def visualize_prediction( algorithms, pred_results, M, labels, output_prefix=""):
visualize_prediction.counter += 1
for m in M:
metadata = m
for algorithm in algorithms:
alg_name = algorithm[2]
mean_preds = np.sum(np.array(pred_results[alg_name])-np.array(labels), axis=0)
sd_preds = np.std(np.array(pred_results[alg_name])-np.array(labels), axis=0)
scalars = np.abs(mean_preds * 10. / np.max(mean_preds))
petacc_root = "/datagrid/Medical/microscopy/petacc3"
image_name = os.path.basename(metadata[0])[:8]
patch_size = int(os.path.basename(metadata[0]).split('_')[3][1:])
image_path = petacc_root + "/batch_2/" + image_name + ".ndpi"
if not os.path.exists(image_path):
image_path = petacc_root + "/batch_1/" + image_name + ".ndpi"
si = NDPISlideImage(image_path, None)
patchvis = si.get_patch_visualization(5, metadata[2], patch_size,
scalars=scalars[m[3]:m[3]+len(m[2])],
line_thickness=2,
show=False )
cv2.imwrite("/tmp/{}_l0_vis_".format(output_prefix)+image_name+"_"+alg_name+"features_{:d}.png".format(visualize_prediction.counter), patchvis)
def visualize_prediction( feature_name, pred_results, M, labels, train_len=0, output_prefix=""):
data_counter = 0
for m in M:
metadata = m
# mean_preds = np.sum(np.array(pred_results[alg_name])-np.array(labels), axis=0)
scalars = np.abs(pred_results * 100. / np.max(pred_results))
petacc_root = "/datagrid/Medical/microscopy/petacc3"
image_name = os.path.basename(metadata[0])[:8]
patch_size = 1024
image_path = petacc_root + "/batch_2/" + image_name + ".ndpi"
if not os.path.exists(image_path):
image_path = petacc_root + "/batch_1/" + image_name + ".ndpi"
si = NDPISlideImage(image_path, None)
patchvis = si.get_patch_visualization(6, metadata[2], patch_size,
scalars=scalars[m[3]:m[3]+len(m[2])],
line_thickness=1,
show=False, filled=True)
# if data_counter < train_len:
# cv2.putText(patchvis, 'test', (40, 40), cv2.FONT_HERSHEY_PLAIN, 1, (255, 0, 0), 2, cv2.LINE_AA)
# else:
# cv2.putText(patchvis, 'vali', (40, 40), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 255), 2, cv2.LINE_AA)
cv2.imwrite("{}_{}_{}".format(output_prefix, image_name, feature_name) + ".png", patchvis)
data_counter += 1
def visualize_prediction( algorithms, pred_results, M, labels, train_len=0, output_prefix=""):
visualize_prediction.counter += 1
data_counter = 0
validation_offset = 0
imtype = ''
for m in M:
metadata = m
for algorithm in algorithms:
alg_name = algorithm[2]
# mean_preds = np.sum(np.array(pred_results[alg_name])-np.array(labels), axis=0)
mean_preds = np.sum(np.array(pred_results[alg_name]), axis=0)
max_preds = np.max(np.array(pred_results[alg_name]), axis=0)
min_preds = np.min(np.array(pred_results[alg_name]), axis=0)
sd_preds = np.sum(np.array(pred_results[alg_name]), axis=0)
# sd_preds = np.std(np.array(pred_results[alg_name])-np.array(labels), axis=0)
scalars = np.abs(mean_preds * 100. / np.max(mean_preds))
border_scalars = np.zeros_like(scalars)
for li, label in enumerate(labels):
if label > 0:
border_scalars[li] = 100 * min_preds[li]
else:
border_scalars[li] = 100 * max_preds[li]
petacc_root = "/datagrid/Medical/microscopy/petacc3"
image_name = os.path.basename(metadata[0])[:8]
patch_size = int(os.path.basename(metadata[0]).split('_')[3][1:])
image_path = petacc_root + "/batch_2/" + image_name + ".ndpi"
if not os.path.exists(image_path):
image_path = petacc_root + "/batch_1/" + image_name + ".ndpi"
si = NDPISlideImage(image_path, None)
patchvis = si.get_patch_visualization(6, metadata[2], patch_size,
scalars=scalars[validation_offset+m[3]:validation_offset+m[3]+len(m[2])],
border_scalars=border_scalars[validation_offset+m[3]:validation_offset+m[3]+len(m[2])],
line_thickness=1,
show=False, filled=True)
if data_counter < train_len:
cv2.putText(patchvis, 'test', (40, 40), cv2.FONT_HERSHEY_PLAIN, 1, (255, 0, 0), 2, cv2.LINE_AA)
imtype='test'
else:
cv2.putText(patchvis, 'vali', (40, 40), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 255), 2, cv2.LINE_AA)
imtype='vali'
data_counter += 1
if data_counter == train_len:
validation_offset = m[3]+len(m[2])
cv2.imwrite("/local/temporary/clf_vis/{}".format(output_prefix)+image_name+"_{}_".format(imtype)+alg_name+"features_{:d}.png".format(visualize_prediction.counter), patchvis)