Skip to content

Commit

Permalink
Multiple row detection implemented
Browse files Browse the repository at this point in the history
  • Loading branch information
@MiroslavPolacek
MiroslavPolacek committed Jun 14, 2022
1 parent db4cc12 commit daa975b
Showing 1 changed file with 92 additions and 78 deletions.
170 changes: 92 additions & 78 deletions CoreProcessingPipelineScripts/CNN/Mask_RCNN/postprocessing/postprocessingCracksRings.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -62,6 +62,9 @@
parser.add_argument('--min_mask_overlap', required=False,
help="Minimum of detected masks to consider good detection")

parser.add_argument('--n_detection_rows', required=False,
help="Minimum of detected masks to consider good detection")

parser.add_argument('--logfile', required=False,
metavar="logfile",
help="logfile name to put in output dir. Prepends other info (used to be 'CNN_')")
Expand Down Expand Up @@ -197,7 +200,7 @@ def write_run_info(string):
############################################################################################################
# Sliding window detection with rotation of each part of image by 90 and 45 degrees and combining the output
############################################################################################################
def sliding_window_detection(image, modelRing=None, modelCrack=None, overlap=0.75, cropUpandDown=0.17):
def sliding_window_detection_multirow(image, detection_rows=1, modelRing=None, modelCrack=None, overlap=0.75, row_overlap=0.1, cropUpandDown=0.17):
write_run_info("Sliding window overlap = {} and cropUpandDown = {}".format(overlap, cropUpandDown))
# Crop image top and bottom to avoid detectectig useles part of the image
imgheight_origin, imgwidth_origin = image.shape[:2]
Expand All @@ -219,8 +222,20 @@ def sliding_window_detection(image, modelRing=None, modelCrack=None, overlap=0.7
imgheight, imgwidth = im_padded.shape[:2]
#print('im_after_pad', im_padded.shape)

looping_range = range(0,imgwidth, int(imgheight-(imgheight*overlap)))
looping_list = [i for i in looping_range if i < int(imgheight_for_pad-(imgheight_for_pad*overlap)) + imgwidth_origin]
# Define sliding window parameters
## rows
if detection_rows > 1:
row_overlap = row_overlap
row_overlap_height = int((imgheight*row_overlap)/(detection_rows-1))
row_height = int(((row_overlap_height*(detection_rows-1))+imgheight)/detection_rows)
row_looping_range = range(0, imgheight-(row_height-1), int(row_height-row_overlap_height))
else:
row_looping_range = [0]
row_height = imgheight

## columns
looping_range = range(0,imgwidth, int(row_height-(row_height*overlap)))
looping_list = [i for i in looping_range if i < int(row_height-(row_height*overlap)) + imgwidth_origin]
#print('looping_list', looping_list)

combined_masks_per_class = np.empty(shape=(imgheight, imgwidth,0))
Expand All @@ -230,80 +245,72 @@ def sliding_window_detection(image, modelRing=None, modelCrack=None, overlap=0.7
models = [modelRing, modelCrack]

for model in models:
the_mask = np.zeros(shape=(imgheight, imgwidth)) #combine all the partial masks in the final size of full tiff
the_mask = np.zeros(shape=(imgheight, imgwidth)) # combine all the partial masks in the final size of full tiff
#print('the_mask', the_mask.shape)
for i in looping_list: #defines the slide value

#print("i", i)
# crop the image
cropped_part = im_padded[:imgheight, i:(i+imgheight)]
#print('cropped_part, i, i+imheight', cropped_part.shape, i, i+imgheight)

# Run detection on the cropped part of the image
results = model.detect([cropped_part], verbose=0)
r = results[0]
r_mask = r['masks']
#visualize.display_instances(cropped_part, r['rois'], r['masks'], r['class_ids'], class_names, r['scores']) #just to check

# Rotate image 90 and run detection
cropped_part_90 = skimage.transform.rotate(cropped_part, 90, preserve_range=True).astype(np.uint8)
results1 = model.detect([cropped_part_90], verbose=0)
r1 = results1[0]
r1_mask = r1['masks']
#visualize.display_instances(cropped_part_90, r1['rois'], r1['masks'], r1['class_ids'], class_names, r1['scores']) #just to check

# Rotate image 45 and run detection
cropped_part_45 = skimage.transform.rotate(cropped_part, angle=45, resize=True).astype(np.uint8)
results2 = model.detect([cropped_part_45], verbose=0)
r2 = results2[0]
r2_mask = r2['masks']

## Flatten all in one layer
maskr = np.zeros(shape=(imgheight, imgheight))
#print('maskr_empty', maskr)
nmasks = r_mask.shape[2]
#print(nmasks)
for m in range(0,nmasks):
maskr = maskr + r_mask[:,:,m]
#print(maskr.sum())

maskr1 = np.zeros(shape=(imgheight, imgheight))
nmasks1 = r1_mask.shape[2]
for m in range(0,nmasks1):
maskr1 = maskr1 + r1_mask[:,:,m]
# Rotate maskr1 masks back
maskr1_back = np.rot90(maskr1, k=-1)
# Beware different dimensions!!!

imheight2 = r2_mask.shape[0]
nmasks2 = r2_mask.shape[2]
maskr2 = np.zeros(shape=(imheight2, imheight2))
for m in range(0,nmasks2):
maskr2 = maskr2 + r2_mask[:,:,m]
# Rotate back
maskr2_back = skimage.transform.rotate(maskr2, angle=-45, resize=False)
# Crop to the right size
to_crop = int((imheight2 - imgheight)/2)

maskr2_back_cropped = maskr2_back[to_crop:(to_crop+int(imgheight)), to_crop:(to_crop+int(imgheight))]

# Put both togather
combined_mask = maskr1_back + maskr + maskr2_back_cropped
# Merge with the relevant position of the big mask and overlap with previous one
cropped_the_mask = the_mask[:imgheight, i:(i+imgheight)]
#print('the_mask_piece', cropped_the_mask.shape)
all_masks_combined = combined_mask + cropped_the_mask
#print("all_masks_combined", all_masks_combined.shape)
#all_masks_combined = np.reshape(all_masks_combined, (all_masks_combined.shape[0],all_masks_combined.shape[1],1))
#print('middle', all_masks_combined.shape)
end_the_mask = the_mask[:imgheight, (i+imgheight):]

if i == 0: # to solve the begining
the_mask = np.concatenate((all_masks_combined, end_the_mask), axis=1)
else:
begining_the_mask = the_mask[:imgheight, :i]
the_mask = np.concatenate((begining_the_mask, all_masks_combined, end_the_mask), axis=1)
#print("the_mask.shape", the_mask.shape)
for rl in row_looping_range:
print("r", rl)
for i in looping_list: # defines the slide value
print("i", i)

#print("i", i)
# crop the image
cropped_part = im_padded[rl:rl+row_height, i:i+row_height]
#print('cropped_part, i, i+imheight', cropped_part.shape, i, i+imgheight)

# Run detection on the cropped part of the image
results = model.detect([cropped_part], verbose=0)
r = results[0]
r_mask = r['masks']
#visualize.display_instances(cropped_part, r['rois'], r['masks'], r['class_ids'], class_names, r['scores']) #just to check

# Rotate image 90 and run detection
cropped_part_90 = skimage.transform.rotate(cropped_part, 90, preserve_range=True).astype(np.uint8)
results1 = model.detect([cropped_part_90], verbose=0)
r1 = results1[0]
r1_mask = r1['masks']
#visualize.display_instances(cropped_part_90, r1['rois'], r1['masks'], r1['class_ids'], class_names, r1['scores']) #just to check

# Rotate image 45 and run detection
cropped_part_45 = skimage.transform.rotate(cropped_part, angle=45, resize=True).astype(np.uint8)
results2 = model.detect([cropped_part_45], verbose=0)
r2 = results2[0]
r2_mask = r2['masks']

## Flatten all in one layer
maskr = np.zeros(shape=(row_height, row_height))
#print('maskr_empty', maskr)
nmasks = r_mask.shape[2]
#print(nmasks)
for m in range(0,nmasks):
maskr = maskr + r_mask[:,:,m]
#print(maskr.sum())

maskr1 = np.zeros(shape=(row_height, row_height))
nmasks1 = r1_mask.shape[2]
for m in range(0,nmasks1):
maskr1 = maskr1 + r1_mask[:,:,m]
# Rotate maskr1 masks back
maskr1_back = np.rot90(maskr1, k=-1)
# Beware different dimensions!!!

imheight2 = r2_mask.shape[0]
nmasks2 = r2_mask.shape[2]
maskr2 = np.zeros(shape=(imheight2, imheight2))
for m in range(0,nmasks2):
maskr2 = maskr2 + r2_mask[:,:,m]
# Rotate back
maskr2_back = skimage.transform.rotate(maskr2, angle=-45, resize=False)
# Crop to the right size
to_crop = int((imheight2 - row_height)/2)

maskr2_back_cropped = maskr2_back[to_crop:(to_crop+int(row_height)), to_crop:(to_crop+int(row_height))]

# Put all togather
combined_mask = maskr1_back + maskr + maskr2_back_cropped
print("combined mask shape", combined_mask.shape)
print("the_mask shape", the_mask.shape)
print("the_mask[] type", the_mask[rl:rl+row_height, i:i+row_height].shape)
the_mask[rl:rl+row_height, i:i+row_height] = the_mask[rl:rl+row_height, i:i+row_height] + combined_mask

the_mask = np.reshape(the_mask, (the_mask.shape[0],the_mask.shape[1],1))
combined_masks_per_class = np.append(combined_masks_per_class, the_mask, axis=2)
Expand Down Expand Up @@ -942,7 +949,7 @@ def main():
image_path = os.path.join(input_path, f)
im_origin = skimage.io.imread(image_path)

# Define cropUpandDown and overlap values if they were not provided as arguments
# Define cropUpandDown, overlap and detection_rows values if they were not provided as arguments
if args.cropUpandDown is not None:
cropUpandDown = float(args.cropUpandDown)
else:
Expand All @@ -953,11 +960,18 @@ def main():
else:
sliding_window_overlap = 0.75

detected_mask = sliding_window_detection(image = im_origin,
if args.n_detection_rows is None or args.n_detection_rows==1:
detection_rows = 1
else:
detection_rows=int(args.n_detection_rows)

detected_mask = sliding_window_detection_multirow(image = im_origin,
detection_rows=detection_rows,
modelRing=modelRing,
modelCrack=modelCrack,
overlap = sliding_window_overlap,
cropUpandDown = cropUpandDown)

detected_mask_rings = detected_mask[:,:,0]
print("detected_mask_rings", detected_mask_rings.shape)

Expand Down

0 comments on commit daa975b

Please sign in to comment.