dicom reader

image_classifiers/eval_images_general.py

@@ -6,11 +6,11 @@ import sys
 import numpy as np
 import pandas as pd
 from PIL import Image
-from checkpoint_utils import CheckpointParser
 
+from functools import partial
 from keras.optimizers import Adam
 from keras.models import load_model
-from image import load_img_opencv
+import image
 #from image import ImageDataGenerator
 #from inception_short import get_model
 
@@ -21,27 +21,28 @@ os.environ["CUDA_VISIBLE_DEVICES"] = '3'
 os.environ["PYTHONHASHSEED"]='0'
 
 def batch_iterator(paths, batch_size=8, target_side=99, target_size=None,
-                   preprocessing_function=None):
+                   preprocessing_function=None, 
+                   img_loader=partial(image.load_img_opencv, color_mode='rgb')):
     batchx = []
     batchmeta = []
     ii = 0
     if (target_size is None) and (target_side is not None):
         target_size = (target_side, target_side)
-    for pp in (paths):
+    for filepath in (paths):
         try:
-            img = load_img_opencv(pp, color_mode='rgb', 
-                                  target_size=target_size,)
+            img = img_loader(filepath, target_size=target_size,)
             #import ipdb; ipdb.set_trace()
-            #img = Image.open(pp).convert('F')
+            #img = Image.open(filepath).convert('F')
             #if target_size is not None:
             #    img = img.resize(target_size)
             #img = np.asarray(img)/2**8
             batchx.append(img)
-            batchmeta.append(os.path.basename(pp))
+            batchmeta.append(os.path.basename(filepath))
         except Exception as ex:
             raise ex
+
         ii+=1
-        if ii%batch_size == 0:
+        if ii % batch_size == 0 or ii == len(paths):
             #batchx = np.stack([np.stack(batchx)]*3,axis=-1)
             batchx = np.stack(batchx, axis=0)
             if preprocessing_function is not None:
@@ -53,29 +54,36 @@ def batch_iterator(paths, batch_size=8, target_side=99, target_size=None,
 ##############################################
 ## AUGMENT BY FLIPPING L-R?
 fliplr = True
+
+if fliplr:
+    preprocessing_function = lambda x: x[...,::-1,:]
+    flipsuffix = 'fliplr'
+else:
+    preprocessing_function = None
+    flipsuffix = 'orig'
 ##############################################
 ## CONSTRUCT A LIST OF PNG FILE PATHS
 # this script can be modified to read DICOMs: 
 # replace `read_img_opencv` with your favorite reader in `batch_generator()`
 
-fnmeta = "/data/dlituiev/tables/2017-06-mammo_tables/df_dcm_reports_birads_path_indic_dens_birad_wi_year_noreport_nodupl.csv.gz"
-
-df = pd.read_csv(fnmeta)
-
-pngdir = "/media/exx/tron/2017-07-png-jae/"
-df["png"] = df["id"].map(lambda x: os.path.join(pngdir, x+".png"))
-png_list = df["png"].values
+fnmeta = "test.csv.gz"
+# df = pd.read_csv(fnmeta)
+# pngdir = "/media/exx/tron/2017-07-png-jae/"
+# df["png"] = df["id"].map(lambda x: os.path.join(pngdir, x+".png"))
+# png_list = df["png"].values
+png_list = ['data/test.dcm']
 
 ## FORMAT AN OUTPUT FILE 
 fnbase = os.path.basename(fnmeta).replace(".gz","").replace(".csv","")
 fnoutpred = os.path.join(os.path.dirname(fnmeta),
                          '{}-spotmag_img_prediction-{}-{}.csv'.format(
-                             fnbase, indir.split('/')[1],
+                             fnbase, 'general',
                              flipsuffix))
+
 ##############################################
 ## LOAD WEIGHTS AND OTHER INFERENCE SETTINGS
 batch_size = 128 
-WEIGHTFILE = "checkpoints/e5ce2d69b035975cb5336cec0da9a32a/model-272-general-e5ce2d69b035975cb5336cec0da9a32a.hdf5"
+WEIGHTFILE = "e5ce2d69b035975cb5336cec0da9a32a/model-272-general-e5ce2d69b035975cb5336cec0da9a32a.hdf5"
 indir = os.path.dirname(WEIGHTFILE)
 
 print(WEIGHTFILE)
@@ -86,7 +94,6 @@ with open(os.path.join(indir, "checkpoint.info")) as chkpt_fh:
 
 print("loading weights from:\t%s" % WEIGHTFILE)
 model = load_model(WEIGHTFILE)
-
 ##############################################
 
 #model = get_model(n_classes=prms["n_classes"],
@@ -98,13 +105,6 @@ model = load_model(WEIGHTFILE)
 prms["loss"] = '{}_crossentropy'.format( prms["class_mode"] )
 model.compile(optimizer=Adam(lr=prms["lr"]), loss=prms["loss"], metrics=['accuracy'])
     
-if fliplr:
-    preprocessing_function = lambda x: x[...,::-1,:]
-    flipsuffix = 'fliplr'
-else:
-    preprocessing_function = None
-    flipsuffix = 'orig'
-
 
 print("SAVING TO", fnoutpred)
 try:
@@ -113,15 +113,32 @@ except:
     print("%s\tnot found" % fnoutpred)
     pass
 
+#################################
+## set image loader
+# for PNGs:
+#img_loader=partial(image.load_img_opencv, color_mode='rgb')
+
+# for DICOMs:
+img_loader=image.load_pydicom
+
 biter = batch_iterator(png_list, batch_size=batch_size,
+                       img_loader=img_loader,
+                       target_size = (99,99),
                        preprocessing_function=preprocessing_function)
 
+#import ipdb
+#ipdb.set_trace()
+
+kwargs = dict(header=True)
+
 for nn, (filenames_, batch) in enumerate(biter):
     yscore = model.predict(batch)
     index = [ff.split("/")[-1].replace(".png","") for ff in filenames_]
     dfout = pd.Series(yscore.ravel(), 
             index=index)
-    dfout.to_csv(fnoutpred, mode='a', header=None)
+    dfout.to_csv(fnoutpred, **kwargs)
+    kwargs = dict(mode='a', header=None)
     print(nn)
+    print(dfout)
 
 print("DONE")

image_classifiers/image.py

@@ -28,6 +28,17 @@ try:
 except ImportError:
     pil_image = None
 
+try:
+    import pydicom
+except ImportError:
+    warn('unable to import pydicom')
+
+try:
+    import mudicom
+except ImportError:
+    warn('unable to import mudicom')
+
+
 if pil_image is not None:
     _PIL_INTERPOLATION_METHODS = {
         'nearest': pil_image.NEAREST,
@@ -410,7 +421,28 @@ def load_img(path, grayscale=False, color_mode='rgb', target_size=None,
     return img
         
 
-def load_img_opencv(path, grayscale=False, color_mode='rgb', target_size=None,
+def load_mudicom(fn, target_size=None):
+    mu = mudicom.load(fn)
+    img = mu.image.numpy[np.newaxis,...]
+    if target_size is not None:
+        return cv2.resize(img, target_size)
+    else:
+        return img
+
+def load_pydicom(fn, target_size=None, mode='rgb'):
+    dcm = pydicom.read_file(fn)
+    img = dcm.pixel_array
+    #import ipdb
+    #ipdb.set_trace()
+    if target_size is not None:
+        img = cv2.resize(img, target_size)
+    if mode=='rgb':
+        return np.stack([img]*3, axis=-1)
+    else:
+        return img
+
+
+def load_img_opencv(path, target_size=None, grayscale=False, color_mode='rgb', 
              interpolation='nearest'):
     """Loads an image using opencv format.
     # Arguments