From 8c86f74ca01ec4c46997915e878b2a6ac518ff6d Mon Sep 17 00:00:00 2001
From: Kamran Melikov <melikovk@gmail.com>
Date: Tue, 21 Jan 2020 07:45:34 -0500
Subject: [PATCH] Non color images (#1976)
* First Draft
On branch non-color-images
Changes to be committed:
modified: mmdet/datasets/pipelines/loading.py
* Add option to load non color images
Add 'color_type' parameter to LoadImageFromFile class
Change __repr__ method accordingly
Since non-color images maybe two dimensional expand image
dimensions if necessary in DefaultFormatBundle and
ImageToTensor classes
Changes to be committed:
modified: mmdet/datasets/pipelines/formating.py
modified: mmdet/datasets/pipelines/loading.py
* Fix RandomCrop to work with grayscale images
Changes to be committed:
modified: mmdet/datasets/pipelines/transforms.py
* Modify retrieving w, h of padded image in anchor heads
This addreses problems with single channel images for which the
shape is tuple with 2 values
Changes to be committed:
modified: mmdet/models/anchor_heads/anchor_head.py
modified: mmdet/models/anchor_heads/guided_anchor_head.py
modified: mmdet/models/anchor_heads/reppoints_head.py
---
mmdet/datasets/pipelines/formating.py | 10 ++++++++--
mmdet/datasets/pipelines/loading.py | 9 +++++----
mmdet/datasets/pipelines/transforms.py | 2 +-
mmdet/models/anchor_heads/anchor_head.py | 2 +-
mmdet/models/anchor_heads/guided_anchor_head.py | 2 +-
mmdet/models/anchor_heads/reppoints_head.py | 2 +-
6 files changed, 17 insertions(+), 10 deletions(-)
diff --git a/mmdet/datasets/pipelines/formating.py b/mmdet/datasets/pipelines/formating.py
index 83385ab..e14dd0a 100644
--- a/mmdet/datasets/pipelines/formating.py
+++ b/mmdet/datasets/pipelines/formating.py
@@ -52,7 +52,10 @@ class ImageToTensor(object):
def __call__(self, results):
for key in self.keys:
- results[key] = to_tensor(results[key].transpose(2, 0, 1))
+ img = results[key]
+ if len(img.shape) < 3:
+ img = np.expand_dims(img, -1)
+ results[key] = to_tensor(img.transpose(2, 0, 1))
return results
def __repr__(self):
@@ -115,7 +118,10 @@ class DefaultFormatBundle(object):
def __call__(self, results):
if 'img' in results:
- img = np.ascontiguousarray(results['img'].transpose(2, 0, 1))
+ img = results['img']
+ if len(img.shape) < 3:
+ img = np.expand_dims(img, -1)
+ img = np.ascontiguousarray(img.transpose(2, 0, 1))
results['img'] = DC(to_tensor(img), stack=True)
for key in ['proposals', 'gt_bboxes', 'gt_bboxes_ignore', 'gt_labels']:
if key not in results:
diff --git a/mmdet/datasets/pipelines/loading.py b/mmdet/datasets/pipelines/loading.py
index f4aa6de..190773b 100644
--- a/mmdet/datasets/pipelines/loading.py
+++ b/mmdet/datasets/pipelines/loading.py
@@ -10,8 +10,9 @@ from ..registry import PIPELINES
@PIPELINES.register_module
class LoadImageFromFile(object):
- def __init__(self, to_float32=False):
+ def __init__(self, to_float32=False, color_type='color'):
self.to_float32 = to_float32
+ self.color_type = color_type
def __call__(self, results):
if results['img_prefix'] is not None:
@@ -19,7 +20,7 @@ class LoadImageFromFile(object):
results['img_info']['filename'])
else:
filename = results['img_info']['filename']
- img = mmcv.imread(filename)
+ img = mmcv.imread(filename, self.color_type)
if self.to_float32:
img = img.astype(np.float32)
results['filename'] = filename
@@ -29,8 +30,8 @@ class LoadImageFromFile(object):
return results
def __repr__(self):
- return self.__class__.__name__ + '(to_float32={})'.format(
- self.to_float32)
+ return '{} (to_float32={}, color_type={})'.format(
+ self.__class__.__name__, self.to_float32, self.color_type)
@PIPELINES.register_module
diff --git a/mmdet/datasets/pipelines/transforms.py b/mmdet/datasets/pipelines/transforms.py
index a9516d5..8a5c728 100644
--- a/mmdet/datasets/pipelines/transforms.py
+++ b/mmdet/datasets/pipelines/transforms.py
@@ -364,7 +364,7 @@ class RandomCrop(object):
crop_x1, crop_x2 = offset_w, offset_w + self.crop_size[1]
# crop the image
- img = img[crop_y1:crop_y2, crop_x1:crop_x2, :]
+ img = img[crop_y1:crop_y2, crop_x1:crop_x2, ...]
img_shape = img.shape
results['img'] = img
results['img_shape'] = img_shape
diff --git a/mmdet/models/anchor_heads/anchor_head.py b/mmdet/models/anchor_heads/anchor_head.py
index 83944c8..0fdc0aa 100644
--- a/mmdet/models/anchor_heads/anchor_head.py
+++ b/mmdet/models/anchor_heads/anchor_head.py
@@ -127,7 +127,7 @@ class AnchorHead(nn.Module):
for i in range(num_levels):
anchor_stride = self.anchor_strides[i]
feat_h, feat_w = featmap_sizes[i]
- h, w, _ = img_meta['pad_shape']
+ h, w = img_meta['pad_shape'][:2]
valid_feat_h = min(int(np.ceil(h / anchor_stride)), feat_h)
valid_feat_w = min(int(np.ceil(w / anchor_stride)), feat_w)
flags = self.anchor_generators[i].valid_flags(
diff --git a/mmdet/models/anchor_heads/guided_anchor_head.py b/mmdet/models/anchor_heads/guided_anchor_head.py
index 271a0dc..9fdf4f6 100644
--- a/mmdet/models/anchor_heads/guided_anchor_head.py
+++ b/mmdet/models/anchor_heads/guided_anchor_head.py
@@ -246,7 +246,7 @@ class GuidedAnchorHead(AnchorHead):
approxs = multi_level_approxs[i]
anchor_stride = self.anchor_strides[i]
feat_h, feat_w = featmap_sizes[i]
- h, w, _ = img_meta['pad_shape']
+ h, w = img_meta['pad_shape'][:2]
valid_feat_h = min(int(np.ceil(h / anchor_stride)), feat_h)
valid_feat_w = min(int(np.ceil(w / anchor_stride)), feat_w)
flags = self.approx_generators[i].valid_flags(
diff --git a/mmdet/models/anchor_heads/reppoints_head.py b/mmdet/models/anchor_heads/reppoints_head.py
index 1ce7abd..b3214f3 100644
--- a/mmdet/models/anchor_heads/reppoints_head.py
+++ b/mmdet/models/anchor_heads/reppoints_head.py
@@ -320,7 +320,7 @@ class RepPointsHead(nn.Module):
for i in range(num_levels):
point_stride = self.point_strides[i]
feat_h, feat_w = featmap_sizes[i]
- h, w, _ = img_meta['pad_shape']
+ h, w = img_meta['pad_shape'][:2]
valid_feat_h = min(int(np.ceil(h / point_stride)), feat_h)
valid_feat_w = min(int(np.ceil(w / point_stride)), feat_w)
flags = self.point_generators[i].valid_flags(
--
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