def joint_prediction(sess, predicted_boxes):
#tensorflow sessions
new_boxes, new_scores, new_labels = predict.non_max_suppression(
sess,
predicted_boxes[["xmin", "ymin", "xmax", "ymax"]].values,
predicted_boxes.score.values,
predicted_boxes.label.values,
max_output_size=predicted_boxes.shape[0],
iou_threshold=0.1)
#Recreate box dataframe
image_detections = np.concatenate([
new_boxes,
np.expand_dims(new_scores, axis=1),
np.expand_dims(new_labels, axis=1)
],
axis=1)
mosaic_df = pd.DataFrame(image_detections,
columns=["xmin", "ymin", "xmax", "ymax", "score", "label"])
mosaic_df.label = mosaic_df.label.str.decode("utf-8")
print("{} predictions kept after non-max suppression".format(mosaic_df.shape[0]))
return mosaic_df
def predict_tile(self,
raster_path=None,
numpy_image=None,
patch_size=400,
patch_overlap=0.05,
iou_threshold=0.15,
return_plot=False,
show=False):
"""For images too large to input into the model, predict_tile cuts the
image into overlapping windows, predicts trees on each window and
reassambles into a single array.
Args:
raster_path: Path to image on disk
numpy_image (array): Numpy image array in BGR channel order
following openCV convention
patch_size: patch size default400,
patch_overlap: patch overlap default 0.15,
iou_threshold: Minimum iou overlap among predictions between
windows to be suppressed. Defaults to 0.5.
Lower values suppress more boxes at edges.
return_plot: Should the image be returned with the predictions drawn?
show (bool): Plot the predicted image with bounding boxes.
Ignored if return_plot=False
Returns:
boxes (array): if return_plot, an image.
Otherwise a numpy array of predicted bounding boxes, scores and labels
"""
if numpy_image is not None:
pass
else:
# Load raster as image
raster = Image.open(raster_path)
numpy_image = np.array(raster)
# Compute sliding window index
windows = preprocess.compute_windows(numpy_image, patch_size, patch_overlap)
# Save images to tmpdir
predicted_boxes = []
for index, window in enumerate(tqdm(windows)):
# Crop window and predict
crop = numpy_image[windows[index].indices()]
# Crop is RGB channel order, change to BGR
crop = crop[..., ::-1]
boxes = self.predict_image(numpy_image=crop,
return_plot=False,
score_threshold=self.config["score_threshold"])
# transform coordinates to original system
xmin, ymin, xmax, ymax = windows[index].getRect()
boxes.xmin = boxes.xmin + xmin
boxes.xmax = boxes.xmax + xmin
boxes.ymin = boxes.ymin + ymin
boxes.ymax = boxes.ymax + ymin
predicted_boxes.append(boxes)
predicted_boxes = pd.concat(predicted_boxes)
# Non-max supression for overlapping boxes among window
if patch_overlap == 0:
mosaic_df = predicted_boxes
else:
with tf.Session() as sess:
print(
"{} predictions in overlapping windows, applying non-max supression".
format(predicted_boxes.shape[0]))
new_boxes, new_scores, new_labels = predict.non_max_suppression(
sess,
predicted_boxes[["xmin", "ymin", "xmax", "ymax"]].values,
predicted_boxes.score.values,
predicted_boxes.label.values,
max_output_size=predicted_boxes.shape[0],
iou_threshold=iou_threshold)
# Recreate box dataframe
image_detections = np.concatenate([
new_boxes,
np.expand_dims(new_scores, axis=1),
np.expand_dims(new_labels, axis=1)
],
axis=1)
mosaic_df = pd.DataFrame(
image_detections,
columns=["xmin", "ymin", "xmax", "ymax", "score", "label"])
if mosaic_df.shape[0] == 0:
print("No predictions made, returning None")
return None
mosaic_df.label = mosaic_df.label.str.decode("utf-8")
print("{} predictions kept after non-max suppression".format(
mosaic_df.shape[0]))
if return_plot:
# Draw predictions
for box in mosaic_df[["xmin", "ymin", "xmax", "ymax"]].values:
draw_box(numpy_image, box, [0, 0, 255])
if show:
plt.imshow(numpy_image[:, :, ::-1])
plt.show()
# Mantain consistancy with predict_image
return numpy_image
else:
return mosaic_df