def main():
dir_train = "./2019Proj1_train/"
dir_labeled_train = "./labeled_train/"
if not os.path.exists(dir_labeled_train):
os.makedirs(dir_labeled_train)
filelist = os.listdir(dir_train)
num_file = len(filelist)
startIdx = 0
endIdx = num_file - 1
if len(sys.argv) > 1 and sys.argv[1].isdigit():
startIdx = int(sys.argv[1])
if len(sys.argv) > 2 and sys.argv[2].isdigit():
endIdx = int(sys.argv[2])
print(endIdx)
for i in range(max(0, startIdx), min(num_file - 1, endIdx) + 1):
filename = filelist[i]
name, extension = os.path.splitext(filename)
image = Image.open(dir_train + filename)
plt.title("Labeling: " + str(i) + ". " + name)
plt.imshow(image)
roi = RoiPoly(color='r')
# show result
plt.title("Result: " + str(i) + ". " + name)
plt.imshow(image)
roi.display_roi()
plt.show()
# get mask
imgSize = np.shape(image)
mask = roi.get_mask(np.zeros(imgSize[0:2], dtype=bool))
# show mask
# plt.title("Mask: " + str(i) + ". " + name)
# plt.imshow(mask)
# plt.show()
np.save(os.path.join(dir_labeled_train, name + '.npy'), mask)
print("Finish labeling: " + dir_labeled_train + name + '.npy')
plt.close()
# # Let user draw third ROI
# roi3 = RoiPoly(color='r', fig=fig)
#
# # Show the image
# fig = plt.figure()
# plt.imshow(I1, interpolation='nearest')
# plt.title('Image %i, draw fourth ROI' %i)
# plt.show(block=False)
# # Let user draw fourth ROI
# roi4 = RoiPoly(color='b', fig=fig)
#
# # Show the image with both ROIs
# plt.imshow(I1, interpolation='nearest')
# [x.display_roi() for x in [roi1, roi2, roi3, roi4]]
roi1.display_roi()
plt.title('All ROIs')
plt.show()
mask1 = roi1.get_mask(gray)
# mask2 = roi2.get_mask(gray)
# mask3 = roi3.get_mask(gray)
# mask4 = roi4.get_mask(gray)
# Show ROI masks
plt.imshow(mask1, interpolation='nearest', cmap='Greys')
plt.title('ROI mask')
plt.show()
# plt.imshow(mask1 + mask2 + mask3 + mask4, \
# interpolation='nearest', cmap="Greys")
# plt.title('ROI masks of the ROIs')
if __name__ == '__main__':
results = multiprocessing.Queue()
# configure below for your experiment
detectorName = 'MPCCD-8N0-3-002-6'
integrateOver = 10
t0offset = -10
fig = plt.figure()
tenFrames, curTag = returnTenFrames(detectorName,
integrateOver=integrateOver)
plt.imshow(tenFrames)
my_roi = RoiPoly(color='r', fig=fig)
plt.imshow(tenFrames)
my_roi.display_roi()
mask = my_roi.get_mask(tenFrames)
#binning parameters
roiBins = 40
startBin = -2.0
endbin = 3.0
binCounterBin = np.zeros(roiBins)
binTheData = binROI(results, detectorName, curTag, startBin, endbin, mask,
roiBins, t0offset)
binTheData.start()
binROIs = np.zeros(roiBins)
bin_nomDel = np.zeros(roiBins)
i0det = 0
beamStatus = 0
# Show the image
fig = plt.figure()
plt.imshow(img, interpolation='nearest', cmap="Greys")
plt.colorbar()
plt.title("left click: line segment right click or double click: close region")
plt.show(block=False)
# Let user draw first ROI
roi1 = RoiPoly(color='r', fig=fig)
# Show the image with the first ROI
fig = plt.figure()
plt.imshow(img, interpolation='nearest', cmap="Greys")
plt.colorbar()
roi1.display_roi()
plt.title('draw second ROI')
plt.show(block=False)
# Let user draw second ROI
roi2 = RoiPoly(color='b', fig=fig)
# Show the image with both ROIs and their mean values
plt.imshow(img, interpolation='nearest', cmap="Greys")
plt.colorbar()
for roi in [roi1, roi2]:
roi.display_roi()
roi.display_mean(img)
plt.title('The two ROIs')
plt.show()
def main():
# logging.basicConfig(format='%(levelname)s ''%(processName)-10s : %(asctime)s ','%(module)s.%(funcName)s:%(lineno)s %(message)s',level=logging.INFO)
# load images
data_path = '/home/pratique/drone-course/Window_detection/GMM/daylight_100'
reply = 'k'
i = 0
for filename in os.listdir(data_path):
# print filename
if i%10 != 0:
i+=1
continue
img = cv2.imread(os.path.join(data_path,filename))
# convert = tune_RGB(img)
#convert = tune_HSV(img)
convert = adjust_gamma(img, gamma = 1.5)
# convert = cv2.fastNlMeansDenoisingColored(convert, None, 3, 3, 7, 15)
convert = cv2.medianBlur(convert,5)
img = cv2.cvtColor(convert,cv2.COLOR_BGR2RGB)
# Show the image
# Show the image
# img = img[:,:,[2,1,0]]
fig = plt.figure()
plt.imshow(img, interpolation='nearest', cmap="Greys")
plt.colorbar()
plt.title("left click: line segment right click or double click: close region")
plt.show(block=False)
# Let user draw first ROI
roi1 = RoiPoly(color='r', fig=fig)
# Show the image with the first ROI
fig = plt.figure()
plt.imshow(img, interpolation='nearest', cmap="Greys")
plt.colorbar()
roi1.display_roi()
plt.title('draw second ROI')
plt.show(block=False)
# Let user draw second ROI
roi2 = RoiPoly(color='b', fig=fig)
# Show the image with both ROIs and their mean values
plt.imshow(img, interpolation='nearest', cmap="Greys")
plt.colorbar()
for roi in [roi1, roi2]:
roi.display_roi()
roi.display_mean(img)
plt.title('The two ROIs')
plt.show()
# plt.imshow(np.bitwise_not(roi2.get_mask(img) ^ roi1.get_mask(img)),
# interpolation='nearest', cmap="Greys")
# plt.title('ROI masks of the two ROIs')
# plt.show()
#store data
mask = roi2.get_mask(img) ^ roi1.get_mask(img)
mask.astype(int)
# print(np.uint8(mask))
window_mask = np.uint8(mask*255)
extracted_frame = cv2.bitwise_and(img, img, mask = window_mask)
extracted_frame = cv2.cvtColor(extracted_frame,cv2.COLOR_RGB2BGR)
# input('aa')
cv2.imwrite("./data/window_100"+str(i)+".jpg", extracted_frame)
i = i+1
if 0xFF == ord('q'):
break
l_mouse_mask = np.load(l_mask_file)
if l_mouse_mask.shape != (256, 256):
l_green_frame_file = direc.file(exp_folder=EXP, fname="left green")
l_green_frame = np.load(l_green_frame_file)
plt.figure()
plt.title('Left Mouse Left Hemisphere')
plt.imshow(l_green_frame, cmap='gray', vmin=0, vmax=150)
LM_left_hem = RoiPoly(color='b')
plt.title('Left Mouse Right Hemisphere')
plt.imshow(l_green_frame, cmap='gray', vmin=0, vmax=150)
LM_right_hem = RoiPoly(color='r')
plt.title('Left Mouse Hemispheres')
plt.imshow(l_green_frame, cmap='gray', vmin=0, vmax=150)
LM_left_hem.display_roi()
LM_right_hem.display_roi()
plt.xticks([])
plt.yticks([])
l_mouse_mask = np.logical_or(
LM_left_hem.get_mask(l_mouse_frames[2000]),
LM_right_hem.get_mask(l_mouse_frames[2000]))
above_threshold = np.where(roi_gradient_signal >= threshold)[0]
interaction_time = np.arange(start_interaction, end_interaction)
solo_time = np.setdiff1d(
np.arange(roi_gradient_signal.size),
np.arange(start_first_translation, end_second_translation))
solo_whisk_index = np.intersect1d(above_threshold, solo_time)
social_whisk_index = np.intersect1d(above_threshold, interaction_time)
import numpy as np # noqa: E402
import pandas as pd # noqa: E402
from pandas import DataFrame, Series # noqa: E402
import pims # noqa: E402
import trackpy as tp # noqa: E402
from roipoly import RoiPoly # noqa: E402
frames = pims.ImageSequence("../test/wt_pos1/mag1/*.tif", as_grey=True)
# print(frames[0]) # first frame
plt.imshow(frames[0]) # show the image
immobile_bead = RoiPoly(
color='b') # draw the ROI on shown image, shows fig by default
plt.imshow(frames[0]) # show image first
immobile_bead.display_roi() # overlay ROI on shown image
plt.show() # show the plot
mask = immobile_bead.get_mask(frames[0]) # call mask attribute from ROI object
print(mask)
plt.imshow(mask)
plt.show()
frame_zero = frames[0]
frame_zero[~mask] = 0
plt.imshow(frame_zero)
plt.show()
