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Use the caffe-users list instead.
This helps developers maintain a clear, uncluttered, and efficient view of the state of Caffe.
Issue summary
Using the model nyud-fcn32s-hha-heavy.caffemodel to infer images in NYU Depth Dataset V2, the result is wrong. Inference script as blow.
import numpy as np
from PIL import Image
import os
import sys
current_path = os.path.dirname(__file__)
project_path = os.path.dirname(os.path.dirname(__file__))
sys.path.append(project_path)
import caffe
import vis
def zero_multi_padding(in_array, padding_size=0):
in_channels, h, w = in_array.shape
padding_array = np.zeros([in_channels, h + 2 * padding_size, w + 2 * padding_size],dtype=in_array.dtype)
for i in range(in_channels):
for xx in range(h):
for yy in range(w):
padding_array[i, xx + padding_size, yy + padding_size] = in_array[i, xx, yy]
return padding_array
# the demo image is "2007_000129" from PASCAL VOC
# load image, switch to BGR, subtract mean, and make dims C x H x W for Caffe
# im = Image.open(project_path+'/demo/image.jpg')
# in_ = np.array(im, dtype=np.float32)
# print(in_.shape)
# in_ = in_[:,:,::-1]
# in_ -= np.array((104.00698793,116.66876762,122.67891434))
# in_ = in_.transpose((2,0,1))
# in_pad = zero_multi_padding(in_, 99)
# print(in_pad.shape)
# print(in_.shape)
im = Image.open('/home/azure002/my_worksapce/fcn.berkeleyvision.org/nyu_images/169.jpg')
# im = Image.open('../demo/image.jpg')
new_image = im.resize((1024,1024), Image.Resampling.BICUBIC)
new_image.save(current_path+'/test_init.png')
in_ = np.array(im, dtype=np.float32)
in_ = in_[:,:,::-1]
in_ -= np.array((104.00698793,116.66876762,122.67891434))
in_ = in_.transpose((2,0,1))
# load net
net = caffe.Net(current_path+'/deploy.prototxt', current_path+'/nyud-fcn32s-hha-heavy.caffemodel', caffe.TEST)
# # shape for input (data blob is N x C x H x W), set data
# net.blobs['None'].reshape(1, *in_.shape)
# net.blobs['None'].data[...] = in_
# net.blobs['data'].reshape(1, *in_pad.shape)
# net.blobs['data'].data[...] = in_pad
net.blobs['data'].reshape(1, *in_.shape)
net.blobs['data'].data[...] = in_
# run net and take argmax for prediction
net.forward()
out = net.blobs['score'].data[0].argmax(axis=0)
import matplotlib.pyplot as plt
plt.imshow(out,cmap='gray')
plt.axis('off')
plt.savefig(current_path+'/test.png')
# visualize segmentation in PASCAL VOC colors
voc_palette = vis.make_palette(40)
print(voc_palette)
out_im = Image.fromarray(vis.color_seg(out, voc_palette))
out_im.save(current_path+'/output.png')
masked_im = Image.fromarray(vis.vis_seg(im, out, voc_palette))
masked_im.save(current_path+'/visualization.jpg')
IMAGE: PROCESS:
Steps to reproduce
run the python script
Tried solutions
System configuration
Operating system: Linux azure002-System-Product-Name 5.4.0-126-generic Sort out models, data, and tools #142-Ubuntu SMP Fri Aug 26 12:12:57 UTC 2022 x86_64 x86_64 x86_64 GNU/Linux
Compiler:
CUDA version (if applicable):
CUDNN version (if applicable):
BLAS:
Python version (if using pycaffe): Python 3.8.10
MATLAB version (if using matcaffe):
Issue checklist
read the guidelines and removed the first paragraph
written a short summary and detailed steps to reproduce
explained how solutions to related problems failed (tick if found none)
filled system configuration
attached relevant logs/config files (tick if not applicable)
The text was updated successfully, but these errors were encountered:
Important - read before submitting
Please read the guidelines for contributing before submitting this issue!
Please do not post installation, build, usage, or modeling questions, or other requests for help to Issues.
Use the caffe-users list instead.
This helps developers maintain a clear, uncluttered, and efficient view of the state of Caffe.
Issue summary
Using the model nyud-fcn32s-hha-heavy.caffemodel to infer images in NYU Depth Dataset V2, the result is wrong. Inference script as blow.
IMAGE: PROCESS:
![image](https://user-images.githubusercontent.com/76459010/196842855-8ef94eb1-c27a-4e30-ad71-8598d67a951d.png)
Steps to reproduce
run the python script
Tried solutions
System configuration
Issue checklist
The text was updated successfully, but these errors were encountered: