# Change to calculate F1 score of detection accuracy
path = "salmonella"
imlist = []
gtlist = []
for i in range(1, 100):
imlist.append((cv2.imread(path + str(i).zfill(3) + "cell.tif",
cv2.IMREAD_GRAYSCALE)))
gtlist.append((((cv2.imread(path + str(i).zfill(3) + "dots.png",
cv2.IMREAD_GRAYSCALE))[:, :] > 0) *
100).astype(np.uint8))
DensityNet = CNN.Density()
DensityNet.load_weights("weights/salmonella.h5") # Change Weights
mean = np.mean(imlist)
std = np.std(imlist)
for i in range(len(imlist)):
imgorigin = imlist[i]
img = imlist[i]
gt = gtlist[i]
img = (img - mean) / std
img = np.expand_dims(img, -1)
img = np.expand_dims(img, 0)
density = DensityNet.predict(img)
import os
import tensorflow as tf
import numpy as np
import keras
import scipy.ndimage as ndimage
from keras import backend as K
import cv2
import CNN
def act(x):
return (K.sigmoid(x) * x)
keras.utils.generic_utils.get_custom_objects().update({'act': keras.layers.Activation(act)})
model = CNN.Density()
# Convenience function to normalise
def normalize(array):
return (array-np.mean(array))/np.std(array)
# Read Data
def read_data(path):
data1=[]
anno1=[]
for base_path in path:
imList = os.listdir(base_path)
for i in range(1,(len(imList)//2)+1):
img1 = cv2.imread(os.path.join(base_path, str(i).zfill(3)+"cell.tif"), cv2.IMREAD_GRAYSCALE)
def main(path, weights):
timelapse = Load(path)
previous = None
fps = cv2.VideoCapture(path).get(cv2.CAP_PROP_FPS)
DensityNet = CNN.Density()
DensityNet.load_weights("weights/"+weights+".h5")
vid = cv2.VideoWriter(path[:-4] + "_Cross.mp4", cv2.VideoWriter_fourcc(*'mp4v'), fps, ((timelapse[0].shape[1]//4)*4, (timelapse[0].shape[0]//4)*4))
id = 0
trees = {}
frameno=0
tag=1
if timelapse:
mean = np.mean(timelapse)
std = np.std(timelapse)
for image in timelapse:
frame = (image-mean)/std
frame = np.expand_dims(frame, 0)
frame = np.expand_dims(frame, -1)
density = DensityNet.predict(frame)
density = density.squeeze(axis=(0, -1))
count = int(np.sum(density)/100) # Calculate cell count
# Scale output density map
factor = 255.0/np.amax(density)
bwdensity = (density * factor)
colour = cv2.applyColorMap(bwdensity.astype(np.uint8), cv2.COLORMAP_JET)
dot, cross, id = Centroid(density, image, count, previous, id, tag)
vid.write(cross)
if dot:
previous = dot
cv2.imshow("1", colour); cv2.imshow("2", cross); cv2.waitKey(0)
# Add centroids to lineage tree
for cell in dot:
tree = cell[2].find(".")
if tree == -1:
tree = cell[2]
else:
tree = cell[2][:tree]
if tree not in trees:
trees[tree] = LineageTree.LineageTree(cell, frameno)
tag+=1
else:
trees[tree].insert(cell)
frameno+=1
# Display lineage tree
for i in trees:
handle = io.StringIO(str(trees[i]))
tree = Phylo.read(handle, "newick")
ax = Phylo.draw(tree, do_show=False)
ax.ticklabel_format(axis='x', useOffset=-trees[i].frameno)
ax.set_xlabel('Frame Number')
plt.show()
return trees
