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Add image crop transform #78

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9 changes: 8 additions & 1 deletion match_two.py
Original file line number Diff line number Diff line change
Expand Up @@ -95,7 +95,10 @@ def match_two(model, device, config, im_one, im_two, plot_save_path):

model.eval()

it = input_transform((int(config['feature_extract']['imageresizeH']), int(config['feature_extract']['imageresizeW'])))
crop_roi = None
if 'imagecrop' in config['feature_extract']:
crop_roi = tuple([int(x) for x in config['feature_extract']['imagecrop'].split(",")])
it = input_transform((int(config['feature_extract']['imageresizeH']), int(config['feature_extract']['imageresizeW'])), crop_roi)

im_one_pil = Image.fromarray(cv2.cvtColor(im_one, cv2.COLOR_BGR2RGB))
im_two_pil = Image.fromarray(cv2.cvtColor(im_two, cv2.COLOR_BGR2RGB))
Expand Down Expand Up @@ -148,6 +151,10 @@ def match_two(model, device, config, im_one, im_two, plot_save_path):
tqdm.write('====> Plotting Local Features and save them to ' + str(join(plot_save_path, 'patchMatchings.png')))

# using cv2 for their in-built keypoint correspondence plotting tools
if crop_roi is not None:
top, left, bottom, right = crop_roi
im_one = im_one[top:bottom,left:right]
im_two = im_two[top:bottom,left:right]
cv_im_one = cv2.resize(im_one, (int(config['feature_extract']['imageresizeW']), int(config['feature_extract']['imageresizeH'])))
cv_im_two = cv2.resize(im_two, (int(config['feature_extract']['imageresizeW']), int(config['feature_extract']['imageresizeH'])))
# cv2 resize slightly different from torch, but for visualisation only not a big problem
Expand Down
31 changes: 17 additions & 14 deletions patchnetvlad/tools/datasets.py
Original file line number Diff line number Diff line change
Expand Up @@ -27,6 +27,7 @@
import os

import torchvision.transforms as transforms
import torchvision.transforms.functional as TF
import torch.utils.data as data

import numpy as np
Expand All @@ -36,20 +37,19 @@
from patchnetvlad.tools import PATCHNETVLAD_ROOT_DIR


def input_transform(resize=(480, 640)):
def input_transform(resize=(480, 640), crop_roi=None):
trans = []
if crop_roi is not None and len(crop_roi) == 4 and all([x >= 0 for x in crop_roi]):
top, left, bottom, right = crop_roi
trans.append(transforms.Lambda(lambda x: TF.crop(x, top, left, bottom-top, right-left)))
if resize[0] > 0 and resize[1] > 0:
return transforms.Compose([
transforms.Resize(resize),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
else:
return transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
trans.append(transforms.Resize(resize))
trans.extend([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
return transforms.Compose(trans)


class PlaceDataset(data.Dataset):
Expand Down Expand Up @@ -80,8 +80,11 @@ def __init__(self, query_file_path, index_file_path, dataset_root_dir, ground_tr
self.positives = None
self.distances = None

crop_roi = None
if 'imagecrop' in config:
crop_roi = tuple([int(x) for x in config['imagecrop'].split(',')])
self.resize = (int(config['imageresizeH']), int(config['imageresizeW']))
self.mytransform = input_transform(self.resize)
self.mytransform = input_transform(self.resize, crop_roi)

def __getitem__(self, index):
img = Image.open(self.images[index])
Expand Down