updates

CellCounting/models/forward_density.py

@@ -16,7 +16,9 @@ import itertools as it
 import argparse
 # Image stuff
 from skimage import io as skio
-from sklearn.feature_extraction import image 
+from sklearn.feature_extraction import image
+from skimage.transform import rescale, resize, downscale_local_mean
+
 # DL stuff
 from keras.models import load_model
 
@@ -34,7 +36,7 @@ def preprocess(imnp, model, args):
     patches = image.extract_patches(imnp, patch_shape = (h, w, ncols), extraction_step = stride)
     patches = patches.reshape(-1,h,w,ncols)
     # Normalise intensities
-    patches = patches.astype(np.float32) #/ 255.
+    patches = patches.astype(np.float32) / 255.0
     img_avg = np.load(os.path.join(args.model_folder,'image_normalise','img_avg.npy'))
     img_std = np.load(os.path.join(args.model_folder,'image_normalise','img_std.npy'))
     patches = (patches - img_avg)/img_std
@@ -107,7 +109,7 @@ def forward_prediction(patches, model, args):
 
 
 
-def save_results(density, cells, image, args):         
+def save_results(density, cells, image, patch_shape, args):         
 
     basename = args.out   
     if basename is None:
@@ -116,10 +118,14 @@ def save_results(density, cells, image, args):
     outfile = basename+'_density_cells.npz'
 
     print('* Saving predictions: {}'.format(outfile))
-    np.savez_compressed(outfile, density=density, cells=cells)
+
+    # Estimate number of cells in image
+    ncells = density.mean()*image.size/patch_shape[1]/patch_shape[2]
+    print('* Detected {} Cells'.format(ncells))
+        
+    np.savez_compressed(outfile, density=density, cells=cells, ncells=ncells)
 
     #visualise results
-       
     outfile=basename+'_density.png'
     print('* Saving snapshot:    {}'.format(outfile))
     plt.imshow(image)
@@ -186,7 +192,7 @@ def main():
         # Here: depending on overall size of the image
         # Split it into bits, run forward stuff on each bit
         # Then downsample each bit and stick it all together
-        
+        model_out = forward_prediction(patches, model, args)
     else:
         model_out = forward_prediction(patches, model, args)
 
@@ -198,7 +204,7 @@ def main():
     print('** Total run time = {:.2f} seconds'.format(end_time - start_time))
 
     print('* Save results')
-    save_results(density, cells, imnp, args)
+    save_results(density, cells, imnp, patches.shape, args)
     
 
 if __name__ == '__main__':

CellCounting/models/train_model.py

@@ -136,6 +136,7 @@ def train_model(model, celldb, args):
                                     epochs = epochs, verbose=1,
                                     shuffle=True, validation_data = (X_test, y_test))
     else:
+        print('* NOT using data augmentation')
         info = model.fit(X_train,y_train, batch_size = batch_size,
                          epochs = epochs, verbose=1,
                          shuffle=True, validation_data=(X_test,y_test))
@@ -190,7 +191,9 @@ def main():
                    help='train/test split (default=0.1)')
     p.add_argument('--model', default='convnet', type=str, metavar='<str>',
                    help='choose model amongst [convet,...] (default=convnet)')
-    p.add_argument('--augment', default=False, type=bool, metavar='<bool>',
+    p.add_argument('--load_model', default=None, type=str, metavar='<str>.h5',
+                   help='load pretrained model')
+    p.add_argument('--augment', default=False, type=lambda s: s.lower() in ['true', 't', 'yes', '1'], metavar='<bool>',
                    help='use data augmentation (default=False)')
     
     # Required arguments
@@ -211,7 +214,12 @@ def main():
     
     print('* Preparing and training model')
     shape = celldb.images.shape[1:]
-    model = create_model(shape,args.model)
+    if args.load_model is not None:
+        print('** Loading pretrained model')
+        from keras.models import load_model
+        model = load_model(args.load_model)
+    else:
+        model = create_model(shape,args.model)
     info  = train_model(model, celldb, args)
 
     print('* Saving results')

CellCounting/utils/cell_report.py

@@ -3,17 +3,33 @@
 import argparse
 import jinja2 as j2
 import os.path as op
+from numpy import around, load, uint8
 
 thisdir = op.dirname(__file__)
 template_file = op.join(thisdir,'template.j2')
 
 
+def get_info(basenames):
+    image_list  = []
+    cell_counts = []
+    for b in basenames:
+        print(b)
+        if op.isfile(b+"_density.jpg"):
+            image_list.append(b+"_density.jpg")
+        else:
+            image_list.append(b+"_density.png")
+        x = load(b+"_density_cells.npz",mmap_mode='r')
+        ncells = around(x['ncells']).astype(uint8)
+        cell_counts.append(ncells)
 
-def create_html_from_template(image_list):
+    return image_list, cell_counts
+
+def create_html_from_template(image_list, cell_counts):
     with open(template_file,'rt') as f:
         template = f.read()
     template = j2.Template(template)
-    html = template.render(images=image_list)
+    images_ncells = zip(image_list, cell_counts)
+    html = template.render(images_ncells=images_ncells)
     return html
 
 def main():
@@ -22,15 +38,19 @@ def main():
 
     parser.add_argument("outfile",
                         help="Output file name", metavar='<file>.html')    
-    parser.add_argument('images', 
-                        type=str, nargs='+', metavar='<str>.tiff', 
-                        help='List of images')
+    parser.add_argument('basenames', 
+                        type=str, nargs='+', metavar='<str>', 
+                        help='List of basenames (will look for <str>_density_cells.npz and <str>_density.png/jpg')
 
     # Parse arguments
     args = parser.parse_args()
 
+    # Get list of images and ncells
+    
+    image_list, cell_counts = get_info(args.basenames)
+    
     # create html from template                
-    content = create_html_from_template(args.images)
+    content = create_html_from_template(image_list,cell_counts)
     
     with open(args.outfile,"w") as f:
         f.write(content)

CellCounting/utils/image_info.py

@@ -36,7 +36,7 @@ def main():
     # Optional arguments
    # Required arguments
     p.add_argument('image', 
-                   type=str, nargs='*', metavar='<str>.tiff [<str>.tiff ...] ', 
+                   type=str, nargs='+', metavar='<str>.tiff [<str>.tiff ...] ', 
                    help='input image or list of images')
     p.add_argument('--width',action='store_true', 
                    default=False, dest='boolean_w', 

CellCounting/utils/template.j2

@@ -68,10 +68,11 @@ body {
 </div>
 
 <!-- Photo Grid -->
-<div class="row"> 
-  {% for  i in images %}
-  <div class="column">
-    <img src="{{ i }}" style="width:100%">
+<div class="row" style="text-align:center;border:1px solid black"> 
+  {% for  img,n in images_ncells %}
+<div class="column" style="text-align:center;border:1px solid black">
+    <p> <font size="1" color="red"> ~{{ n }} cells</font>
+    <img src="{{ img }}" style="width:100%">
   </div>
     {% endfor %}
 </div>

README

-Cell Counting code
+Cell Counting Project
+
+-- Install Instructions
+
+git clone https://git.fmrib.ox.ac.uk/saad/CellCounting.git
+cd CellCounting
+pip install .
+
+
+-- Howto?
+
+
 
--- ClickCells
-   Code for creating training data
 
-   -- select_zones.py
-   -- split_zones.py
-   -- click_cells.py
 
--- Models
-   -- sequential_cnn.py