def preprocess_ground_truth(self):
print('Extracting features for data {}'.format(self.config.data_name))
data = []
self.ground_truth = pickle.load(
open(self.config.path.ground_truth,
'rb'))[:self.config.data_size.max_data_size]
if self.config.data_type == 'synthetic':
grid = [0, 0, 1248, 384]
elif self.config.data_type == 'naturalistic':
grid = [570, 442, 1350, 682]
'''Examples: the consecutive frames are 69,70,71,72,95,96,97,98
Before the loop, next_idx gives 99
The loop is in reverse order: 98,97,96,95,72,71,70,69
'''
# ---------------- Initialize data for calculating image difference ----------------
# Obtain the length of the prefix
len_prefix = len(self.config.image_name_convention.prefix)
len_postfix = len(self.config.image_name_convention.postfix)
next_idx, next_file_name, next_velocity_im = self.get_next_image(
self.ground_truth[-1]['image_name'], len_prefix, len_postfix, grid)
# ---------------- Loop through data in reverse order ----------------
for file in reversed(self.ground_truth):
# read and crop the image
im = cv2.imread(
os.path.join(self.config.path.images,
file['image_name']))[grid[1]:grid[3],
grid[0]:grid[2], :]
# ---------------- obtain image array for distance ----------------
distance_im = cv2.resize(im, (1248, 384))
distance_im = distance_im[0:384, 432:816, :] # im_size: H*W*C
# ---------obtain image array (diff between two consecutive images) for velocity ----------------
velocity_im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
velocity_im = cv2.resize(velocity_im, (220, 66),
interpolation=cv2.INTER_AREA)
# obtain next image's index, image name and image array
# Example: for indice 98,97,96,95,72,71,70,69
# 72+1 != 95 = next_idx, the following command resets next_idx = 72+1 = 73
# otherwise, no change made to next_idx, next_file_name, next_velocity_im
cur_idx = int(file['image_name'][len_prefix:-len_postfix])
if cur_idx + 1 != next_idx:
next_idx, next_file_name, next_velocity_im = self.get_next_image(
file['image_name'], len_prefix, len_postfix, grid)
im_diff = opticalFlowDenseDim3(velocity_im, next_velocity_im)
# ----------------------- Append all data -----------------------
data.append({
'cur_frame': file['image_name'],
'next_frame': next_file_name,
'true_distance': file['true_distance'],
'true_velocity': file['true_velocity'],
'distance_im': distance_im,
'velocity_im_diff': im_diff
})
next_idx, next_file_name, next_velocity_im = cur_idx, file[
'image_name'], velocity_im
return data
if time_now - time_prev > 0 and 0.0000001 < time_now - time_prev < 0.58: # 0.578111 is highest diff i have seen
# in this case row_prev is x1 and row_now is x2
row1 = row_prev
row2 = row_now
elif time_next - time_now > 0 and 0.0000001 < time_next - time_now < 0.58:
# in this case row_now is x1 and row_next is x2
row1 = row_now
row2 = row_next
x1, y1 = preprocess_image_valid_from_path(row1['image_path'].values[0],
row1['speed'].values[0])
x2, y2 = preprocess_image_valid_from_path(row2['image_path'].values[0],
row2['speed'].values[0])
img_diff = opticalFlowDenseDim3(x1, x2)
img_diff_reshaped = img_diff.reshape(1, img_diff.shape[0],
img_diff.shape[1], img_diff.shape[2])
prediction = model.predict(img_diff_reshaped)
error = abs(prediction - y2)
predict_path = os.path.join(TEST_PREDICT_PATH, str(idx) + '.jpg')
# overwrite the prediction of y2 onto image x2
# save overwritten image x2 to new directory ./data/predict
# Make a copy
x2_copy = np.copy(x2)
# to write new image via openCV
offset = 30
