f4334277
 Hu Chunming
提交3rdparty
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import numpy as np
import sys
import os
import fnmatch
import argparse
try:
import cv2 as cv
except ImportError:
raise ImportError('Can\'t find OpenCV Python module. If you\'ve built it from sources without installation, '
'configure environment variable PYTHONPATH to "opencv_build_dir/lib" directory (with "python3" subdirectory if required)')
try:
import torch
except ImportError:
raise ImportError('Can\'t find pytorch. Please install it by following instructions on the official site')
from torch.utils.serialization import load_lua
from pascal_semsegm_test_fcn import eval_segm_result, get_conf_mat, get_metrics, DatasetImageFetch, SemSegmEvaluation
from imagenet_cls_test_alexnet import Framework, DnnCaffeModel
class NormalizePreproc:
def __init__(self):
pass
@staticmethod
def process(img):
image_data = np.array(img).transpose(2, 0, 1).astype(np.float32)
image_data = np.expand_dims(image_data, 0)
image_data /= 255.0
return image_data
class CityscapesDataFetch(DatasetImageFetch):
img_dir = ''
segm_dir = ''
segm_files = []
colors = []
i = 0
def __init__(self, img_dir, segm_dir, preproc):
self.img_dir = img_dir
self.segm_dir = segm_dir
self.segm_files = sorted([img for img in self.locate('*_color.png', segm_dir)])
self.colors = self.get_colors()
self.data_prepoc = preproc
self.i = 0
@staticmethod
def get_colors():
result = []
colors_list = (
(0, 0, 0), (128, 64, 128), (244, 35, 232), (70, 70, 70), (102, 102, 156), (190, 153, 153), (153, 153, 153),
(250, 170, 30), (220, 220, 0), (107, 142, 35), (152, 251, 152), (70, 130, 180), (220, 20, 60), (255, 0, 0),
(0, 0, 142), (0, 0, 70), (0, 60, 100), (0, 80, 100), (0, 0, 230), (119, 11, 32))
for c in colors_list:
result.append(DatasetImageFetch.pix_to_c(c))
return result
def __iter__(self):
return self
def next(self):
if self.i < len(self.segm_files):
segm_file = self.segm_files[self.i]
segm = cv.imread(segm_file, cv.IMREAD_COLOR)[:, :, ::-1]
segm = cv.resize(segm, (1024, 512), interpolation=cv.INTER_NEAREST)
img_file = self.rreplace(self.img_dir + segm_file[len(self.segm_dir):], 'gtFine_color', 'leftImg8bit')
assert os.path.exists(img_file)
img = cv.imread(img_file, cv.IMREAD_COLOR)[:, :, ::-1]
img = cv.resize(img, (1024, 512))
self.i += 1
gt = self.color_to_gt(segm, self.colors)
img = self.data_prepoc.process(img)
return img, gt
else:
self.i = 0
raise StopIteration
def get_num_classes(self):
return len(self.colors)
@staticmethod
def locate(pattern, root_path):
for path, dirs, files in os.walk(os.path.abspath(root_path)):
for filename in fnmatch.filter(files, pattern):
yield os.path.join(path, filename)
@staticmethod
def rreplace(s, old, new, occurrence=1):
li = s.rsplit(old, occurrence)
return new.join(li)
class TorchModel(Framework):
net = object
def __init__(self, model_file):
self.net = load_lua(model_file)
def get_name(self):
return 'Torch'
def get_output(self, input_blob):
tensor = torch.FloatTensor(input_blob)
out = self.net.forward(tensor).numpy()
return out
class DnnTorchModel(DnnCaffeModel):
net = cv.dnn.Net()
def __init__(self, model_file):
self.net = cv.dnn.readNetFromTorch(model_file)
def get_output(self, input_blob):
self.net.setBlob("", input_blob)
self.net.forward()
return self.net.getBlob(self.net.getLayerNames()[-1])
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--imgs_dir", help="path to Cityscapes validation images dir, imgsfine/leftImg8bit/val")
parser.add_argument("--segm_dir", help="path to Cityscapes dir with segmentation, gtfine/gtFine/val")
parser.add_argument("--model", help="path to torch model, download it here: "
"https://www.dropbox.com/sh/dywzk3gyb12hpe5/AAD5YkUa8XgMpHs2gCRgmCVCa")
parser.add_argument("--log", help="path to logging file")
args = parser.parse_args()
prep = NormalizePreproc()
df = CityscapesDataFetch(args.imgs_dir, args.segm_dir, prep)
fw = [TorchModel(args.model),
DnnTorchModel(args.model)]
segm_eval = SemSegmEvaluation(args.log)
segm_eval.process(fw, df)
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