def postprocess_image(params):
# load the prob map from a previously generated numpy array
print "laoding probability map"
prob_map = np.load(pc_config.fname_segmentation_pmap_npy)
if params["training_type"] == "segmentation":
pass # here one could smooth the segmentation or set a treshold
if params["training_type"] == "objectdetection":
# here comes the peak finder as in ciresan mitosis 2013
p_thresh = 0.9 # 0.21
prob_map_peaks = np.zeros((prob_map.shape[0], prob_map.shape[1]))
coords_list = []
radius = params["object_size"]
while True:
coords_max = np.unravel_index(prob_map.argmax(), prob_map.shape)
cx = coords_max[0]
cy = coords_max[1]
print "peak at", cx, cy, prob_map[cx, cy]
if prob_map[cx, cy] < p_thresh:
break
coords_list.append([cx, cy])
# remove these pixels
prob_map = pc_utilities.circle_mask(
cx, cy, int(radius * 1.5), prob_map, 0
) # avoid double detection by a slightly larger circle
# draw new prob map with the coords
prob_map_peaks = pc_utilities.circle_mask(cx, cy, radius, prob_map_peaks, 1)
# test
# pc_utilities.plot_image(prob_map_peaks)
# save png and json
if params["training_type"] == "segmentation":
prob_map_int = (prob_map * 255.0).astype("uint8")
# save json prob map
print "saving json probability map to ", pc_config.fname_segmentation_pmap_json
json.dump(
prob_map.tolist(),
codecs.open(pc_config.fname_segmentation_pmap_json, "w", encoding="utf-8"),
separators=(",", ":"),
sort_keys=True,
indent=4,
)
# save png
print "saving png probability map to ", pc_config.fname_segmentation_pmap_imagepath + pc_dataset.fnames_images[
pc_config.eval_img_id
]
img = Image.fromarray(prob_map_int)
img.save(pc_config.fname_segmentation_pmap_imagepath + pc_dataset.fnames_images[pc_config.eval_img_id])
if params["training_type"] == "objectdetection":
prob_map_int = (255.0 - (prob_map_peaks * 255.0)).astype("uint8")
# save json of coords list
print "saving json points to", pc_config.fname_objectdetection_coords_json
json.dump(
coords_list,
codecs.open(pc_config.fname_objectdetection_coords_json, "w", encoding="utf-8"),
separators=(",", ":"),
sort_keys=True,
indent=4,
)
# save png
print "saving png probability map to ", pc_config.fname_segmentation_pmap_imagepath + pc_dataset.fnames_images[
pc_config.eval_img_id
]
img = Image.fromarray(prob_map_int)
img.save(pc_config.fname_segmentation_pmap_imagepath + pc_dataset.fnames_images[pc_config.eval_img_id])
def read_imagesandlabels_fromfile(params):
mode=params['mode']
training_id=params['training_id']
training_type=params['training_type']
job_id=params['job_id']
imgid_min=params['image_id_min']
imgid_max=params['image_id_max']
imgarray_traindata = []
imgarray_trainlabels = []
imgarray_evaldata = []
global fnames_images
#image filenames
if mode=='training':
imgpath = pc_config.datastorage_path+"trainedalgorithms/"+training_id+"/dataset/training_data/webview/"
if mode=='evaluation':
imgpath = pc_config.datastorage_path+"analysisjobs/"+job_id+"/input/"
fnames_images = [ f for f in listdir(imgpath) if isfile(join(imgpath,f)) ]
fnames_images.sort()
#check if labels exits. if not do not consider the image
if mode=='training':
for i in range(len(fnames_images)):
fname_image = os.path.splitext(os.path.basename(fnames_images[i]))[0]
fname_label = fname_image + ".json"
if not (os.path.isfile(fname_label)):
del fnames_images[i]
#load images
for i in range(imgid_min,imgid_max):
if mode=='training':
fname = imgpath+fnames_images[i]
print "reading image", fname
#with pil
img = Image.open(fname).convert('L')
#img.show()
imgarray = np.array(img,dtype="float")
if i==imgid_min: #generate array with right x y dimensions
imgarray_traindata = np.zeros( (imgid_max-imgid_min,imgarray.shape[0],imgarray.shape[1]) , dtype="float32")
imgarray_traindata[i-imgid_min] = imgarray/255.
if mode=='evaluation':
fname = imgpath+fnames_images[i]
print "reading image", fname
#with pil
img = Image.open(fname).convert('L')
#img.show()
imgarray = np.array(img)
if i==imgid_min: #generate array with right x y dimensions
imgarray_evaldata = np.zeros( (imgid_max-imgid_min,imgarray.shape[0],imgarray.shape[1]) , dtype="float32")
imgarray_evaldata[i-imgid_min] = imgarray/255.
#test
# img = Image.fromarray(imgarray_traindata[0]*255)
# img.show()
#load labels
if (training_type=='objectdetection'):
radius = params['object_size']
print "radius for labels:", radius
if (mode=='training'):
labelpath = pc_config.datastorage_path+"trainedalgorithms/"+training_id+"/dataset/training_data/label/"
fnames2 = [ f for f in listdir(labelpath) if isfile(join(labelpath,f)) ]
fnames2.sort()
#print fnames2
for i in range(imgid_min,imgid_max):
if i==imgid_min: #generate array with right x y dimensions
imgarray_trainlabels = np.zeros( (imgid_max-imgid_min,imgarray.shape[0],imgarray.shape[1]) , dtype="uint8")
fname = labelpath+fnames2[i]
print "reading labels", fname
json_data=open(fname).read()
if (training_type=='segmentation'):
labels = json.loads(json_data)
labels = np.array(labels)
imgarray_trainlabels[i-imgid_min] = labels #1-labels #we want labels to be 1 and bg to be 0.
if (training_type=='objectdetection'):
coords = json.loads(json_data)
max_x=imgarray.shape[0]
max_y=imgarray.shape[1]
for j in range(len(coords)):
#dominiks convention: x is horizontal, y is vertical, origin is left top.
#the swap here is because imagefromarray expects the [0] coordinate, i.e. x to be the vertical and not horizontal axis
#numpys first index is the row, not the colum. pil draws like print(nparray), so again x is vertical.
#this also maps to how np arrays are created e.g. a = [[x1y1,x1y2],[x2y1,x2y2]].
cx = coords[j]['y']
cy = coords[j]['x']
#print cx,cy
#imgarray_trainlabels[i-imgid_min,cx,cy] = 1
imgarray_trainlabels[i-imgid_min] = pc_utilities.circle_mask(cx,cy,radius,imgarray_trainlabels[i-imgid_min],1)
#test
# img = Image.fromarray((1-imgarray_trainlabels[0])*255) #invert color. 255 = white, but we want labels to be black.
# img.show()
# sys.exit()
print "Done."
return imgarray_traindata, imgarray_trainlabels, imgarray_evaldata
