def video_demo(args):
cap = cv2.VideoCapture('data/demo_video.mp4')
while (cap.isOpened()):
ret, frame_bgr = cap.read()
if not ret:
break
with Tick('interference'):
detections = predict(frame_bgr)
overlap = plot(detections, frame_bgr)
cv2.imshow('overlap', overlap)
if 27 == cv2.waitKey(1):
break
def image_callback(self,ros_data):
if self.input_compressed:
np_arr = np.fromstring(ros_data.data, np.uint8)
img_input = cv2.imdecode(np_arr,1)
else:
img_input = bridge.imgmsg_to_cv2(ros_data)
with Tick('interference'):
detections = predict(img_input)
overlap = plot(detections,img_input)
detections = overlap
cv2.imshow('overlap',overlap)
cv2.waitKey(1)
def images_demo(args):
for fn in glob.glob('data/COCO/*'):
frame_bgr = cv2.imread(fn)
with Tick('interference'):
detections = predict(frame_bgr)
rcnn_overlap = plot(detections, frame_bgr)
fig = plt.figure(figsize=(12, 4),
dpi=100,
facecolor='w',
edgecolor='k')
sub_plot(fig, 1, 2, 1, 'image',
cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB))
sub_plot(fig, 1, 2, 2, 'overlap',
cv2.cvtColor(rcnn_overlap, cv2.COLOR_BGR2RGB))
plt.show(block=False)
plt.show()
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
import time
import cv2
import json
import glob
import numpy as np
import matplotlib.pyplot as plt
import random
import keras
import tensorflow as tf
from tensorflow.python.keras import backend as K
from model_wrapper.utils import sub_plot,Tick,voc
from mask_rcnn.warpper import predict,plot
cap = cv2.VideoCapture('data/demo_video.mp4')
while(cap.isOpened()):
ret, frame_bgr = cap.read()
if not ret:
break
with Tick('interference'):
detections = predict(frame_bgr)
overlap = plot(detections,frame_bgr)
cv2.imshow('overlap',overlap)
if 27 == cv2.waitKey(1):
break