mirror of
https://github.com/opencv/opencv.git
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256 lines
9.3 KiB
Python
256 lines
9.3 KiB
Python
from __future__ import print_function
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from abc import ABCMeta, abstractmethod
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import numpy as np
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import sys
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import argparse
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import time
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from imagenet_cls_test_alexnet import CaffeModel, DNNOnnxModel
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try:
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import cv2 as cv
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except ImportError:
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raise ImportError('Can\'t find OpenCV Python module. If you\'ve built it from sources without installation, '
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'configure environment variable PYTHONPATH to "opencv_build_dir/lib" directory (with "python3" subdirectory if required)')
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def get_metrics(conf_mat):
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pix_accuracy = np.trace(conf_mat) / np.sum(conf_mat)
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t = np.sum(conf_mat, 1)
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num_cl = np.count_nonzero(t)
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assert num_cl
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mean_accuracy = np.sum(np.nan_to_num(np.divide(np.diagonal(conf_mat), t))) / num_cl
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col_sum = np.sum(conf_mat, 0)
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mean_iou = np.sum(
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np.nan_to_num(np.divide(np.diagonal(conf_mat), (t + col_sum - np.diagonal(conf_mat))))) / num_cl
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return pix_accuracy, mean_accuracy, mean_iou
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def eval_segm_result(net_out):
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assert type(net_out) is np.ndarray
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assert len(net_out.shape) == 4
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channels_dim = 1
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y_dim = channels_dim + 1
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x_dim = y_dim + 1
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res = np.zeros(net_out.shape).astype(int)
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for i in range(net_out.shape[y_dim]):
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for j in range(net_out.shape[x_dim]):
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max_ch = np.argmax(net_out[..., i, j])
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res[0, max_ch, i, j] = 1
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return res
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def get_conf_mat(gt, prob):
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assert type(gt) is np.ndarray
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assert type(prob) is np.ndarray
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conf_mat = np.zeros((gt.shape[0], gt.shape[0]))
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for ch_gt in range(conf_mat.shape[0]):
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gt_channel = gt[ch_gt, ...]
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for ch_pr in range(conf_mat.shape[1]):
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prob_channel = prob[ch_pr, ...]
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conf_mat[ch_gt][ch_pr] = np.count_nonzero(np.multiply(gt_channel, prob_channel))
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return conf_mat
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class MeanChannelsPreproc:
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def __init__(self):
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pass
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@staticmethod
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def process(img, framework):
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image_data = None
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if framework == "Caffe":
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image_data = cv.dnn.blobFromImage(img, scalefactor=1.0, mean=(123.0, 117.0, 104.0), swapRB=True)
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elif framework == "DNN (ONNX)":
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image_data = cv.dnn.blobFromImage(img, scalefactor=0.019, mean=(123.675, 116.28, 103.53), swapRB=True)
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else:
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raise ValueError("Unknown framework")
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return image_data
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class DatasetImageFetch(object):
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__metaclass__ = ABCMeta
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data_prepoc = object
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@abstractmethod
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def __iter__(self):
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pass
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@abstractmethod
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def next(self):
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pass
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@staticmethod
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def pix_to_c(pix):
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return pix[0] * 256 * 256 + pix[1] * 256 + pix[2]
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@staticmethod
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def color_to_gt(color_img, colors):
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num_classes = len(colors)
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gt = np.zeros((num_classes, color_img.shape[0], color_img.shape[1])).astype(int)
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for img_y in range(color_img.shape[0]):
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for img_x in range(color_img.shape[1]):
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c = DatasetImageFetch.pix_to_c(color_img[img_y][img_x])
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if c in colors:
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cls = colors.index(c)
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gt[cls][img_y][img_x] = 1
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return gt
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class PASCALDataFetch(DatasetImageFetch):
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img_dir = ''
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segm_dir = ''
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names = []
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colors = []
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i = 0
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def __init__(self, img_dir, segm_dir, names_file, segm_cls_colors, preproc):
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self.img_dir = img_dir
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self.segm_dir = segm_dir
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self.colors = self.read_colors(segm_cls_colors)
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self.data_prepoc = preproc
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self.i = 0
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with open(names_file) as f:
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for l in f.readlines():
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self.names.append(l.rstrip())
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@staticmethod
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def read_colors(colors):
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result = []
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for color in colors:
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result.append(DatasetImageFetch.pix_to_c(color))
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return result
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def __iter__(self):
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return self
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def __next__(self):
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if self.i < len(self.names):
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name = self.names[self.i]
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self.i += 1
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segm_file = self.segm_dir + name + ".png"
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img_file = self.img_dir + name + ".jpg"
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gt = self.color_to_gt(cv.imread(segm_file, cv.IMREAD_COLOR)[:, :, ::-1], self.colors)
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img = cv.imread(img_file, cv.IMREAD_COLOR)
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img_caffe = self.data_prepoc.process(img[:, :, ::-1], "Caffe")
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img_dnn = self.data_prepoc.process(img[:, :, ::-1], "DNN (ONNX)")
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img_dict = {
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"Caffe": img_caffe,
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"DNN (ONNX)": img_dnn
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}
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return img_dict, gt
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else:
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self.i = 0
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raise StopIteration
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def get_num_classes(self):
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return len(self.colors)
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class SemSegmEvaluation:
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log = sys.stdout
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def __init__(self, log_path,):
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self.log = open(log_path, 'w')
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def process(self, frameworks, data_fetcher):
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samples_handled = 0
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conf_mats = [np.zeros((data_fetcher.get_num_classes(), data_fetcher.get_num_classes())) for i in range(len(frameworks))]
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blobs_l1_diff = [0] * len(frameworks)
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blobs_l1_diff_count = [0] * len(frameworks)
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blobs_l_inf_diff = [sys.float_info.min] * len(frameworks)
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inference_time = [0.0] * len(frameworks)
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for in_blob_dict, gt in data_fetcher:
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frameworks_out = []
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samples_handled += 1
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for i in range(len(frameworks)):
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start = time.time()
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framework_name = frameworks[i].get_name()
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out = frameworks[i].get_output(in_blob_dict[framework_name])
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end = time.time()
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segm = eval_segm_result(out)
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conf_mats[i] += get_conf_mat(gt, segm[0])
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frameworks_out.append(out)
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inference_time[i] += end - start
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pix_acc, mean_acc, miou = get_metrics(conf_mats[i])
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name = frameworks[i].get_name()
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print(samples_handled, 'Pixel accuracy, %s:' % name, 100 * pix_acc, file=self.log)
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print(samples_handled, 'Mean accuracy, %s:' % name, 100 * mean_acc, file=self.log)
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print(samples_handled, 'Mean IOU, %s:' % name, 100 * miou, file=self.log)
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print("Inference time, ms ", \
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frameworks[i].get_name(), inference_time[i] / samples_handled * 1000, file=self.log)
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for i in range(1, len(frameworks)):
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log_str = frameworks[0].get_name() + " vs " + frameworks[i].get_name() + ':'
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diff = np.abs(frameworks_out[0] - frameworks_out[i])
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l1_diff = np.sum(diff) / diff.size
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print(samples_handled, "L1 difference", log_str, l1_diff, file=self.log)
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blobs_l1_diff[i] += l1_diff
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blobs_l1_diff_count[i] += 1
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if np.max(diff) > blobs_l_inf_diff[i]:
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blobs_l_inf_diff[i] = np.max(diff)
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print(samples_handled, "L_INF difference", log_str, blobs_l_inf_diff[i], file=self.log)
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self.log.flush()
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for i in range(1, len(blobs_l1_diff)):
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log_str = frameworks[0].get_name() + " vs " + frameworks[i].get_name() + ':'
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print('Final l1 diff', log_str, blobs_l1_diff[i] / blobs_l1_diff_count[i], file=self.log)
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# PASCAL VOC 2012 classes colors
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colors_pascal_voc_2012 = [
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[0, 0, 0],
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[128, 0, 0],
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[0, 128, 0],
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[128, 128, 0],
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[0, 0, 128],
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[128, 0, 128],
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[0, 128, 128],
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[128, 128, 128],
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[64, 0, 0],
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[192, 0, 0],
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[64, 128, 0],
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[192, 128, 0],
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[64, 0, 128],
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[192, 0, 128],
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[64, 128, 128],
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[192, 128, 128],
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[0, 64, 0],
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[128, 64, 0],
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[0, 192, 0],
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[128, 192, 0],
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[0, 64, 128],
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]
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--imgs_dir", help="path to PASCAL VOC 2012 images dir, data/VOC2012/JPEGImages")
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parser.add_argument("--segm_dir", help="path to PASCAL VOC 2012 segmentation dir, data/VOC2012/SegmentationClass/")
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parser.add_argument("--val_names", help="path to file with validation set image names, download it here: "
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"https://github.com/shelhamer/fcn.berkeleyvision.org/blob/master/data/pascal/seg11valid.txt")
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parser.add_argument("--prototxt", help="path to caffe prototxt, download it here: "
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"https://github.com/opencv/opencv/blob/4.x/samples/data/dnn/fcn8s-heavy-pascal.prototxt")
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parser.add_argument("--caffemodel", help="path to caffemodel file, download it here: "
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"http://dl.caffe.berkeleyvision.org/fcn8s-heavy-pascal.caffemodel")
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parser.add_argument("--onnxmodel", help="path to onnx model file, download it here: "
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"https://github.com/onnx/models/raw/491ce05590abb7551d7fae43c067c060eeb575a6/validated/vision/object_detection_segmentation/fcn/model/fcn-resnet50-12.onnx")
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parser.add_argument("--log", help="path to logging file", default='log.txt')
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parser.add_argument("--in_blob", help="name for input blob", default='data')
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parser.add_argument("--out_blob", help="name for output blob", default='score')
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args = parser.parse_args()
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prep = MeanChannelsPreproc()
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df = PASCALDataFetch(args.imgs_dir, args.segm_dir, args.val_names, colors_pascal_voc_2012, prep)
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fw = [CaffeModel(args.prototxt, args.caffemodel, args.in_blob, args.out_blob, True),
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DNNOnnxModel(args.onnxmodel, args.in_blob, args.out_blob)]
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segm_eval = SemSegmEvaluation(args.log)
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segm_eval.process(fw, df)
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