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Copy pathMobileNetSSDwithRealSenseTF.py
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MobileNetSSDwithRealSenseTF.py
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import sys
if sys.version_info.major < 3 or sys.version_info.minor < 4:
print("Please using python3.4 or greater!")
exit(1)
import os
import numpy as np
import argparse
import cv2
import pyrealsense2 as rs
import tensorflow as tf
sys.path.append('..')
from utils import label_map_util
import visualization_utils as vis_util
from OpenGL.GL import *
from OpenGL.GLU import *
from OpenGL.GLUT import *
inWidth = 300
inHeight = 300
WHRatio = inWidth / float(inHeight)
inScaleFactor = 0.007843
meanVal = 127.5
pipeline = None
try:
HOME_PATH = os.path.expanduser('~')
CWD_PATH = 'model'
#MODEL_NAME = 'ssdlite_mobilenet_v2_coco_2018_05_09'
PATH_TO_CKPT = os.path.join(CWD_PATH, 'frozen_inference_graph.pb')
PATH_TO_LABELS = os.path.join(CWD_PATH, 'mscoco_label_map.pbtxt')
swapRB = True
NUM_CLASSES = 90
## Load the label map.
# Label maps map indices to category names, so that when the convolution
# network predicts `5`, we know that this corresponds to `airplane`.
# Here we use internal utility functions, but anything that returns a
# dictionary mapping integers to appropriate string labels would be fine
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)
# Load the Tensorflow model into memory.
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
sess = tf.Session(graph=detection_graph)
# Input tensor is the image
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Output tensors are the detection boxes, scores, and classes
# Each box represents a part of the image where a particular object was detected
detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represents level of confidence for each of the objects.
# The score is shown on the result image, together with the class label.
detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
# Number of objects detected
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
# Configure depth and color streams RealSense D435
pipeline = rs.pipeline()
config = rs.config()
config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)
# Start streaming
pipeline.start(config)
except:
import traceback
traceback.print_exc()
# Stop streaming
if pipeline != None:
pipeline.stop()
print("\n\nFinished\n\n")
sys.exit()
def init():
glClearColor(0.7, 0.7, 0.7, 0.7)
def idle():
glutPostRedisplay()
def resizeview(w, h):
glViewport(0, 0, w, h)
glLoadIdentity()
glOrtho(-w / 640, w / 640, -h / 480, h / 480, -1.0, 1.0)
def keyboard(key, x, y):
key = key.decode('utf-8')
if key == 'q':
sys.exit()
def camThread():
# Wait for a coherent pair of frames: depth and color
frames = pipeline.wait_for_frames()
depth_frame = frames.get_depth_frame()
color_frame = frames.get_color_frame()
if not depth_frame or not color_frame:
return
# Convert images to numpy arrays
depth_image = np.asanyarray(depth_frame.get_data())
color_image = np.asanyarray(color_frame.get_data())
height = color_image.shape[0]
width = color_image.shape[1]
frame_expanded = np.expand_dims(color_image, axis=0)
# Perform the actual detection by running the model with the image as input
(boxes, scores, classes, num) = sess.run(
[detection_boxes, detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: frame_expanded})
# Draw the results of the detection (aka 'visulaize the results')
img = vis_util.visualize_boxes_and_labels_on_image_array(
color_image,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=2,
min_score_thresh=0.55,
depth_frame=depth_frame,
height=height,
width=width)
glTexImage2D(GL_TEXTURE_2D, 0, GL_RGB, width, height, 0, GL_RGB, GL_UNSIGNED_BYTE, cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)
glColor3f(1.0, 1.0, 1.0)
glEnable(GL_TEXTURE_2D)
glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_LINEAR)
glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_LINEAR)
glBegin(GL_QUADS)
glTexCoord2d(0.0, 1.0)
glVertex3d(-1.0, -1.0, 0.0)
glTexCoord2d(1.0, 1.0)
glVertex3d( 1.0, -1.0, 0.0)
glTexCoord2d(1.0, 0.0)
glVertex3d( 1.0, 1.0, 0.0)
glTexCoord2d(0.0, 0.0)
glVertex3d(-1.0, 1.0, 0.0)
glEnd()
glFlush()
glutSwapBuffers()
try:
glutInitWindowPosition(0, 0)
glutInit(sys.argv)
glutInitDisplayMode(GLUT_RGBA | GLUT_DOUBLE )
glutCreateWindow("DEMO")
glutFullScreen()
glutDisplayFunc(camThread)
glutReshapeFunc(resizeview)
glutKeyboardFunc(keyboard)
init()
glutIdleFunc(idle)
glutMainLoop()
except:
import traceback
traceback.print_exc()
finally:
# Stop streaming
if pipeline != None:
pipeline.stop()
print("\n\nFinished\n\n")
sys.exit()