def process_xray_image(crt_xray_image, DenseNetImageNet121_model):
# print(crt_xray_image.shape)
crt_xray_image = azure_chestxray_utils.normalize_nd_array(crt_xray_image)
crt_xray_image = 255 * crt_xray_image
crt_xray_image = crt_xray_image.astype('uint8')
crt_predictions, crt_cam_image, predicted_disease_index = get_score_and_cam_picture(
crt_xray_image, DenseNetImageNet121_model)
prj_consts = azure_chestxray_utils.chestxray_consts()
likely_disease = prj_consts.DISEASE_list[predicted_disease_index]
likely_disease_prob = 100 * crt_predictions[predicted_disease_index]
likely_disease_prob_ratio = 100 * crt_predictions[
predicted_disease_index] / sum(crt_predictions)
print('predictions: ', crt_predictions)
print('likely disease: ', likely_disease)
print('likely disease prob: ', likely_disease_prob)
print('likely disease prob ratio: ', likely_disease_prob_ratio)
crt_blended_image = process_cam_image(crt_cam_image, crt_xray_image)
plot_cam_results(
crt_blended_image, crt_cam_image, crt_xray_image,
str(likely_disease) + ' ' + "{0:.1f}".format(likely_disease_prob) +
'% (weight ' + "{0:.1f}".format(likely_disease_prob_ratio) + '%)')
def get_image_score_and_serialized_cam(crt_cv2_image, crt_chest_XRay_model):
prj_consts = azure_chestxray_utils.chestxray_consts()
crt_cv2_image = azure_chestxray_utils.normalize_nd_array(crt_cv2_image)
crt_cv2_image = 255*crt_cv2_image
crt_cv2_image=crt_cv2_image.astype('uint8')
predictions, cam_image, predicted_disease_index = \
azure_chestxray_cam.get_score_and_cam_picture(crt_cv2_image, crt_chest_XRay_model)
blended_image = azure_chestxray_cam.process_cam_image(cam_image, crt_cv2_image)
serialized_image = azure_chestxray_cam.plot_cam_results(blended_image, cam_image, crt_cv2_image, \
prj_consts.DISEASE_list[predicted_disease_index])
return predictions, serialized_image
def process_nih_data(nih_data_files, NIH_data_dir, DenseNetImageNet121_model):
for crt_image in nih_data_files:
# print(crt_image)
prj_consts = azure_chestxray_utils.chestxray_consts()
crt_xray_image = cv2.imread(os.path.join(NIH_data_dir,crt_image))
crt_xray_image = cv2.resize(crt_xray_image,
(prj_consts.CHESTXRAY_MODEL_EXPECTED_IMAGE_HEIGHT,
prj_consts.CHESTXRAY_MODEL_EXPECTED_IMAGE_WIDTH)) \
.astype(np.float32)
process_xray_image(crt_xray_image, DenseNetImageNet121_model )
np.float32)
process_xray_image(crt_xray_image, DenseNetImageNet121_model)
if __name__ == "__main__":
#FIXME
# add example/test code here
NIH_annotated_Cardiomegaly = ['00005066_030.png']
data_dir = ''
cv2_image = cv2.imread(
os.path.join(data_dir, NIH_annotated_Cardiomegaly[0]))
azure_chestxray_utils.print_image_stats_by_channel(cv2_image)
cv2_image = azure_chestxray_utils.normalize_nd_array(cv2_image)
cv2_image = 255 * cv2_image
cv2_image = cv2_image.astype('uint8')
azure_chestxray_utils.print_image_stats_by_channel(cv2_image)
predictions, cam_image, predicted_disease_index = get_score_and_cam_picture(
cv2_image, model)
print(predictions)
prj_consts = azure_chestxray_utils.chestxray_consts()
print(prj_consts.DISEASE_list[predicted_disease_index])
print('likely disease: ', prj_consts.DISEASE_list[predicted_disease_index])
print('likely disease prob ratio: ',
predictions[predicted_disease_index] / sum(predictions))
blended_image = process_cam_image(cam_image, cv2_image)
plot_cam_results(blended_image, cam_image, cv2_image,
prj_consts.DISEASE_list[predicted_disease_index])