def callback(self, ros_data):
np_arr = np.frombuffer(ros_data.data, np.uint8)
image_np = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
# convert it to pil format
im = cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB)
im_pil = Image.fromarray(im)
# im_pil = im_pil.convert('RGB')
transform = input_transform
im_pil = transform(im_pil)
# print(type(img))
# Make prediction
im_pil = im_pil.cuda().unsqueeze(0)
output = model.evaluate(im_pil)
predict = torch.max(output, 1)[1].cpu().numpy() + 1
# Get color pallete for visualization
mask = encoding.utils.get_mask_pallete(predict, 'minc_dataset')
# print(type(mask))
# print(mask)
mask.save('deeplab_resnest101_rgb_202008042350.png')
# mask.show()
# time.sleep(5)
# mask.close()
img = np.asarray(mask)
# pil2ros = cv2.cvtColor(pil2ros, cv2.COLOR_RGB2BGR)
img3_shape = np.insert(img.shape,2,[3])
img3 = np.zeros((img3_shape)).astype("uint8")
img3[:,:,0] = img
img3[:,:,1] = img
img3[:,:,2] = img
for i in range(24):
img3 = np.where(img3==((i,i,i)),((mincpallete[i*3+2],mincpallete[i*3+1],mincpallete[i*3])),img3)
img3 = np.array(img3, dtype=np.uint8)
cv2.imshow("window",img3)
k = cv2.waitKey(0)
cv2.imwrite('deeplabresnest101_gray_202008042350.png',pil2ros)
# print(pil2ros.shape)
# show the mask
cv2.imshow("Image window", pil2ros)
cv2.waitKey(3)
msg = CompressedImage()
msg.header.stamp = rospy.Time.now()
msg.format = "jpeg"
msg.data = np.array(cv2.imencode('.jpg', pil2ros)[1]).tostring()
self.image_pub.publish(msg)
def load_image(filename, size=None, scale=None, keep_asp=True, transform=input_transform):
"""Load the image for demos"""
img = Image.open(filename).convert('RGB')
if size is not None:
if keep_asp:
size2 = int(size * 1.0 / img.size[0] * img.size[1])
img = img.resize((size, size2), Image.ANTIALIAS)
else:
img = img.resize((size, size), Image.ANTIALIAS)
elif scale is not None:
img = img.resize((int(img.size[0] / scale), int(img.size[1] / scale)), Image.ANTIALIAS)
if transform:
img = transform(img)
return img
def callback(self, ros_data):
np_arr = np.frombuffer(ros_data.data, np.uint8)
image_np = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
cv2.imwrite(SAVENAME + DATE + '_orig_' + '.png', image_np)
# convert it to pil format
im = cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB)
im_pil = Image.fromarray(im)
# im_pil = im_pil.convert('RGB')
transform = input_transform
im_pil = transform(im_pil)
# print(type(img))
# Make prediction
im_pil = im_pil.cuda().unsqueeze(0)
output = model.evaluate(im_pil)
predict = torch.max(output, 1)[1].cpu().numpy() + 1
# Get color pallete for visualization
mask = encoding.utils.get_mask_pallete(predict, 'minc_dataset')
# print(type(mask))
# print(mask)
mask.save(SAVENAME + DATE + '_rgb_' + '.png')
# mask.show()
# time.sleep(5)
# mask.close()
pil2ros = np.asarray(mask)
pil2ros = cv2.cvtColor(pil2ros, cv2.COLOR_RGB2BGR)
cv2.imwrite(SAVENAME + DATE + '_gray_' + '.png', pil2ros)
# print(pil2ros.shape)
# show the mask
cv2.imshow("Image window", pil2ros)
cv2.waitKey(3)
msg = CompressedImage()
msg.header.stamp = rospy.Time.now()
msg.format = "jpeg"
msg.data = np.array(cv2.imencode('.jpg', pil2ros)[1]).tostring()
self.image_pub.publish(msg)
def callback(self, ros_data):
np_arr = np.frombuffer(ros_data.data, np.uint8)
image_np = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
# convert it to pil format
im = cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB)
im_pil = Image.fromarray(im)
# im_pil = im_pil.convert('RGB')
transform = input_transform
im_pil = transform(im_pil)
# print(type(img))
# Make prediction
im_pil = im_pil.cuda().unsqueeze(0)
output = model.evaluate(im_pil)
predict = torch.max(output, 1)[1].cpu().numpy() + 1
# Get color pallete for visualization
mask = encoding.utils.get_mask_pallete(predict, 'ade20k')
print(type(mask))
print(mask)
# mask.save('output2.png')
# mask.show()
# time.sleep(5)
# mask.close()
pil2ros = np.asarray(mask)
pil2ros = cv2.cvtColor(pil2ros, cv2.COLOR_RGB2BGR)
# cv2.imwrite('cvformat.png',pil2ros)
# print(pil2ros.shape)
# show the mask
cv2.imshow("Image window", pil2ros)
cv2.waitKey(3)
'''