def infer_image_v2( graph, img_draw, img):
# The first inference takes an additional ~20ms due to memory
# initializations, so we make a 'dummy forward pass'.
graph.LoadTensor( img, 'user object' )
output, userobj = graph.GetResult()
# Load the image as a half-precision floating point array
graph.LoadTensor( img, 'user object' )
# Get the results from NCS
output, userobj = graph.GetResult()
# Get execution time
inference_time = graph.GetGraphOption( mvnc.GraphOption.TIME_TAKEN )
# Deserialize the output into a python dictionary
if ARGS['network'] == 'SSD':
output_dict = deserialize_output.ssd( output, CONFIDANCE_THRESHOLD, img_draw.shape )
elif ARGS['network'] == 'TinyYolo':
output_dict = deserialize_output.tinyyolo( output, CONFIDANCE_THRESHOLD, img_draw.shape )
tab = []
for i in range( 0, output_dict['num_detections'] ):
tabbis = []
tabbis.append( '{}'.format(output_dict['detection_scores_' + str(i)]) )
tabbis.append( labels[ int(output_dict['detection_classes_' + str(i)]) ] )
tabbis.append( output_dict['detection_boxes_' + str(i)][0] )
tabbis.append( output_dict['detection_boxes_' + str(i)][1] )
tab.append(tabbis)
# Draw bounding boxes around valid detections
(y1, x1) = output_dict.get('detection_boxes_' + str(i))[0]
(y2, x2) = output_dict.get('detection_boxes_' + str(i))[1]
# Prep string to overlay on the image
display_str = (
labels[output_dict.get('detection_classes_' + str(i))]
+ ": "
+ str( output_dict.get('detection_scores_' + str(i) ) )
+ "%" )
img_draw = visualize_output.draw_bounding_box(
y1, x1, y2, x2,
img_draw,
thickness=4,
color=(255, 255, 0),
display_str=display_str )
print( "==============================================================\n" )
if not os.path.exists('pictures'):
os.makedirs('pictures')
skimage.io.imshow( img_draw )
filename = 'pictures/{}.jpg'.format(str(round(time.time() * 1000)))
skimage.io.imsave(filename, img_draw)
return tab
def infer_image( graph, img ):
# Read original image, so we can perform visualization ops on it
img_draw = skimage.io.imread( ARGS['image'] )
# The first inference takes an additional ~20ms due to memory
# initializations, so we make a 'dummy forward pass'.
graph.LoadTensor( img, 'user object' )
output, userobj = graph.GetResult()
# Load the image as a half-precision floating point array
graph.LoadTensor( img, 'user object' )
# Get the results from NCS
output, userobj = graph.GetResult()
# Get execution time
inference_time = graph.GetGraphOption( mvnc.GraphOption.TIME_TAKEN )
# Deserialize the output into a python dictionary
if ARGS['network'] == 'SSD':
output_dict = deserialize_output.ssd( output, CONFIDANCE_THRESHOLD, img_draw.shape )
elif ARGS['network'] == 'TinyYolo':
output_dict = deserialize_output.tinyyolo( output, CONFIDANCE_THRESHOLD, img_draw.shape )
# Print the results
print( "\n==============================================================" )
print( "I found these objects in", ntpath.basename( ARGS['image'] ) )
print( "Execution time: " + str( np.sum( inference_time ) ) + "ms" )
print( "--------------------------------------------------------------" )
for i in range( 0, output_dict['num_detections'] ):
print( "%3.1f%%\t" % output_dict['detection_scores_' + str(i)]
+ labels[ int(output_dict['detection_classes_' + str(i)]) ]
+ ": Top Left: " + str( output_dict['detection_boxes_' + str(i)][0] )
+ " Bottom Right: " + str( output_dict['detection_boxes_' + str(i)][1] ) )
# Draw bounding boxes around valid detections
(y1, x1) = output_dict.get('detection_boxes_' + str(i))[0]
(y2, x2) = output_dict.get('detection_boxes_' + str(i))[1]
# Prep string to overlay on the image
display_str = (
labels[output_dict.get('detection_classes_' + str(i))]
+ ": "
+ str( output_dict.get('detection_scores_' + str(i) ) )
+ "%" )
img_draw = visualize_output.draw_bounding_box(
y1, x1, y2, x2,
img_draw,
thickness=4,
color=(255, 255, 0),
display_str=display_str )
print( "==============================================================\n" )
# If a display is available, show the image on which inference was performed
if 'DISPLAY' in os.environ:
skimage.io.imshow( img_draw )
skimage.io.show()
def infer_image(graph, img, frame):
# Load the image as a half-precision floating point array
graph.LoadTensor(img, 'user object')
# Get the results from NCS
output, userobj = graph.GetResult()
# Get execution time
inference_time = graph.GetGraphOption(mvnc.GraphOption.TIME_TAKEN)
# Deserialize the output into a python dictionary
output_dict = deserialize_output.ssd(output, CONFIDANCE_THRESHOLD,
frame.shape)
# Print the results (each image/frame may have multiple objects)
print("I found these objects in " + " ( %.2f ms ):" %
(numpy.sum(inference_time)))
flag = 0
for i in range(0, output_dict['num_detections']):
print("%3.1f%%\t" % output_dict['detection_scores_' + str(i)] +
labels[int(output_dict['detection_classes_' + str(i)])] +
": Top Left: " +
str(output_dict['detection_boxes_' + str(i)][0]) +
" Bottom Right: " +
str(output_dict['detection_boxes_' + str(i)][1]))
# Draw bounding boxes around valid detections
(y1, x1) = output_dict.get('detection_boxes_' + str(i))[0]
(y2, x2) = output_dict.get('detection_boxes_' + str(i))[1]
if labels[output_dict.get('detection_classes_' +
str(i))] == '5: bottle':
flag = 1
xdata1 = x1
xdata2 = x2
# Prep string to overlay on the image
display_str = (labels[output_dict.get('detection_classes_' + str(i))] +
": " +
str(output_dict.get('detection_scores_' + str(i))) +
"%")
frame = visualize_output.draw_bounding_box(y1,
x1,
y2,
x2,
frame,
thickness=4,
color=(255, 255, 0),
display_str=display_str)
print('\n')
# If a display is available, show the image on which inference was performed
if 'DISPLAY' in os.environ:
cv2.imshow('NCS live inference', frame)
if flag == 0:
return int((xdata1 + xdata2) / 2) - 288
else:
return -800
def infer_image(graph, img, frame, fps):
# Load the image as a half-precision floating point array
graph.LoadTensor(img, 'user object')
# Get the results from NCS
output, userobj = graph.GetResult()
# Get execution time
inference_time = graph.GetGraphOption(mvnc.GraphOption.TIME_TAKEN)
# Deserialize the output into a python dictionary
output_dict = deserialize_output.ssd(output, CONFIDANCE_THRESHOLD,
frame.shape)
# Print the results (each image/frame may have multiple objects)
for i in range(0, output_dict['num_detections']):
# Filter a specific class/category
if (output_dict.get('detection_classes_' + str(i)) == CLASS_PERSON):
#cur_time = strftime( "%Y_%m_%d_%H_%M_%S", localtime() )
#print( "Person detected on " + cur_time )
# Extract top-left & bottom-right coordinates of detected objects
(y1, x1) = output_dict.get('detection_boxes_' + str(i))[0]
(y2, x2) = output_dict.get('detection_boxes_' + str(i))[1]
# Prep string to overlay on the image
display_str = (
labels[output_dict.get('detection_classes_' + str(i))] + ": " +
str(output_dict.get('detection_scores_' + str(i))) + "%")
# Overlay bounding boxes, detection class and scores
frame = visualize_output.draw_bounding_box(y1,
x1,
y2,
x2,
frame,
thickness=4,
color=(255, 255, 0),
display_str=display_str)
# Capture snapshots
#img = Image.fromarray( frame )
#photo = ( os.path.dirname(os.path.realpath(__file__))
# + "/captures/photo_"
# + cur_time + ".jpg" )
#img.save( photo )
# If a display is available, show the image on which inference was performed
#if 'DISPLAY' in os.environ:
# img.show()
frame = visualize_output.draw_fps(frame, fps)
img = Image.fromarray(frame)
imgbytearr = io.BytesIO()
img.save(imgbytearr, format='jpeg')
mqttframe = imgbytearr.getvalue()
client.publish(MQTT_TOPIC, mqttframe, 0, False)
def infer_image( graph, img ):
# Read original image, so we can perform visualization ops on it
img_draw = skimage.io.imread( ARGS.image )
# The first inference takes an additional ~20ms due to memory
# initializations, so we make a 'dummy forward pass'.
graph.LoadTensor( img, 'user object' )
output, userobj = graph.GetResult()
# Load the image as a half-precision floating point array
graph.LoadTensor( img, 'user object' )
# Get the results from NCS
output, userobj = graph.GetResult()
# Get execution time
inference_time = graph.GetGraphOption( mvnc.GraphOption.TIME_TAKEN )
# Deserialize the output into a python dictionary
output_dict = deserialize_output.ssd(
output,
CONFIDANCE_THRESHOLD,
img_draw.shape )
# Print the results
print( "\n==============================================================" )
print( "I found these objects in", ntpath.basename( ARGS.image ) )
print( "Execution time: " + str( numpy.sum( inference_time ) ) + "ms" )
print( "--------------------------------------------------------------" )
for i in range( 0, output_dict['num_detections'] ):
print( "%3.1f%%\t" % output_dict['detection_scores_' + str(i)]
+ labels[ int(output_dict['detection_classes_' + str(i)]) ]
+ ": Top Left: " + str( output_dict['detection_boxes_' + str(i)][0] )
+ " Bottom Right: " + str( output_dict['detection_boxes_' + str(i)][1] ) )
# Draw bounding boxes around valid detections
(y1, x1) = output_dict.get('detection_boxes_' + str(i))[0]
(y2, x2) = output_dict.get('detection_boxes_' + str(i))[1]
# Prep string to overlay on the image
display_str = (
labels[output_dict.get('detection_classes_' + str(i))]
+ ": "
+ str( output_dict.get('detection_scores_' + str(i) ) )
+ "%" )
img_draw = visualize_output.draw_bounding_box(
y1, x1, y2, x2,
img_draw,
thickness=4,
color=(255, 255, 0),
display_str=display_str )
print( "==============================================================\n" )
# If a display is available, show the image on which inference was performed
if 'DISPLAY' in os.environ:
skimage.io.imshow( img_draw )
skimage.io.show()
def ssd_infer_image(tensor, image_name):
original_image = skimage.io.imread(image_name)
output, inference_time = mv.manage_NCS(graph_file, tensor)
output_dict = deserialize_output.ssd(output, CONFIDENCE_THRESHOLD,
original_image.shape)
# Compile results
results = "SSD Object Detection results:\n\n This image contains:\n"
for i in range(0, output_dict['num_detections']):
results = results + str(output_dict['detection_scores_' + str(i)]) + "% confidence it could be a " + \
labels[int(output_dict['detection_classes_' + str(i)])][3:] + "\n"
# Draw bounding boxes around valid detections
(y1, x1) = output_dict.get('detection_boxes_' + str(i))[0]
(y2, x2) = output_dict.get('detection_boxes_' + str(i))[1]
# Prep string to overlay on the image
display_str = (labels[output_dict.get('detection_classes_' + str(i))] +
str(output_dict.get('detection_scores_' + str(i))) +
"%")
original_image = visualize_output.draw_bounding_box(
y1,
x1,
y2,
x2,
original_image,
thickness=6,
color=(255, 255, 0),
display_str=display_str)
results = results + "\n\nExecution time: " + str(
int(numpy.sum(inference_time))) + " ms"
return original_image, results
def infer_image(graph, img, frame):
# Load the image as a half-precision floating point array
graph.LoadTensor(img, 'user object')
# Get the results from NCS
output, userobj = graph.GetResult()
# Get execution time
inference_time = graph.GetGraphOption(mvnc.GraphOption.TIME_TAKEN)
# Deserialize the output into a python dictionary
output_dict = deserialize_output.ssd(output, CONFIDANCE_THRESHOLD,
frame.shape)
global lastcount
count = 0
selected = 'bottle'
for i in range(0, output_dict['num_detections']):
print("%3.1f%%\t" % output_dict['detection_scores_' + str(i)] +
labels[int(output_dict['detection_classes_' + str(i)])] +
": Top Left: " +
str(output_dict['detection_boxes_' + str(i)][0]) +
" Bottom Right: " +
str(output_dict['detection_boxes_' + str(i)][1]))
if str(selected) in labels[int(output_dict['detection_classes_' +
str(i)])]:
count = count + 1
# Draw bounding boxes around valid detections
(y1, x1) = output_dict.get('detection_boxes_' + str(i))[0]
(y2, x2) = output_dict.get('detection_boxes_' + str(i))[1]
# Prep string to overlay on the image
display_str = (
labels[output_dict.get('detection_classes_' + str(i))] + ": " +
str(output_dict.get('detection_scores_' + str(i))) + "%")
frame = visualize_output.draw_bounding_box(y1,
x1,
y2,
x2,
frame,
thickness=4,
color=(255, 255, 0),
display_str=display_str)
if lastcount != count and selected != '':
datastring = 'Item=' + selected + "&Count=" + str(count)
file = open('./bottles.txt', 'w')
file.write(datastring)
file.close()
lastcount = count
print(count)
print('\n')
# If a display is available, show the image on which inference was performed
if 'DISPLAY' in os.environ:
cv2.imshow('NCS live inference', frame)
def infer_image( graph, img, frame ):
# Load the image as a half-precision floating point array
graph.LoadTensor( img, 'user object' )
# Get the results from NCS
output, userobj = graph.GetResult()
# Get execution time
inference_time = graph.GetGraphOption( mvnc.GraphOption.TIME_TAKEN )
# Deserialize the output into a python dictionary
output_dict = deserialize_output.ssd(
output,
CONFIDANCE_THRESHOLD,
frame.shape )
# Print the results (each image/frame may have multiple objects)
#print( "I found these objects in "
#+ " ( %.2f ms ):" % ( numpy.sum( inference_time ) ) )
global lastcount
count = 0
for i in range( 0, output_dict['num_detections'] ):
print( "%3.1f%%\t" % output_dict['detection_scores_' + str(i)]
+ labels[ int(output_dict['detection_classes_' + str(i)]) ]
+ ": Top Left: " + str( output_dict['detection_boxes_' + str(i)][0] )
+ " Bottom Right: " + str( output_dict['detection_boxes_' + str(i)][1] ) )
if 'person' in labels[ int(output_dict['detection_classes_' + str(i)]) ]:
count = count + 1
# Draw bounding boxes around valid detections
(y1, x1) = output_dict.get('detection_boxes_' + str(i))[0]
(y2, x2) = output_dict.get('detection_boxes_' + str(i))[1]
# Prep string to overlay on the image
display_str = (
labels[output_dict.get('detection_classes_' + str(i))]
+ ": "
+ str( output_dict.get('detection_scores_' + str(i) ) )
+ "%" )
frame = visualize_output.draw_bounding_box(
y1, x1, y2, x2,
frame,
thickness=4,
color=(255, 255, 0),
display_str=display_str )
if lastcount != count:
file = open('../node_server/client/people.txt','w')
file.write(str(count))
file.close()
lastcount = count
print( str(count) + ' people\n' )
# If a display is available, show the image on which inference was performed
if 'DISPLAY' in os.environ:
cv2.imshow( 'NCS live inference', frame )
def infer_image(graph, img, frame):
# Load the image as a half-precision floating point array
graph.LoadTensor(img, 'user object')
# Get the results from NCS
output, userobj = graph.GetResult()
# Get execution time
inference_time = graph.GetGraphOption(mvnc.GraphOption.TIME_TAKEN)
# Deserialize the output into a python dictionary
output_dict = deserialize_output.ssd(output, CONFIDANCE_THRESHOLD,
frame.shape)
# Print the results (each image/frame may have multiple objects)
output_str = "\n==============================================================\n"
output_str += "Execution time: " + "%.1fms" % (
numpy.sum(inference_time)) + "\n"
for i in range(0, output_dict['num_detections']):
output_str += "%3.1f%%\t" % output_dict['detection_scores_' + str(i)]
output_str += labels[int(output_dict['detection_classes_' + str(i)])]
output_str += ": Top Left: " + str(
output_dict['detection_boxes_' + str(i)][0])
output_str += " Bottom Right: " + str(
output_dict['detection_boxes_' + str(i)][1])
output_str += "\n"
# Draw bounding boxes around valid detections
(y1, x1) = output_dict.get('detection_boxes_' + str(i))[0]
(y2, x2) = output_dict.get('detection_boxes_' + str(i))[1]
label = labels[output_dict.get('detection_classes_' + str(i))]
labelsArray = label.split(":")
if len(labelsArray) == 2:
label = labelsArray[1]
# Prep string to overlay on the image
display_str = (label + ": " +
str(output_dict.get('detection_scores_' + str(i))) +
"%")
frame = visualize_output.draw_bounding_box(y1,
x1,
y2,
x2,
frame,
thickness=2,
fontsize=20,
outlineColor=(0, 255, 0),
textColor=(0, 255, 0),
display_str=display_str)
output_str += "==============================================================\n"
print(output_str)
return frame
def infer_image( graph, img, frame ):
# Load the image as a half-precision floating point array
graph.LoadTensor( img, 'user object' )
# Get the results from NCS
output, userobj = graph.GetResult()
# Get execution time
inference_time = graph.GetGraphOption( mvnc.GraphOption.TIME_TAKEN )
# Deserialize the output into a python dictionary
output_dict = deserialize_output.ssd(
output,
CONFIDANCE_THRESHOLD,
frame.shape )
# Print the results (each image/frame may have multiple objects)
for i in range( 0, output_dict['num_detections'] ):
# Filter a specific class/category
if( output_dict.get( 'detection_classes_' + str(i) ) == CLASS_PERSON ):
cur_time = strftime( "%Y_%m_%d_%H_%M_%S", localtime() )
print( "Person detected on " + cur_time )
# Extract top-left & bottom-right coordinates of detected objects
(y1, x1) = output_dict.get('detection_boxes_' + str(i))[0]
(y2, x2) = output_dict.get('detection_boxes_' + str(i))[1]
# Prep string to overlay on the image
display_str = (
labels[output_dict.get('detection_classes_' + str(i))]
+ ": "
+ str( output_dict.get('detection_scores_' + str(i) ) )
+ "%" )
# Overlay bounding boxes, detection class and scores
frame = visualize_output.draw_bounding_box(
y1, x1, y2, x2,
frame,
thickness=4,
color=(255, 255, 0),
display_str=display_str )
# Capture snapshots
img = Image.fromarray( frame )
photo = ( os.path.dirname(os.path.realpath(__file__))
+ "/captures/photo_"
+ cur_time + ".jpg" )
img.save( photo )
# If a display is available, show the image on which inference was performed
if 'DISPLAY' in os.environ:
img.show()
def infer_image(graph, img, frame):
# Load the image as a half-precision floating point array
graph.LoadTensor(img, 'user object')
# Get the results from NCS
output, userobj = graph.GetResult()
# Get execution time
inference_time = graph.GetGraphOption(mvnc.GraphOption.TIME_TAKEN)
# Deserialize the output into a python dictionary
#print("the shape of the frame is "+str(frame.shape))
output_dict = deserialize_output.ssd(output, CONFIDANCE_THRESHOLD,
frame.shape)
# Print the results (each image/frame may have multiple objects)
#print( "I found these objects in "
# + " ( %.2f ms ):" % ( numpy.sum( inference_time ) ) )
for i in range(0, output_dict['num_detections']):
if (i > 0):
f.write(" and ")
f.write("%3.1f%%\t" % output_dict['detection_scores_' + str(i)] +
labels[int(output_dict['detection_classes_' + str(i)])] +
": Top Left: " +
str(output_dict['detection_boxes_' + str(i)][0]) +
" Bottom Right: " +
str(output_dict['detection_boxes_' + str(i)][1]))
#f.write('\n')
# Draw bounding boxes around valid detections
(y1, x1) = output_dict.get('detection_boxes_' + str(i))[0]
(y2, x2) = output_dict.get('detection_boxes_' + str(i))[1]
# Prep string to overlay on the image
display_str = (labels[output_dict.get('detection_classes_' + str(i))] +
": " +
str(output_dict.get('detection_scores_' + str(i))) +
"%")
frame = visualize_output.draw_bounding_box(y1,
x1,
y2,
x2,
frame,
thickness=4,
color=(255, 255, 0),
display_str=display_str)
f.write('\n')
return frame
def infer_image( graph, img, frame ):
# Load the image as a half-precision floating point array
graph.LoadTensor( img, 'user object' )
# Get the results from NCS
output, userobj = graph.GetResult()
# Get execution time
inference_time = graph.GetGraphOption( mvnc.GraphOption.TIME_TAKEN )
# Deserialize the output into a python dictionary
output_dict = deserialize_output.ssd(
output,
CONFIDANCE_THRESHOLD,
frame.shape )
# Print the results (each image/frame may have multiple objects)
print( "I found these objects in "
+ " ( %.2f ms ):" % ( numpy.sum( inference_time ) ) )
for i in range( 0, output_dict['num_detections'] ):
print( "%3.1f%%\t" % output_dict['detection_scores_' + str(i)]
+ labels[ int(output_dict['detection_classes_' + str(i)]) ]
+ ": Top Left: " + str( output_dict['detection_boxes_' + str(i)][0] )
+ " Bottom Right: " + str( output_dict['detection_boxes_' + str(i)][1] ) )
# Draw bounding boxes around valid detections
(y1, x1) = output_dict.get('detection_boxes_' + str(i))[0]
(y2, x2) = output_dict.get('detection_boxes_' + str(i))[1]
# Prep string to overlay on the image
display_str = (
labels[output_dict.get('detection_classes_' + str(i))]
+ ": "
+ str( output_dict.get('detection_scores_' + str(i) ) )
+ "%" )
frame = visualize_output.draw_bounding_box(
y1, x1, y2, x2,
frame,
thickness=4,
color=(255, 255, 0),
display_str=display_str )
print( '\n' )
# If a display is available, show the image on which inference was performed
if 'DISPLAY' in os.environ:
cv2.imshow( 'NCS live inference', frame )
def infer_image(graph, img, frame):
# Get the results from NCS
output, inference_time = mv.infer_image(graph, img)
# Deserialize the output into a python dictionary
output_dict = deserialize_output.ssd(output, CONFIDENCE_THRESHOLD,
frame.shape)
# Print the results (each image/frame may have multiple objects)
for i in range(0, output_dict['num_detections']):
# Filter a specific class/category
if (output_dict.get('detection_classes_' + str(i)) == BANNED_CLASS):
print("Illegal object found!")
cur_time = strftime("%Y_%m_%d_%H_%M_%S", localtime())
# Extract top-left & bottom-right coordinates of detected objects
(y1, x1) = output_dict.get('detection_boxes_' + str(i))[0]
(y2, x2) = output_dict.get('detection_boxes_' + str(i))[1]
# Prep string to overlay on the image
display_str = (labels[output_dict.get('detection_classes_' + str(i))] +
": " +
str(output_dict.get('detection_scores_' + str(i))) +
"%")
# Overlay bounding boxes, detection class and scores
frame = visualize_output.draw_bounding_box(y1,
x1,
y2,
x2,
frame,
thickness=4,
color=(255, 255, 0),
display_str=display_str)
# Capture snapshots
photo = (os.path.dirname(os.path.realpath(__file__)) +
"/captures/photo_" + cur_time + ".jpg")
cv2.imwrite(photo, frame)
# If a display is available, show the image on which inference was performed
if 'DISPLAY' in os.environ:
cv2.imshow('NCS live inference', frame)
def infer_image(graph, img, frame):
# Load the image as a half-precision floating point array
graph.LoadTensor(img, 'user object')
# Get the results from NCS
output, userobj = graph.GetResult()
# Get execution time
inference_time = graph.GetGraphOption(mvnc.GraphOption.TIME_TAKEN)
# Deserialize the output into a python dictionary
output_dict = deserialize_output.ssd(output, CONFIDANCE_THRESHOLD,
frame.shape)
# Print the results (each image/frame may have multiple objects)
for i in range(0, output_dict['num_detections']):
# Filter a specific class/category
if (output_dict.get('detection_classes_' + str(i)) == CLASS_PERSON):
# Extract top-left & bottom-right coordinates of detected objects
(y1, x1) = output_dict.get('detection_boxes_' + str(i))[0]
(y2, x2) = output_dict.get('detection_boxes_' + str(i))[1]
# Prep string to overlay on the image
display_str = (
labels[output_dict.get('detection_classes_' + str(i))] + ": " +
str(output_dict.get('detection_scores_' + str(i))) + "%")
# Overlay bounding boxes, detection class and scores
frame = visualize_output.draw_bounding_box(y1,
x1,
y2,
x2,
frame,
thickness=4,
color=(255, 255, 0),
display_str=display_str)
# If a display is available, show the image on which inference was performed
if 'DISPLAY' in os.environ:
cv2.imshow('NCS live inference', frame)
def infer_image(movidius, graph, img, frame, labels):
# Load the image as a half-precision floating point array
graph.LoadTensor(img, 'user object')
# Get the results from NCS
output, _ = graph.GetResult()
# Get execution time
inference_time = graph.GetGraphOption(mvnc.GraphOption.TIME_TAKEN)
# Deserialize the output into a python dictionary
output_dict = deserialize_output.ssd(output, CONFIDENCE_THRESHOLD,
frame.shape)
num_detections = output_dict['num_detections']
# print( "%i objects identified in %.1f ms" % (num_detections, numpy.sum(inference_time)))
# publish over mqtt
publish_detctions(num_detections, output_dict, labels)
# If a display is available, show the image and results
if 'DISPLAY' in os.environ:
show_image(num_detections, output_dict, movidius, frame, labels)
def infer_image(graph, img, frame):
# Load the image as a half-precision floating point array
graph.LoadTensor(img, 'user object')
# Get the results from NCS
output, userobj = graph.GetResult()
# Get execution time
inference_time = graph.GetGraphOption(mvnc.GraphOption.TIME_TAKEN)
# Deserialize the output into a python dictionary
output_dict = deserialize_output.ssd(output, CONFIDANCE_THRESHOLD,
frame.shape)
# Print the results (each image/frame may have multiple objects)
for i in range(0, output_dict['num_detections']):
# Filter a specific class/category
if ((output_dict.get('detection_classes_' + str(i)) == CLASS_PERSON) or
(output_dict.get('detection_classes_' + str(i)) == CLASS_CAR)):
cur_time = strftime("%Y_%m_%d_%H_%M_%S", localtime())
print("Person detected on " + cur_time)
# Print the results (each image/frame may have multiple objects)
print("I found these objects in " + " ( %.2f ms ):" %
(numpy.sum(inference_time)))
print("%3.1f%%\t" % output_dict['detection_scores_' + str(i)] +
labels[int(output_dict['detection_classes_' + str(i)])] +
": Top Left: " +
str(output_dict['detection_boxes_' + str(i)][0]) +
" Bottom Right: " +
str(output_dict['detection_boxes_' + str(i)][1]))
# Extract top-left & bottom-right coordinates of detected objects
(y1, x1) = output_dict.get('detection_boxes_' + str(i))[0]
(y2, x2) = output_dict.get('detection_boxes_' + str(i))[1]
#center x, y target point
cx = x1 + (x2 - x1) / 2
cy = y1 + (y2 - y1) / 2
print(cx, cy)
# 使用1種字體
if cx <= (width * 4 / 7) and cx >= (width * 3 / 7) and cy <= (
height * 4 / 7) and cy >= (height * 3 / 7):
text = 'Locking'
cv2.putText(frame, text, (300, 40), cv2.FONT_HERSHEY_SIMPLEX,
1, (0, 0, 255), 2, cv2.LINE_AA)
# fire
if ((x2 - x1) * (y2 - y1)) >= (width * height) / 4:
arduino.write(b'5')
print('fire')
#time.sleep(0.5)
else:
text = 'Serching....'
cv2.putText(frame, text, (10, 40), cv2.FONT_HERSHEY_SIMPLEX, 1,
(0, 255, 0), 2, cv2.LINE_AA)
# Prep string to overlay on the image
display_str = (
labels[output_dict.get('detection_classes_' + str(i))] + ": " +
str(output_dict.get('detection_scores_' + str(i))) + "%")
# Overlay bounding boxes, detection class and scores
frame = visualize_output.draw_bounding_box(y1,
x1,
y2,
x2,
frame,
thickness=4,
color=(255, 255, 0),
display_str=display_str)
'''
# Capture snapshots
photo = ( os.path.dirname(os.path.realpath(__file__))
+ "/captures/photo_"
+ cur_time + ".jpg" )
cv2.imwrite( photo, frame )
'''
# If a display is available, show the image on which inference was performed
if 'DISPLAY' in os.environ:
# Cross Line
green = (0, 255, 0)
red = (0, 0, 255)
cv2.line(frame, (cross_line_w, cross_line_h - 80),
(cross_line_w, cross_line_h + 80), green, 2) #line y
cv2.line(frame, (cross_line_w - 20, cross_line_h - 5),
(cross_line_w - 20, cross_line_h + 5), green, 2) #'left
cv2.line(frame, (cross_line_w + 20, cross_line_h - 5),
(cross_line_w + 20, cross_line_h + 5), green, 2) #'right
cv2.line(frame, (cross_line_w - 40, cross_line_h - 5),
(cross_line_w - 40, cross_line_h + 5), green, 2) #''left
cv2.line(frame, (cross_line_w + 40, cross_line_h - 5),
(cross_line_w + 40, cross_line_h + 5), green, 2) #''right
cv2.line(frame, (cross_line_w - 60, cross_line_h - 5),
(cross_line_w - 60, cross_line_h + 5), green, 2) #'''left
cv2.line(frame, (cross_line_w + 60, cross_line_h - 5),
(cross_line_w + 60, cross_line_h + 5), green, 2) #'''right
cv2.line(frame, (cross_line_w - 80, cross_line_h),
(cross_line_w + 80, cross_line_h), green, 2) #line x
cv2.line(frame, (cross_line_w - 5, cross_line_h - 20),
(cross_line_w + 5, cross_line_h - 20), green, 2) #'up
cv2.line(frame, (cross_line_w - 5, cross_line_h + 20),
(cross_line_w + 5, cross_line_h + 20), green, 2) #'down
cv2.line(frame, (cross_line_w - 5, cross_line_h - 40),
(cross_line_w + 5, cross_line_h - 40), green, 2) #''up
cv2.line(frame, (cross_line_w - 5, cross_line_h + 40),
(cross_line_w + 5, cross_line_h + 40), green, 2) #''down
cv2.line(frame, (cross_line_w - 5, cross_line_h - 60),
(cross_line_w + 5, cross_line_h - 60), green, 2) #'''up
cv2.line(frame, (cross_line_w - 5, cross_line_h + 60),
(cross_line_w + 5, cross_line_h + 60), green, 2) #'''down
cv2.imshow('NCS live inference', frame)
def infer_image_fps(graph, img, frame, fps):
global direction
global counter
global plotbuff
a = []
# Load the image as a half-precision floating point array
graph.LoadTensor(img, 'user object')
# Get the results from NCS
output, userobj = graph.GetResult()
# Get execution time
inference_time = graph.GetGraphOption(mvnc.GraphOption.TIME_TAKEN)
# Deserialize the output into a python dictionary
output_dict = deserialize_output.ssd(output, CONFIDANCE_THRESHOLD,
frame.shape)
# elapsedtime = time.time() - starttime
# Print the results (each image/frame may have multiple objects)
# print( "I found these objects in ( %.2f ms ):" % ( numpy.sum( inference_time ) ) )
inftime = numpy.sum(inference_time)
numobj = (output_dict['num_detections'])
# create array for detected obj
a = [[] for _ in range(numobj)]
# print (numobj)
cpu = psutil.cpu_percent()
for i in range(0, output_dict['num_detections']):
print("%3.1f%%\t" % output_dict['detection_scores_' + str(i)] +
labels[int(output_dict['detection_classes_' + str(i)])] +
": Top Left: " +
str(output_dict['detection_boxes_' + str(i)][0]) +
" Bottom Right: " +
str(output_dict['detection_boxes_' + str(i)][1]))
# print(str(i))
a[i].append(output_dict['detection_scores_' + str(i)])
a[i].append(labels[int(output_dict['detection_classes_' + str(i)])])
a[i].append(str(output_dict['detection_boxes_' + str(i)][0]))
a[i].append(str(output_dict['detection_boxes_' + str(i)][1]))
# Draw bounding boxes around valid detections
(y1, x1) = output_dict.get('detection_boxes_' + str(i))[0]
(y2, x2) = output_dict.get('detection_boxes_' + str(i))[1]
# spencer: moving direction...
avgx = x2 - x1
avgy = y2 - y1
prevavg = (y1, x1)
plotbuff.appendleft(prevavg)
for j in np.arange(1, len(plotbuff)):
if plotbuff[j - 1] is None or plotbuff[j] is None:
continue
if counter >= 4 and j == 1 and len(plotbuff) == PLOTBUFFSIZE:
# dX = plotbuff[4][0] - plotbuff[j][0]
dY = plotbuff[j][0] - plotbuff[4][0]
# dY = plotbuff[4][1] - plotbuff[j][1]
dX = plotbuff[j][1] - plotbuff[4][1]
(dirY, dirX) = ("", "")
# print((dY,dX))
if np.abs(dX) > 10:
dirX = "EAST" if np.sign(dX) == 1 else "WEST"
if np.abs(dY) > 20:
dirY = "SOUTH" if np.sign(dY) == 1 else "NORTH"
if dirX != "" and dirY != "":
direction = "{}-{}".format(dirY, dirX)
else:
direction = dirX if dirX != "" else dirY
print(direction)
print(avgx)
if direction == "EAST" or direction == "EAST-SOUTH" or direction == "EAST-NORTH":
if avgx >= width - 200:
print("{} MOVING EAST OUT OF THE SCENE", format(avgx))
if direction == "WEST" or direction == "WEST-SOUTH" or direction == "WEST-NORTH":
if avgx <= 200:
print("{} MOVING WEST OUT OF THE SCENE", format(avgx))
# Prep string to overlay on the image
display_str = (labels[output_dict.get('detection_classes_' + str(i))] +
": " +
str(output_dict.get('detection_scores_' + str(i))) +
"%")
frame = visualize_output.draw_bounding_box(y1,
x1,
y2,
x2,
frame,
thickness=4,
color=(255, 255, 0),
display_str=display_str)
cv2.putText(frame, 'FPS:' + str(fps), (10, 50),
cv2.FONT_HERSHEY_SIMPLEX, 0.65, (255, 255, 255), 2,
cv2.LINE_AA)
cv2.putText(frame, direction, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.65,
(0, 0, 255), 3)
# print( '\n' )
# If a display is available, show the image on which inference was performed
counter += 1
if displayit == "on":
cv2.imshow('NCS live inference', frame)
# need to log here.
# print (elapsedtime)
# for tracking one human...
f.write(
str("{0:.2f}".format(elapsedtime)) + '\t' + str(cpu) + '\t' +
str("{0:.2f}".format(inftime)) + '\t' + str("{0:.2f}".format(fps)) +
'\t' + str(numobj) + '\t' + str(direction) + '\t' + str(a) + '\n')
# print(a)
# need plots...! for multiple objects
del (a)
preserve_range=True)
input = input[:, :, ::-1] # RGB -> BGR
input = input.astype(np.float16)
input = (input - np.float16(MEAN)) * SCALE # subtract mean & scale
# warm start with a dummy forward pass
graph.LoadTensor(input, 'user object')
output, userobj = graph.GetResult()
# actual timed inference
graph.LoadTensor(input, 'user object')
output, userobj = graph.GetResult()
inference_time = graph.GetGraphOption(mvnc.GraphOption.TIME_TAKEN)
# deserialize and print output
output_dict = deserialize_output.ssd(output, CONFIDENCE_THRESHOLD,
input.shape)
print("Execution time: " + str(np.sum(inference_time)) + "ms")
for i in range(0, output_dict['num_detections']):
print("%3.1f%%\t" % output_dict['detection_scores_' + str(i)] +
labels[int(output_dict['detection_classes_' + str(i)])] +
": Top Left: " +
str(output_dict['detection_boxes_' + str(i)][0]) +
" Bottom Right: " +
str(output_dict['detection_boxes_' + str(i)][1]))
finally:
# cleanup
graph.DeallocateGraph()
movidius.CloseDevice()
def infer_image(graph, img, frame, motor, pid):
# Load the image as a half-precision floating point array
graph.LoadTensor(img, 'user object')
# Get the results from NCS
output, userobj = graph.GetResult()
# Get execution time
inference_time = graph.GetGraphOption(mvnc.GraphOption.TIME_TAKEN)
# Deserialize the output into a python dictionary
output_dict = deserialize_output.ssd(output, CONFIDANCE_THRESHOLD,
frame.shape)
# Print the results (each image/frame may have multiple objects)
# print( "I found these objects in "
# + " ( %.2f ms ):" % ( numpy.sum( inference_time ) ) )
count_person = 0
for i in range(0, output_dict['num_detections']):
# Only interested in person.
if labels[output_dict['detection_classes_' + str(i)]] != "15: person":
continue
if count_person > 0:
continue
count_person = count_person + 1
#print( "%3.1f%%\t" % output_dict['detection_scores_' + str(i)]
# + labels[ int(output_dict['detection_classes_' + str(i)]) ]
# + ": Top Left: " + str( output_dict['detection_boxes_' + str(i)][0] )
# + " Bottom Right: " + str( output_dict['detection_boxes_' + str(i)][1] ) )
# Draw bounding boxes around valid detections
(y1, x1) = output_dict.get('detection_boxes_' + str(i))[0]
(y2, x2) = output_dict.get('detection_boxes_' + str(i))[1]
mid = (x1 + x2) / 2
dis = 160 - mid
pid.update(dis)
print("distance: %d " % dis, end='', flush=True)
if pid.output > 30:
motor.direction = int(numpy.interp(pid.output, [0, 160], [1, 100]))
print("direction: %d\n" % motor.direction)
elif pid.output < -30:
motor.direction = int(
numpy.interp(pid.output, [-160, 0], [-100, -1]))
print("direction: %d\n" % motor.direction)
else:
motor.direction = 0
# Prep string to overlay on the image
#display_str = (
# labels[output_dict.get('detection_classes_' + str(i))]
# + ": "
# + str( output_dict.get('detection_scores_' + str(i) ) )
# + "%" )
#frame = visualize_output.draw_bounding_box(
# y1, x1, y2, x2,
# frame,
# thickness=4,
# color=(255, 255, 0),
# display_str=display_str )
if count_person == 0:
motor.direction = 0
def infer_image(graph, img, frame):
# Load the image as a half-precision floating point array
graph.LoadTensor(img, 'user object')
# Get the results from NCS
output, userobj = graph.GetResult()
# Get execution time
inference_time = graph.GetGraphOption(mvnc.GraphOption.TIME_TAKEN)
# Deserialize the output into a python dictionary
output_dict = deserialize_output.ssd(output, CONFIDANCE_THRESHOLD,
frame.shape)
# Print the results (each image/frame may have multiple objects)
for i in range(0, output_dict['num_detections']):
# Filter a specific class/category
if ((output_dict.get('detection_classes_' + str(i)) == CLASS_PERSON) or
(output_dict.get('detection_classes_' + str(i)) == CLASS_CAR) or
(output_dict.get('detection_classes_' + str(i)) == CLASS_FACE)):
cur_time = strftime("%Y_%m_%d_%H_%M_%S", localtime())
print("Person detected on " + cur_time)
# Print the results (each image/frame may have multiple objects)
print("I found these objects in " + " ( %.2f ms ):" %
(numpy.sum(inference_time)))
print("%3.1f%%\t" % output_dict['detection_scores_' + str(i)] +
labels[int(output_dict['detection_classes_' + str(i)])] +
": Top Left: " +
str(output_dict['detection_boxes_' + str(i)][0]) +
" Bottom Right: " +
str(output_dict['detection_boxes_' + str(i)][1]))
# Extract top-left & bottom-right coordinates of detected objects
(y1, x1) = output_dict.get('detection_boxes_' + str(i))[0]
(y2, x2) = output_dict.get('detection_boxes_' + str(i))[1]
#center x, y target point of object box
cx = x1 + (x2 - x1) / 2
cy = y1 + (y2 - y1) / 2
print(cx, cy)
# print target number 終端顯示目標號碼
print('#' + str(i + 1))
# 追蹤目標識別率最大#1 OR #2
if (i + 1) == 1 or 2:
#servo motor
if cx < width * 3 / 7:
arduino.write(b'4')
print('4')
if cx < width * 2 / 7:
arduino.write(b'4')
print('44')
if cx < width * 1 / 7:
arduino.write(b'44')
print('444')
if cx > width * 4 / 7:
arduino.write(b'6')
print('6')
if cx > width * 5 / 7:
arduino.write(b'6')
print('66')
if cx > width * 6 / 7:
arduino.write(b'66')
print('666')
if cy < height * 3 / 7:
arduino.write(b'2')
print('2')
if cy < height * 2 / 7:
arduino.write(b'2')
print('22')
if cy < height * 1 / 7:
arduino.write(b'22')
print('222')
if cy > height * 4 / 7:
arduino.write(b'8')
print('8')
if cy > height * 5 / 7:
arduino.write(b'8')
print('88')
if cy > height * 6 / 7:
arduino.write(b'88')
print('888')
'''
#servo motor
if cx < width*3 / 7 :
arduino.write(b'4')
print ('4')
if cx < width*2 / 7 :
arduino.write(b'4')
print ('44')
if cx < width*1 / 7 :
arduino.write(b'44')
print ('444')
if cx > width*4 / 7 :
arduino.write(b'6')
print ('6')
if cx > width*5 / 7 :
arduino.write(b'6')
print ('66')
if cx > width*6 / 7 :
arduino.write(b'66')
print ('666')
if cy < height*3 / 7 :
arduino.write(b'2')
print ('2')
if cy < height*2 / 7 :
arduino.write(b'2')
print ('22')
if cy < height*1 / 7 :
arduino.write(b'22')
print ('222')
if cy > height*4 / 7 :
arduino.write(b'8')
print ('8')
if cy > height*5 / 7 :
arduino.write(b'8')
print ('88')
if cy > height*6 / 7 :
arduino.write(b'88')
print ('888')
'''
# Prep string to overlay on the image
display_str = (
labels[output_dict.get('detection_classes_' + str(i))] + ": " +
str(output_dict.get('detection_scores_' + str(i))) + "%")
# Overlay bounding boxes, detection class and scores
frame = visualize_output.draw_bounding_box(y1,
x1,
y2,
x2,
frame,
thickness=4,
color=(255, 255, 0),
display_str=display_str)
# follow box of number, 隨目標框顯示目標號碼(號碼依目標識別率最大依序排列)
cv2.putText(frame, '#' + str(i + 1), (x1, y1 - 10),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2,
cv2.LINE_AA)
# 使用1種字體,fire or Capture snapshots
if cx <= (width * 4 / 7) and cx >= (width * 3 / 7) and cy <= (
height * 4 / 7) and cy >= (height * 3 / 7):
text = 'Locking'
cv2.putText(frame, text, (300, 40), cv2.FONT_HERSHEY_SIMPLEX,
1, (0, 0, 255), 2, cv2.LINE_AA)
# show targets number
cv2.putText(frame, 'Targets:' + max(str(i + 1)), (10, 100),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2,
cv2.LINE_AA)
# fire to near me and the #1, 攻擊靠近本體(高威脅)和#1目標
if ((x2 - x1) *
(y2 - y1)) >= (width * height) / 3 and (i + 1) == 1:
arduino.write(b'5')
print('fire')
text = 'FIRE'
cv2.putText(frame, text, (500, 40),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2,
cv2.LINE_AA)
# Cross Line in snapshots
green = (0, 255, 0)
red = (0, 0, 255)
cv2.line(frame, (cross_line_w, cross_line_h - 80),
(cross_line_w, cross_line_h + 80), green,
2) #line y
cv2.line(frame, (cross_line_w - 20, cross_line_h - 5),
(cross_line_w - 20, cross_line_h + 5), green,
2) #,left
cv2.line(frame, (cross_line_w + 20, cross_line_h - 5),
(cross_line_w + 20, cross_line_h + 5), green,
2) #,right
cv2.line(frame, (cross_line_w - 40, cross_line_h - 5),
(cross_line_w - 40, cross_line_h + 5), green,
2) #,,left
cv2.line(frame, (cross_line_w + 40, cross_line_h - 5),
(cross_line_w + 40, cross_line_h + 5), green,
2) #,,right
cv2.line(frame, (cross_line_w - 60, cross_line_h - 5),
(cross_line_w - 60, cross_line_h + 5), green,
2) #,,,left
cv2.line(frame, (cross_line_w + 60, cross_line_h - 5),
(cross_line_w + 60, cross_line_h + 5), green,
2) #,,,right
cv2.line(frame, (cross_line_w - 80, cross_line_h),
(cross_line_w + 80, cross_line_h), green,
2) #line x
cv2.line(frame, (cross_line_w - 5, cross_line_h - 20),
(cross_line_w + 5, cross_line_h - 20), green,
2) #,up
cv2.line(frame, (cross_line_w - 5, cross_line_h + 20),
(cross_line_w + 5, cross_line_h + 20), green,
2) #,down
cv2.line(frame, (cross_line_w - 5, cross_line_h - 40),
(cross_line_w + 5, cross_line_h - 40), green,
2) #,,up
cv2.line(frame, (cross_line_w - 5, cross_line_h + 40),
(cross_line_w + 5, cross_line_h + 40), green,
2) #,,down
cv2.line(frame, (cross_line_w - 5, cross_line_h - 60),
(cross_line_w + 5, cross_line_h - 60), green,
2) #,,,up
cv2.line(frame, (cross_line_w - 5, cross_line_h + 60),
(cross_line_w + 5, cross_line_h + 60), green,
2) #,,,down
# Capture snapshots
photo = (os.path.dirname(os.path.realpath(__file__)) +
"/captures/photo_" + cur_time + ".jpg")
cv2.imwrite(photo, frame)
else:
text = 'Serching....'
cv2.putText(frame, text, (10, 40), cv2.FONT_HERSHEY_SIMPLEX, 1,
(0, 255, 0), 2, cv2.LINE_AA)
# show targets number
cv2.putText(frame, 'Targets:' + max(str(i + 1)), (10, 100),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2,
cv2.LINE_AA)
'''
# Capture snapshots
photo = ( os.path.dirname(os.path.realpath(__file__))
+ "/captures/photo_"
+ cur_time + ".jpg" )
cv2.imwrite( photo, frame )
'''
# If a display is available, show the image on which inference was performed
if 'DISPLAY' in os.environ:
# Cross Line
green = (0, 255, 0)
red = (0, 0, 255)
cv2.line(frame, (cross_line_w, cross_line_h - 80),
(cross_line_w, cross_line_h + 80), green, 2) #line y
cv2.line(frame, (cross_line_w - 20, cross_line_h - 5),
(cross_line_w - 20, cross_line_h + 5), green, 2) #,left
cv2.line(frame, (cross_line_w + 20, cross_line_h - 5),
(cross_line_w + 20, cross_line_h + 5), green, 2) #,right
cv2.line(frame, (cross_line_w - 40, cross_line_h - 5),
(cross_line_w - 40, cross_line_h + 5), green, 2) #,,left
cv2.line(frame, (cross_line_w + 40, cross_line_h - 5),
(cross_line_w + 40, cross_line_h + 5), green, 2) #,,right
cv2.line(frame, (cross_line_w - 60, cross_line_h - 5),
(cross_line_w - 60, cross_line_h + 5), green, 2) #,,,left
cv2.line(frame, (cross_line_w + 60, cross_line_h - 5),
(cross_line_w + 60, cross_line_h + 5), green, 2) #,,,right
cv2.line(frame, (cross_line_w - 80, cross_line_h),
(cross_line_w + 80, cross_line_h), green, 2) #line x
cv2.line(frame, (cross_line_w - 5, cross_line_h - 20),
(cross_line_w + 5, cross_line_h - 20), green, 2) #,up
cv2.line(frame, (cross_line_w - 5, cross_line_h + 20),
(cross_line_w + 5, cross_line_h + 20), green, 2) #,down
cv2.line(frame, (cross_line_w - 5, cross_line_h - 40),
(cross_line_w + 5, cross_line_h - 40), green, 2) #,,up
cv2.line(frame, (cross_line_w - 5, cross_line_h + 40),
(cross_line_w + 5, cross_line_h + 40), green, 2) #,,down
cv2.line(frame, (cross_line_w - 5, cross_line_h - 60),
(cross_line_w + 5, cross_line_h - 60), green, 2) #,,,up
cv2.line(frame, (cross_line_w - 5, cross_line_h + 60),
(cross_line_w + 5, cross_line_h + 60), green, 2) #,,,down
cv2.imshow('NCS live inference', frame)
def infer_image(graph, img, frame):
# Load the image as a half-precision floating point array
graph.LoadTensor(img, 'user object')
# Get the results from NCS
output, userobj = graph.GetResult()
# Get execution time
inference_time = graph.GetGraphOption(mvnc.GraphOption.TIME_TAKEN)
# Deserialize the output into a python dictionary
output_dict = deserialize_output.ssd(output, CONFIDANCE_THRESHOLD,
frame.shape)
# Print the results (each image/frame may have multiple objects)
for i in range(0, output_dict['num_detections']):
# Filter a specific class/category
if ((output_dict.get('detection_classes_' + str(i)) == CLASS_PERSON) or
(output_dict.get('detection_classes_' + str(i)) == CLASS_CAR)):
cur_time = strftime("%Y_%m_%d_%H_%M_%S", localtime())
print("Person detected on " + cur_time)
# Print the results (each image/frame may have multiple objects)
print("I found these objects in " + " ( %.2f ms ):" %
(numpy.sum(inference_time)))
print("%3.1f%%\t" % output_dict['detection_scores_' + str(i)] +
labels[int(output_dict['detection_classes_' + str(i)])] +
": Top Left: " +
str(output_dict['detection_boxes_' + str(i)][0]) +
" Bottom Right: " +
str(output_dict['detection_boxes_' + str(i)][1]))
# Extract top-left & bottom-right coordinates of detected objects
(y1, x1) = output_dict.get('detection_boxes_' + str(i))[0]
(y2, x2) = output_dict.get('detection_boxes_' + str(i))[1]
#center x, y target point of object box
cx = x1 + (x2 - x1) / 2
cy = y1 + (y2 - y1) / 2
print(cx, cy)
# print target number 終端顯示目標號碼
print('#' + str(i + 1))
# 追蹤目標識別率最大#1
if (i + 1) == 1:
#servo motor
if cx < width * 5 / 10 and cx > width * 4 / 10:
arduino.write(b'e')
print('e')
if cx < width * 4 / 10 and cx > width * 3 / 10:
arduino.write(b'd')
print('d')
if cx < width * 3 / 10 and cx > width * 2 / 10:
arduino.write(b'c')
print('c')
if cx < width * 2 / 10 and cx > width / 10:
arduino.write(b'b')
print('b')
if cx < width / 10:
arduino.write(b'a')
print('a')
if cx > width * 6 / 10 and cx < width * 7 / 10:
arduino.write(b'f')
print('f')
if cx > width * 7 / 10 and cx < width * 8 / 10:
arduino.write(b'g')
print('g')
if cx > width * 8 / 10 and cx < width * 9 / 10:
arduino.write(b'h')
print('h')
if cx > width * 9 / 10:
arduino.write(b'i')
print('i')
if cy < height * 5 / 10 and cy > height * 4 / 10:
arduino.write(b'n')
print('n')
if cy < height * 4 / 10 and cy > height * 3 / 10:
arduino.write(b'm')
print('m')
if cy < height * 3 / 10 and cy > height * 2 / 10:
arduino.write(b'l')
print('l')
if cy < height * 2 / 10 and cy > height / 10:
arduino.write(b'k')
print('k')
if cy < height / 10:
arduino.write(b'j')
print('j')
if cy > height * 6 / 10 and cy < height * 7 / 10:
arduino.write(b'o')
print('o')
if cy > height * 7 / 10 and cy < height * 8 / 10:
arduino.write(b'p')
print('p')
if cy > height * 8 / 10 and cy < height * 9 / 10:
arduino.write(b'q')
print('q')
if cy > height * 9 / 10:
arduino.write(b'r')
print('r')
'''
#servo motor
if cx < width*5/ 10 and cx > width*4/ 10 :
arduino.write(b'e')
print ('e')
if cx < width*4/ 10 and cx > width*3/ 10 :
arduino.write(b'd')
print ('d')
if cx < width*3/ 10 and cx > width*2/ 10 :
arduino.write(b'c')
print ('c')
if cx < width*2/ 10 and cx > width/ 10 :
arduino.write(b'b')
print ('b')
if cx < width/ 10 :
arduino.write(b'a')
print ('a')
if cx > width*6 / 10 and cx < width*7/ 10 :
arduino.write(b'f')
print ('f')
if cx > width*7/ 10 and cx < width*8/ 10 :
arduino.write(b'g')
print ('g')
if cx > width*8/ 10 and cx < width*9/ 10 :
arduino.write(b'h')
print ('h')
if cx > width*9/ 10 :
arduino.write(b'i')
print ('i')
if cy < height*5/ 10 and cy > height*4/ 10 :
arduino.write(b'n')
print ('n')
if cy < height*4/ 10 and cy > height*3/ 10 :
arduino.write(b'm')
print ('m')
if cy < height*3/ 10 and cy > height*2/ 10 :
arduino.write(b'l')
print ('l')
if cy < height*2/ 10 and cy > height/ 10 :
arduino.write(b'k')
print ('k')
if cy < height/ 10:
arduino.write(b'j')
print ('j')
if cy > height*6 / 10 and cy < height*7 / 10 :
arduino.write(b'o')
print ('o')
if cy > height*7 / 10 and cy < height*8 / 10 :
arduino.write(b'p')
print ('p')
if cy > height*8 / 10 and cy < height*9 / 10 :
arduino.write(b'q')
print ('q')
if cy > height*9 / 10:
arduino.write(b'r')
print ('r')
'''
# Prep string to overlay on the image
display_str = (
labels[output_dict.get('detection_classes_' + str(i))] + ": " +
str(output_dict.get('detection_scores_' + str(i))) + "%")
# Overlay bounding boxes, detection class and scores
frame = visualize_output.draw_bounding_box(y1,
x1,
y2,
x2,
frame,
thickness=4,
color=(255, 255, 0),
display_str=display_str)
# follow box of number, 隨目標框顯示目標號碼(號碼依目標識別率最大依序排列)
cv2.putText(frame, '#' + str(i + 1), (x1, y1 - 10),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2,
cv2.LINE_AA)
cv2.putText(frame, 'Targets:' + str(i + 1), (10, 100),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2,
cv2.LINE_AA)
# fire
if ((x2 - x1) *
(y2 - y1)) >= (width * height) / 4 and (i + 1) == 1:
arduino.write(b'5')
print('fire')
text = 'FIRE'
cv2.putText(frame, text, (500, 40), cv2.FONT_HERSHEY_SIMPLEX,
1, (0, 0, 255), 2, cv2.LINE_AA)
# Capture snapshots
photo = (os.path.dirname(os.path.realpath(__file__)) +
"/captures/photo_" + cur_time + ".jpg")
cv2.imwrite(photo, frame)
'''
# Capture snapshots
photo = ( os.path.dirname(os.path.realpath(__file__))
+ "/captures/photo_"
+ cur_time + ".jpg" )
cv2.imwrite( photo, frame )
'''
# If a display is available, show the image on which inference was performed
if 'DISPLAY' in os.environ:
cv2.imshow('NCS live inference', frame)
def infer_image_fps(graph, img, frame, fps):
# global direction
global counter
# global plotbuff
a = []
# Load the image as a half-precision floating point array
graph.LoadTensor(img, 'user object')
# Get the results from NCS
output, userobj = graph.GetResult()
# Get execution time
inference_time = graph.GetGraphOption(mvnc.GraphOption.TIME_TAKEN)
# Deserialize the output into a python dictionary
output_dict = deserialize_output.ssd(output, CONFIDANCE_THRESHOLD,
frame.shape)
# elapsedtime = time.time() - starttime
# Print the results (each image/frame may have multiple objects)
# print( "I found these objects in ( %.2f ms ):" % ( numpy.sum( inference_time ) ) )
inftime = numpy.sum(inference_time)
numobj = (output_dict['num_detections'])
# create array for detected obj
a = [[] for _ in range(numobj)]
# print (numobj)
cpu = psutil.cpu_percent()
for i in range(0, output_dict['num_detections']):
print("%3.1f%%\t" % output_dict['detection_scores_' + str(i)] +
labels[int(output_dict['detection_classes_' + str(i)])] +
": Top Left: " +
str(output_dict['detection_boxes_' + str(i)][0]) +
" Bottom Right: " +
str(output_dict['detection_boxes_' + str(i)][1]))
# print(str(i))
a[i].append(output_dict['detection_scores_' + str(i)])
a[i].append(labels[int(output_dict['detection_classes_' + str(i)])])
a[i].append(str(output_dict['detection_boxes_' + str(i)][0]))
a[i].append(str(output_dict['detection_boxes_' + str(i)][1]))
# Draw bounding boxes around valid detections
(y1, x1) = output_dict.get('detection_boxes_' + str(i))[0]
(y2, x2) = output_dict.get('detection_boxes_' + str(i))[1]
# Prep string to overlay on the image
display_str = (labels[output_dict.get('detection_classes_' + str(i))] +
": " +
str(output_dict.get('detection_scores_' + str(i))) +
"%")
frame = visualize_output.draw_bounding_box(y1,
x1,
y2,
x2,
frame,
thickness=4,
color=(255, 255, 0),
display_str=display_str)
cv2.putText(frame, 'FPS:' + str(fps), (10, 50),
cv2.FONT_HERSHEY_SIMPLEX, 0.65, (255, 255, 255), 2,
cv2.LINE_AA)
# cv2.putText(frame, direction, (10,30), cv2.FONT_HERSHEY_SIMPLEX, 0.65, (0,0,255),3)
# print( '\n' )
# If a display is available, show the image on which inference was performed
counter += 1
if displayit == "on":
cv2.imshow('NCS live inference', frame)
# need to log here.
# print (elapsedtime)
# for tracking one human...
# print(str("{0:.2f}".format(elapsedtime)) + '\t' + str(cpu) + '\t' + str("{0:.2f}".format(inftime)) + '\t' + str("{0:.2f}".format(fps)) +'\t' + str(numobj)+'\t'+str(a)+'\n')
# need to save to redis.
save = {
"elapsedtime": "{0:.2f}".format(elapsedtime),
"CPU": str(cpu),
"inftime": str("{0:.2f}".format(inftime)),
"fps": str("{0:.2f}".format(fps)),
"numberofobjects": str(numobj),
"a": str(a)
}
r.hmset(counter, save)
# r.sadd("myset",save)
print(r.hgetall(counter))
#print(save)
# need plots...! for multiple objects
del (a)
def infer_image(graph, img, frame):
# Load the image as a half-precision floating point array
graph.LoadTensor(img, 'user object')
# Get the results from NCS
output, userobj = graph.GetResult()
# Get execution time
inference_time = graph.GetGraphOption(mvnc.GraphOption.TIME_TAKEN)
# Deserialize the output into a python dictionary
output_dict = deserialize_output.ssd(output, CONFIDANCE_THRESHOLD,
frame.shape)
# Print the results (each image/frame may have multiple objects)
for i in range(0, output_dict['num_detections']):
# Filter a specific class/category
if (output_dict.get('detection_classes_' + str(i)) == CLASS_PERSON):
# Time
d = datetime.now()
cur_time = strftime("%Y_%m_%d_%H_%M_%S", localtime())
print("Person detected on " + cur_time)
# Extract top-left & bottom-right coordinates of detected objects
(y1, x1) = output_dict.get('detection_boxes_' + str(i))[0]
(y2, x2) = output_dict.get('detection_boxes_' + str(i))[1]
# Prep string to overlay on the image
display_str = (
labels[output_dict.get('detection_classes_' + str(i))] + ": " +
str(output_dict.get('detection_scores_' + str(i))) + "%")
# Overlay bounding boxes, detection class and scores
frame = visualize_output.draw_bounding_box(y1,
x1,
y2,
x2,
frame,
thickness=4,
color=(255, 255, 0),
display_str=display_str)
# Capture snapshots
photo = (os.path.dirname(os.path.realpath(__file__)) +
"/captures/photo_" + cur_time + ".jpg")
cv2.imwrite(photo, frame)
#IFTT notification
global d_last
d_diff = d - d_last
if (d_diff.seconds > MINTIME):
print('\033[31m' + 'Send Notification to IFTTT --- ' +
'\033[0m') # Red text
#print(d)
#print(d_last)
#print(d_diff)
#print(" ")
r = requests.post(
'https://maker.ifttt.com/trigger/rasp_seccam_triggered/with/key/c_6oKb50WdIWAaelvo3EINQ8ZU9ibwxNFJiBV1phPuh',
params={
"value1": cur_time,
"value2": photo,
"value3": "none"
})
os.system("rclone copy " + photo + " gdrive:rclone")
d_last = d
# If a display is available, show the image on which inference was performed
if 'DISPLAY' in os.environ:
cv2.imshow('NCS live inference', frame)
def infer_image(graph, img, frame):
#from pySX127x_master.Tegwyns_LoRa_Beacon import LoRaBeacon
#from pySX127x_master import Tegwyns_LoRa_Beacon
# Load the image as a half-precision floating point array
graph.LoadTensor(img, 'user object')
# Get the results from NCS
output, userobj = graph.GetResult()
# Get execution time
inference_time = graph.GetGraphOption(mvnc.GraphOption.TIME_TAKEN)
# Deserialize the output into a python dictionary
output_dict = deserialize_output.ssd(output, CONFIDANCE_THRESHOLD,
frame.shape)
# Print the results (each image/frame may have multiple objects)
for i in range(0, output_dict['num_detections']):
# Filter a specific class/category
if (output_dict.get('detection_classes_' + str(i)) == CLASS_PERSON):
cur_time = strftime("%Y_%m_%d_%H_%M_%S", localtime())
print("Person detected on " + cur_time)
print(".... Press q to quit ..... ")
# Extract top-left & bottom-right coordinates of detected objects
(y1, x1) = output_dict.get('detection_boxes_' + str(i))[0]
(y2, x2) = output_dict.get('detection_boxes_' + str(i))[1]
#print (y1, x1)
# Prep string to overlay on the image
display_str = (
labels[output_dict.get('detection_classes_' + str(i))] + ": " +
str(output_dict.get('detection_scores_' + str(i))) + "%")
print(display_str)
print(y1, x1)
print(y2, x2)
# Overlay bounding boxes, detection class and scores
frame = visualize_output.draw_bounding_box(y1,
x1,
y2,
x2,
frame,
thickness=4,
color=(255, 255, 0),
display_str=display_str)
global myString
myString = display_str + " , " + "(" + str(y1) + "," + str(
x1) + ")" + "," + "(" + str(y2) + "," + str(x2) + ")"
###########################################################################################
lora.start()
###########################################################################################
# Capture snapshots
photo = (os.path.dirname(os.path.realpath(__file__)) +
"/captures/photo_" + cur_time + ".jpg")
cv2.imwrite(photo, frame)
# If a display is available, show the image on which inference was performed
if 'DISPLAY' in os.environ:
cv2.imshow('NCS live inference', frame)
def infer_image( graph, img, frame, motor ):
# Load the image as a half-precision floating point array
graph.LoadTensor( img, 'user object' )
# Get the results from NCS
output, userobj = graph.GetResult()
# Get execution time
inference_time = graph.GetGraphOption( mvnc.GraphOption.TIME_TAKEN )
# Deserialize the output into a python dictionary
output_dict = deserialize_output.ssd(
output,
CONFIDANCE_THRESHOLD,
frame.shape )
# Print the results (each image/frame may have multiple objects)
print( "I found these objects in "
+ " ( %.2f ms ):" % ( numpy.sum( inference_time ) ) )
count_person = 0
for i in range( 0, output_dict['num_detections'] ):
# Only interested in person.
if labels[output_dict['detection_classes_' + str(i)]] != "15: person":
continue
if count_person > 0:
continue
count_person = count_person + 1
print( "%3.1f%%\t" % output_dict['detection_scores_' + str(i)]
+ labels[ int(output_dict['detection_classes_' + str(i)]) ]
+ ": Top Left: " + str( output_dict['detection_boxes_' + str(i)][0] )
+ " Bottom Right: " + str( output_dict['detection_boxes_' + str(i)][1] ) )
# Draw bounding boxes around valid detections
(y1, x1) = output_dict.get('detection_boxes_' + str(i))[0]
(y2, x2) = output_dict.get('detection_boxes_' + str(i))[1]
print("x1 x2: %d %d\n" % (x1, x2))
mid = (x1+x2)/2
dis = 320 - mid
print("distance: %d\n" % dis)
if dis > 20:
motor.direction = "forward"
elif dis < -20:
motor.direction = "backward"
else:
motor.direction = "stop"
# Prep string to overlay on the image
display_str = (
labels[output_dict.get('detection_classes_' + str(i))]
+ ": "
+ str( output_dict.get('detection_scores_' + str(i) ) )
+ "%" )
frame = visualize_output.draw_bounding_box(
y1, x1, y2, x2,
frame,
thickness=4,
color=(255, 255, 0),
display_str=display_str )
print( '\n' )
# If a display is available, show the image on which inference was performed
if 'DISPLAY' in os.environ:
cv2.imshow( 'NCS live inference', frame )
