def inference(d, n_iter, H, W, labels):
"""
This function carries out the CRF inference, generating a MAP
substrate matrix, a conservative probabilistic map based on
frequentist principles, and a probability per substrate based on
the CRF inference
"""
R = []
Q, tmp1, tmp2 = d.startInference()
for k in range(n_iter):
print('Iteration: %i' % k)
d.stepInference(Q, tmp1, tmp2)
R.append(np.argmax(Q, axis=0).reshape((H, W)))
R = list(R)
l, cnt = md(np.asarray(R, dtype='uint8'), axis=0)
l = np.squeeze(l)
cnt = np.squeeze(cnt)
p = cnt / len(R)
del l, cnt
preds = np.array(Q, dtype=np.float32).reshape(
(len(labels), H, W)).transpose(1, 2, 0) ##labels+1
return np.argmax(Q, axis=0).reshape(
(H, W)), p, np.squeeze(np.expand_dims(preds, 0))
def writeout(tmp, cl, labels, outpath, thres):
l, cnt = md(cl.flatten())
l = np.squeeze(l)
if cnt / len(cl.flatten()) > thres:
outfile = id_generator() + '.jpg'
fp = outpath + os.sep + labels[l] + os.sep + outfile
imwrite(fp, tmp) ##imsave(fp, tmp)
def OpenImage(image_path, im_order):
"""
Returns the image in numpy array format
Input:
image_path : string
Full or relative path to image
config : dict
Dictionary of parameters set in the parameters file
Output:
numpy array of image 2D or 3D or 4+D
"""
with rasterio.open(image_path) as src:
# How many bands does this image have?
layer = src.read()
if layer.shape[0] == 3:
num_bands = src.count
print('Number of bands detected in image: {n}\n'.format(n=num_bands))
del layer
elif layer.shape[0] == 4:
img = tifffile.imread(image_path)
img = img_as_ubyte(img)
val, _ = md(img[:,:,-1].flatten()) # get mode value
del img, layer
if val>254: #if val is 255 (type agnositic to use >254 instead of ==255)
num_bands = 3
else:
num_bands = src.count
print('Number of bands detected in image: {n}\n'.format(n=num_bands))
## ok, we've dealt with every case and determined the number of bands
if (image_path.lower()[-3:] == 'tif') | (image_path.lower()[-4:] == 'tiff'):
if num_bands>3:
try: #4+ band tif file
img = tifffile.imread(image_path)
profile = None
except: ##<=3 band tif file
img = cv2.imread(image_path)
if im_order=='RGB':
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
elif im_order=='BGR':
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
profile = None
else: #3-band geotiff
img, profile = ReadGeotiff(image_path, im_order)
else: ###<=3 band non-tif file
img = cv2.imread(image_path)
if im_order=='RGB':
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
elif im_order=='BGR':
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
profile = None
return np.squeeze(img), profile