
# set the logging level for the NC API
# fx.SetGlobalOption(fx.GlobalOption.LOG_LEVEL, 0) # [aboutyou1219] NCSDK v1
fx.global_set_option(fx.GlobalOption.RW_LOG_LEVEL, fx.LogLevel.DEBUG)
# get a list of names for all the devices plugged into the system
# ncs_names = fx.EnumerateDevices() # [aboutyou1219] NCSDK v1
ncs_names = fx.enumerate_devices()
if (len(ncs_names) < 1):
print(
"Error - no NCS devices detected, verify an NCS device is connected."
)
quit()
# get the first NCS device by its name. For this program we will always open the first NCS device.
dev = fx.Device(ncs_names[0])
# try to open the device. this will throw an exception if someone else has it open already
try:
import numpy
x_train = mnist.train_images()
y_train = mnist.train_labels()
x_test = mnist.test_images()
y_test = mnist.test_labels()
# Prepare test image
test_idx = numpy.random.randint(0, 10000)
test_image = x_test[test_idx]
test_image = test_image.astype('float32') / 255.0
# Using NCS Predict
# set the logging level for the NC API
fx.global_set_option(fx.GlobalOption.RW_LOG_LEVEL, 0)
# get a list of names for all the devices plugged into the system
devices = fx.enumerate_devices()
if (len(devices) < 1):
print("Error - no NCS devices detected, verify an NCS device is connected.")
quit()
# get the first NCS device by its name. For this program we will always open the first NCS device.
dev = fx.Device(devices[0])
# try to open the device. this will throw an exception if someone else has it open already
try:
dev.open()
except:
print("Error - Could not open NCS device.")
def main():
"""Main function for the program. Everything starts here.
:return: None
"""
global resize_output, resize_output_width, resize_output_height, \
resize_output, resize_output_width, resize_output_height, \
device_count
if (not handle_args()):
print_usage()
return 1
# get list of all the .mp4 files in the image directory
input_video_filename_list = os.listdir(input_video_path)
input_video_filename_list = [i for i in input_video_filename_list if i.endswith('.mp4')]
if (len(input_video_filename_list) < 1):
# no images to show
print('No video (.mp4) files found')
return 1
resting_image = cv2.imread("resting_image.png")
if (resting_image is None):
resting_image = numpy.zeros((800, 600, 3), numpy.uint8)
if (resize_output):
resting_image = cv2.resize(resting_image,
(resize_output_width, resize_output_height),
cv2.INTER_LINEAR)
# Set logging level to only log errors
mvnc.global_set_option(mvnc.GlobalOption.RW_LOG_LEVEL, 3)
devices = mvnc.enumerate_devices()
if len(devices) < 1:
print('No NCS device detected.')
print('Insert device and try again!')
return 1
if (device_count < 1) or (device_count > len(devices)):
device_count = len(devices)
# Create an object detector processor for each device that opens
# and store it in our list of processors
obj_detect_list = list()
idle_obj_detect_list = list()
device_number = 0
for one_device in devices:
try:
obj_detect_dev = mvnc.Device(one_device)
obj_detect_dev.open()
print("opened device " + str(device_number))
obj_detector_proc = Yolov2_tiny_Processor(NETWORK_GRAPH_FILENAME, obj_detect_dev,
inital_box_prob_thresh=min_score_percent / 100.0,
classification_mask=object_classifications_mask,
name="object detector " + str(device_number))
if (device_number < device_count):
obj_detect_list.append(obj_detector_proc)
else:
idle_obj_detect_list.append(obj_detector_proc)
device_number += 1
except:
print("Could not open device " + str(device_number) + ", trying next device")
pass
if len(obj_detect_list) < 1:
print('Could not open any NCS devices.')
print('Reinsert devices and try again!')
return 1
print("Using " + str(len(obj_detect_list)) + " devices for object detection")
print_hot_keys()
cv2.namedWindow(cv_window_name)
cv2.moveWindow(cv_window_name, 10, 10)
cv2.waitKey(1)
exit_app = False
while (True):
for input_video_file in input_video_filename_list :
for one_obj_detect_proc in obj_detect_list:
print("using object detector: " + one_obj_detect_proc.get_name())
one_obj_detect_proc.drain_queues()
# video processor that will put video frames images on the object detector's input FIFO queue
video_proc = VideoProcessor(input_video_path + '/' + input_video_file,
network_processor_list = obj_detect_list)
video_proc.start_processing()
frame_count = 0
start_time = time.time()
last_throttle_time = start_time
end_time = start_time
uptime=time.time()
while(True):
done = False
for one_obj_detect_proc in obj_detect_list:
try:
(filtered_objs, display_image) = one_obj_detect_proc.get_async_inference_result()
print("resive result:",time.time()-uptime)
uptime=time.time()
except :
print("exception caught in main")
raise
# check if the window is visible, this means the user hasn't closed
# the window via the X button
prop_val = cv2.getWindowProperty(cv_window_name, cv2.WND_PROP_ASPECT_RATIO)
if (prop_val < 0.0):
end_time = time.time()
video_proc.stop_processing()
video_proc.cleanup()
exit_app = True
break
running_fps = frame_count / (time.time() - start_time)
overlay_on_image(display_image, filtered_objs, running_fps)
print("show time:",time.time()-uptime)
if (resize_output):
display_image = cv2.resize(display_image,
(resize_output_width, resize_output_height),
cv2.INTER_LINEAR)
cv2.imshow(cv_window_name, display_image)
raw_key = cv2.waitKey(1)
if (raw_key != -1):
if (handle_keys(raw_key, obj_detect_list) == False):
end_time = time.time()
exit_app = True
done = True
break
frame_count += 1
#if (one_obj_detect_proc.is_input_queue_empty()):
if (not video_proc.is_processing()):
# asssume the video is over.
end_time = time.time()
done = True
print('video processor not processing, assuming video is finished.')
break
#if (frame_count % 100) == 0:
if ((time.time() - last_throttle_time) > throttle_check_seconds):
#long movie, check for throttling devices
# throttling = one_obj_detect_proc.get_device().get_option(mvnc.DeviceOption.RO_THERMAL_THROTTLING_LEVEL)
last_throttle_time = time.time()
print("movie not done, but going a long time so adjust for throttling")
video_proc.pause()
do_throttle_adjustment(obj_detect_list, idle_obj_detect_list)
video_proc.unpause()
if (done) : break
frames_per_second = frame_count / (end_time - start_time)
print('Frames per Second: ' + str(frames_per_second))
# check for throttling devices and save in throttling list
throttling_list = list()
for one_obj_detect_proc in obj_detect_list:
throttling = one_obj_detect_proc.get_device().get_option(mvnc.DeviceOption.RO_THERMAL_THROTTLING_LEVEL)
if (throttling > 0):
print("\nDevice " + one_obj_detect_proc.get_name() + " is throttling, level is: " + str(throttling))
throttling_list.append(one_obj_detect_proc)
if (not exit_app):
# rest between movies, display an image while resting
resting_display_image = cv2.resize(resting_image,
(display_image.shape[1], display_image.shape[0]),
cv2.INTER_LINEAR)
cv2.imshow(cv_window_name, resting_display_image)
if ((len(throttling_list) > len(idle_obj_detect_list))):
# more devices throttling than we have in the idle list
# so do extra rest by applying a multiplier to the rest time
print("throttling devices... resting")
cv2.waitKey(rest_seconds * 1000 * rest_throttling_multiplier)
else:
cv2.waitKey(rest_seconds * 1000)
# remove the throttling devices from the main list and put them at the end so they will
# be moved to the idle list with priority
for one_throttling in throttling_list:
obj_detect_list.remove(one_throttling)
obj_detect_list.append(one_throttling)
num_idle = len(idle_obj_detect_list)
if (num_idle > len(obj_detect_list)):
num_idle = len(obj_detect_list)
if (num_idle > 0):
# replace one of the devices with an idle device
for idle_index in range(0, num_idle):
#for one_idle_proc in idle_obj_detect_list:
obj_detect_list.insert(0, idle_obj_detect_list.pop(0))
for idle_count in range(0, num_idle):
idle_obj_detect_list.append(obj_detect_list.pop())
video_proc.stop_processing()
video_proc.cleanup()
if (exit_app):
break
if (exit_app):
break
# Clean up the graph and the device
for one_obj_detect_proc in obj_detect_list:
cv2.waitKey(1)
one_obj_detect_proc.cleanup(True)
cv2.destroyAllWindows()
def main():
while True:
vs=VideoStream(usePiCamera=True).start()
time.sleep(1)
print('Running NCS Caffe TinyYolo example')
# Set logging level to only log errors
mvnc.global_set_option(mvnc.GlobalOption.RW_LOG_LEVEL, 3)
###########################################################################
devices = mvnc.enumerate_devices()
if len(devices) == 0:
print('No devices found')
return 1
###########################################################################
device = mvnc.Device(devices[0])
device.open()
#Load graph from disk and allocate graph via API
with open(tiny_yolo_graph_file, mode='rb') as f:
graph_from_disk = f.read()
graph = mvnc.Graph("Tiny Yolo Graph")
############################################################################
fifo_in, fifo_out = graph.allocate_with_fifos(device, graph_from_disk)
#may need another line here from cheat sheet
# fifo_in, fifo_out = graph.allocate_with_fifos(device, graph_from_disk,
# fifo_in_type=mvnc.FifoType.HOST_WO, fifo_in_data_type=mvnc.FifoDataType.FP32,
# fifo_in_num_elem=2,
# fifo_out_type=mvnc.FifoType.HOST_RO, fifo_out_data_type=mvnc.FifoDataType.FP32,
# fifo_out_num_elem=2)
# Read image from file, resize it to network width and height
# save a copy in display_image for display, then convert to float32, normalize (divide by 255),
# and finally convert to convert to float16 to pass to LoadTensor as input for an inference
while True:
frame=vs.read()# Get a frame from a video stream.
input_image=frame.copy()# copy frame to an input image.
display_image = cv2.resize(input_image, (NETWORK_IMAGE_WIDTH, NETWORK_IMAGE_HEIGHT), cv2.INTER_LINEAR)
input_image = cv2.resize(input_image, (NETWORK_IMAGE_WIDTH, NETWORK_IMAGE_HEIGHT), cv2.INTER_LINEAR)
input_image = input_image.astype(np.float32)
##############################################################################
input_image = (255.0/input_image.max())
#could also be this
# input_image = input_image/255
# input_image[:] = ((input_image[:] )*(1.0/255.0))
input_image = input_image[:, :, ::-1] # convert to RGB
#TODO: Use the queue_inference_with_fifo_elem to load the image and get the result from the NCS. This should be one line of code.
graph.queue_inference_with_fifo_elem(fifo_in, fifo_out, input_image,'userobj')
output, userobj = fifo_out.read_elem()
# filter out all the objects/boxes that don't meet thresholds
filtered_objs = filter_objects(output.astype(np.float32), input_image.shape[1], input_image.shape[0])
##############################################################
save_box(filtered_objs,display_image)
print('Displaying image with objects detected in GUI')
print('Click in the GUI window and hit any key to exit')
#display the filtered objects/boxes in a GUI window
display_objects_in_gui(display_image, filtered_objs)
fifo_in.destroy()
fifo_out.destroy()
graph.destroy()
device.close()
device.destroy()
time.sleep(.5)
print('Finished')
class ObjectWrapper():
mvnc.global_set_option(mvnc.GlobalOption.RW_LOG_LEVEL, 2)
devices = mvnc.enumerate_devices()
devNum = len(devices)
if len(devices) == 0:
print('No MVNC devices found')
quit()
devHandle = []
graphHandle = []
fifoInHandle = []
fifoOutHandle = []
def __init__(self, graphfile):
select = 1
self.detector = YoloDetector(select)
for i in range(ObjectWrapper.devNum):
ObjectWrapper.devHandle.append(
mvnc.Device(ObjectWrapper.devices[i]))
ObjectWrapper.devHandle[i].open()
# load blob
with open(graphfile, mode='rb') as f:
blob = f.read()
# create graph instance
ObjectWrapper.graphHandle.append(mvnc.Graph('inst' + str(i)))
# allocate resources
fifoIn, fifoOut = ObjectWrapper.graphHandle[i].allocate_with_fifos(
ObjectWrapper.devHandle[i], blob)
ObjectWrapper.fifoInHandle.append(fifoIn)
ObjectWrapper.fifoOutHandle.append(fifoOut)
self.dim = (416, 416)
self.blockwd = 12
self.wh = self.blockwd * self.blockwd
self.targetBlockwd = 13
self.classes = 20
self.threshold = 0.2
self.nms = 0.4
def __del__(self):
for i in range(ObjectWrapper.devNum):
ObjectWrapper.fifoInHandle[i].destroy()
ObjectWrapper.fifoOutHandle[i].destroy()
ObjectWrapper.graphHandle[i].destroy()
ObjectWrapper.devHandle[i].close()
def PrepareImage(self, img, dim):
imgw = img.shape[1]
imgh = img.shape[0]
imgb = np.empty((dim[0], dim[1], 3))
imgb.fill(0.5)
if imgh / imgw > dim[1] / dim[0]:
neww = int(imgw * dim[1] / imgh)
newh = dim[1]
else:
newh = int(imgh * dim[0] / imgw)
neww = dim[0]
offx = int((dim[0] - neww) / 2)
offy = int((dim[1] - newh) / 2)
imgb[offy:offy + newh,
offx:offx + neww, :] = resize(img.copy() / 255.0, (newh, neww), 1)
im = imgb[:, :, (2, 1, 0)]
return im, int(offx * imgw / neww), int(
offy * imgh / newh), neww / dim[0], newh / dim[1]
def Reshape(self, out, dim):
shape = out.shape
out = np.transpose(out.reshape(self.wh, int(shape[0] / self.wh)))
out = out.reshape(shape)
return out
def Detect(self, img, idx=0):
"""Send image for inference on a single compute stick
Args:
img: openCV image type
idx: index of the compute stick to use for inference
Returns:
[<BBox>]: array of BBox type objects for each result in the detection
"""
imgw = img.shape[1]
imgh = img.shape[0]
im, offx, offy, xscale, yscale = self.PrepareImage(img, self.dim)
#print('xscale = {}, yscale = {}'.format(xscale, yscale))
ObjectWrapper.graphHandle[idx].queue_inference_with_fifo_elem(
ObjectWrapper.fifoInHandle[i], ObjectWrapper.fifoOutHandle[i],
im.astype(np.float32), 'user object')
out, userobj = ObjectWrapper.fifoOutHandle[idx].read_elem()
out = self.Reshape(out, self.dim)
internalresults = self.detector.Detect(out.astype(np.float32),
int(out.shape[0] / self.wh),
self.blockwd, self.blockwd,
self.classes, imgw, imgh,
self.threshold, self.nms,
self.targetBlockwd)
pyresults = [
BBox(x, xscale, yscale, offx, offy) for x in internalresults
]
return pyresults
def Parallel(self, img):
"""Send array of images for inference on multiple compute sticks
Args:
img: array of images to run inference on
Returns:
{ <int>:[<BBox] }: A dict with key-value pairs mapped to compute stick device numbers and arrays of the detection boxs (BBox)
"""
pyresults = {}
for i in range(ObjectWrapper.devNum):
im, offx, offy, w, h = self.PrepareImage(img[i], self.dim)
ObjectWrapper.graphHandle[i].queue_inference_with_fifo_elem(
ObjectWrapper.fifoInHandle[i], ObjectWrapper.fifoOutHandle[i],
im.astype(np.float32), 'user object')
for i in range(ObjectWrapper.devNum):
out, userobj = ObjectWrapper.fifoOutHandle[i].read_elem()
out = self.Reshape(out, self.dim)
imgw = img[i].shape[1]
imgh = img[i].shape[0]
internalresults = self.detector.Detect(out.astype(np.float32),
int(out.shape[0] / self.wh),
self.blockwd, self.blockwd,
self.classes, imgw, imgh,
self.threshold, self.nms,
self.targetBlockwd)
res = [BBox(x, w, h, offx, offy) for x in internalresults]
if i not in pyresults:
pyresults[i] = res
return pyresults
import os
import sys
dim = (227, 227)
EXAMPLES_BASE_DIR = '../../'
# ***************************************************************
# get labels
# ***************************************************************
labels_file = EXAMPLES_BASE_DIR + 'data/ilsvrc12/synset_words.txt'
labels = numpy.loadtxt(labels_file, str, delimiter='\t')
# ***************************************************************
# configure the NCS
# ***************************************************************
mvnc.global_set_option(mvnc.GlobalOption.RW_LOG_LEVEL, 2)
# ***************************************************************
# Get a list of ALL the sticks that are plugged in
# ***************************************************************
devices = mvnc.enumerate_devices()
if len(devices) == 0:
print('No devices found')
quit()
# ***************************************************************
# Pick the first stick to run the network
# ***************************************************************
device = mvnc.Device(devices[0])
# ***************************************************************
def __init__(self):
super(MyMnistWindow, self).__init__()
self.resize(284*2, 330*2) # resize设置宽高
self.move(100, 100) # move设置位置 说明在哪个位置截图
self.setWindowIcon(QIcon('./logo.ico'))
self.setWindowTitle('计算棒测试-手写数字识别')
self.setWindowFlags(Qt.FramelessWindowHint) # 窗体无边框
#setMouseTracking设置为False，否则不按下鼠标时也会跟踪鼠标事件
self.setMouseTracking(False)
self.pos_xy = [] #保存鼠标移动过的点
# 添加一系列控件
self.label_draw = QLabel('', self)
self.label_draw.setStyleSheet("QLabel{background:rgb(255,255,255)}")
self.label_draw.setGeometry(2, 2, 550, 550) # (x,y,width,height)
self.label_draw.setStyleSheet("QLabel{border:1px solid black;}")
self.label_draw.setAlignment(Qt.AlignCenter)
self.label_result_name = QLabel('预测：', self)
self.label_result_name.setGeometry(2, 570, 61, 35)
self.label_result_name.setAlignment(Qt.AlignCenter)
self.label_result = QLabel(' ', self)
self.label_result.setGeometry(64, 570, 35, 35)
self.label_result.setFont(QFont("Roman times", 8, QFont.Bold))
self.label_result.setStyleSheet("QLabel{border:1px solid black;}")
self.label_result.setAlignment(Qt.AlignCenter)
self.btn_recognize = QPushButton("识别", self)
self.btn_recognize.setGeometry(110, 570, 50, 35)
self.btn_recognize.clicked.connect(self.btn_recognize_on_clicked)
self.btn_clear = QPushButton("清空", self)
self.btn_clear.setGeometry(170, 570, 50, 35)
self.btn_clear.clicked.connect(self.btn_clear_on_clicked)
self.btn_close = QPushButton("关闭", self)
self.btn_close.setGeometry(230, 570, 50, 35)
self.btn_close.clicked.connect(self.btn_close_on_clicked)
# 时间
self.label_time_name = QLabel('识别时间：', self)
self.label_time_name.setGeometry(320, 570, 100, 35)
self.label_time_name.setAlignment(Qt.AlignCenter)
self.label_time = QLabel(' ', self)
self.label_time.setGeometry(430, 570, 110, 35)
self.label_time.setFont(QFont("Roman times", 8, QFont.Bold))
self.label_time.setStyleSheet("QLabel{border:1px solid black;}")
self.label_time.setAlignment(Qt.AlignCenter)
# 计算棒信息
self.label_ncs_name = QLabel('NCS状态：', self)
self.label_ncs_name.setGeometry(2, 610, 100, 35)
self.label_ncs_name.setAlignment(Qt.AlignCenter)
self.label_ncs = QLabel(' ', self)
self.label_ncs.setGeometry(110, 610, 430, 35)
self.label_ncs.setFont(QFont("Roman times", 8, QFont.Bold))
self.label_ncs.setStyleSheet("QLabel{border:1px solid black;}")
self.label_ncs.setAlignment(Qt.AlignCenter)
# 打开计算棒的设备
mvnc.global_set_option(mvnc.GlobalOption.RW_LOG_LEVEL,2)
#获取连接到主机系统的神经计算设备列表
self.devices = mvnc.enumerate_devices()
if len(self.devices) == 0:
print("[INFO] 未发现计算棒的任何设备！")
raise("[Error] No devices found!")
# quit()
#调用第一个NCS设备
self.device = mvnc.Device(self.devices[0])
print("[INFO] 打开的计算棒设备id：" + str(self.devices[0]))
#打开通信
self.device.open()
self.ncs_info = "NCS调用成功，device ID:" + str(self.devices[0])
# 加载图
with open(GRAPH_FILE,mode='rb') as f:
self.graphFileBuff = f.read()
#初始化一个名为graph_name的图
self.graph = mvnc.Graph("alexnet")
#创建输入和输出先进先出队列，将图加载到设备
self.input_fifo,self.output_fifo=self.graph.allocate_with_fifos(self.device,self.graphFileBuff)
