def resize_img_and_roi(img_fpath, roi_, new_size=None, sqrt_area=400.0):
printDBG('[segm] imread(%r) ' % img_fpath)
full_img = io.imread(img_fpath)
(full_h, full_w) = full_img.shape[:2] # Image Shape
printDBG('[segm] full_img.shape=%r' % (full_img.shape,))
(rw_, rh_) = roi_[2:]
# Ensure that we know the new chip size
if new_size is None:
target_area = float(sqrt_area) ** 2
def _resz(w, h):
ht = np.sqrt(target_area * h / w)
wt = w * ht / h
return (int(round(wt)), int(round(ht)))
new_size_ = _resz(rw_, rh_)
else:
new_size_ = new_size
# Get Scale Factors
fx = new_size_[0] / rw_
fy = new_size_[1] / rh_
printDBG('[segm] fx=%r fy=%r' % (fx, fy))
dsize = (int(round(fx * full_w)), int(round(fy * full_h)))
printDBG('[segm] dsize=%r' % (dsize,))
# Resize the image
img_resz = cv2.resize(full_img, dsize, interpolation=cv2.INTER_LANCZOS4)
# Get new ROI in resized image
roi_resz = np.array(np.round(roi_ * fx), dtype=np.int64)
return img_resz, roi_resz
def resize_img_and_roi(img_fpath, roi_, new_size=None, sqrt_area=400.0):
printDBG('[segm] imread(%r) ' % img_fpath)
full_img = io.imread(img_fpath)
(full_h, full_w) = full_img.shape[:2] # Image Shape
printDBG('[segm] full_img.shape=%r' % (full_img.shape, ))
(rw_, rh_) = roi_[2:]
# Ensure that we know the new chip size
if new_size is None:
target_area = float(sqrt_area)**2
def _resz(w, h):
ht = np.sqrt(target_area * h / w)
wt = w * ht / h
return (int(round(wt)), int(round(ht)))
new_size_ = _resz(rw_, rh_)
else:
new_size_ = new_size
# Get Scale Factors
fx = new_size_[0] / rw_
fy = new_size_[1] / rh_
printDBG('[segm] fx=%r fy=%r' % (fx, fy))
dsize = (int(round(fx * full_w)), int(round(fy * full_h)))
printDBG('[segm] dsize=%r' % (dsize, ))
# Resize the image
img_resz = cv2.resize(full_img, dsize, interpolation=cv2.INTER_LANCZOS4)
# Get new ROI in resized image
roi_resz = np.array(np.round(roi_ * fx), dtype=np.int64)
return img_resz, roi_resz
def extract_chip(img_fpath, roi, theta, new_size):
'Crops chip from image ; Rotates and scales; Converts to grayscale'
# Read parent image
#printDBG('[cc2] reading image')
imgBGR = io.imread(img_fpath)
#printDBG('[cc2] building transform')
# Build transformation
(rx, ry, rw, rh) = roi
(rw_, rh_) = new_size
Aff = build_transform(rx, ry, rw, rh, rw_, rh_, theta)
#printDBG('[cc2] rotate and scale')
# Rotate and scale
imgBGR = cv2.warpAffine(imgBGR, Aff, (rw_, rh_), **cv2_warp_kwargs)
#printDBG('[cc2] return extracted')
return imgBGR
def extract_chip(img_fpath, roi, theta, new_size):
'Crops chip from image ; Rotates and scales; Converts to grayscale'
# Read parent image
#printDBG('[cc2] reading image')
imgBGR = io.imread(img_fpath)
#printDBG('[cc2] building transform')
# Build transformation
(rx, ry, rw, rh) = roi
(rw_, rh_) = new_size
Aff = build_transform(rx, ry, rw, rh, rw_, rh_, theta)
#printDBG('[cc2] rotate and scale')
# Rotate and scale
imgBGR = cv2.warpAffine(imgBGR, Aff, (rw_, rh_), **cv2_warp_kwargs)
#printDBG('[cc2] return extracted')
return imgBGR
def mask_creator_demo(mode=0):
print('*** START DEMO ***')
print('mode = %r' % mode)
try:
from hscom import fileio as io
img = io.imread('/lena.png', 'RGB')
except ImportError as ex:
print('cant read lena: %r' % ex)
img = np.random.uniform(255, 255, size=(100, 100))
ax = plt.subplot(111)
ax.imshow(img)
if mode == 0:
mc = MaskCreator(ax)
# Do interaction
plt.show()
# Make mask from selection
mask = mc.get_mask(img.shape)
# User must close previous figure
elif mode == 1:
from hsgui import guitools
from hsviz import draw_func2 as df2
ax.set_title('Click two points to select an ROI (old mode)')
# Do selection
roi = guitools.select_roi()
# Make mask from selection
mask = roi_to_mask(img.shape, roi)
# Close previous figure
df2.close_all_figures()
# Modify the image with the mask
masked_img = apply_mask(img, mask)
# show the modified image
plt.imshow(masked_img)
plt.title('Region outside of mask is darkened')
print('show2')
plt.show()
def show_splash(fnum=1, **kwargs):
#printDBG('[viz] show_splash()')
splash_fpath = io.splash_img_fpath()
img = io.imread(splash_fpath)
df2.imshow(img, fnum=fnum, **kwargs)
from PyQt4.Qt import (QAbstractItemModel, QModelIndex, QVariant, QWidget,
Qt, QObject, pyqtSlot, QKeyEvent)
# HotSpotter
from hotspotter import DataStructures as ds
from hotspotter import HotSpotterAPI
from hotspotter import HotSpotterAPI as api
from hotspotter import algos
from hotspotter import chip_compute2 as cc2
from hotspotter import feature_compute2 as fc2
from hotspotter import load_data2 as ld2
from hotspotter import match_chips3 as mc3
from hotspotter import matching_functions as mf
from hotspotter import segmentation
from hotspotter import voting_rules2 as vr2
from hotspotter import nn_filters
from hscom import Parallelize as parallel
from hscom import fileio as io
from hscom import helpers as helpers
from hsviz import draw_func2 as df2
from hsviz import extract_patch
from hsviz import viz
import dev
if __name__ == 'main':
multiprocessing.freeze_support()
#exec(open('dbgimport.py').read())
if '--img' in sys.argv:
img_fpath_ = helpers.dbg_get_imgfpath()
np_img_ = io.imread(img_fpath_)
img_ = Image.open(img_fpath_)
def show_splash(fnum=1, **kwargs):
#printDBG('[viz] show_splash()')
splash_fpath = io.splash_img_fpath()
img = io.imread(splash_fpath)
df2.imshow(img, fnum=fnum, **kwargs)
def gx2_image(hs, gx):
img_fpath = hs.gx2_gname(gx, full=True)
img = io.imread(img_fpath)
return img
def _read_chip(hs, fpath):
return io.imread(fpath)
def gx2_image(hs, gx):
img_fpath = hs.gx2_gname(gx, full=True)
img = io.imread(img_fpath)
return img
def _read_chip(hs, fpath):
return io.imread(fpath)