def imageToFeatures(im):
rim = resizeToArea(im)
cim = toCylindrical(rim)
segments, nSeg = segmentation(cim)
colorF = colorFeatures(cim, rim, (segments, nSeg))
textureF = textureFeatures(cim)
compositionF = compositionFeatures(rim, segments)
print(".", end="")
sys.stdout.flush()
return np.concatenate((colorF, textureF, compositionF))
def hisi(input, use_quadratic=True):
print()
print("====== HISI ======")
print()
#paramaters settings
num_objects = 0
max_num_objects = 10
max_num_bounds = 1000 # repesent the maximum size of an object (security)
min_num_bounds = 7 # represent the minimum border of boundaries before being considred as an object, in order to avoid "noisy pixel" 4 = 1 pixel, 6 = 2 pixels
thresh_bound = 0.25
# todo change max bound as a function of the image size ?
max_induc_bound = 3
if np.shape(input)[0] > 100:
max_induc_bound = 14
print("max induc bound: ", max_induc_bound)
boundaries = get_boundaries(input)
output_img = []
output_bound = []
output_img.append(np.copy(input))
output_bound.append(np.copy(boundaries))
# plt.figure()
# plt.imshow(show_matrix(input, boundaries))
# plt.show()
segmentation(input, boundaries, thresh_bound, max_induc_bound,
min_num_bounds, max_num_bounds, num_objects, max_num_objects,
output_img, output_bound, use_quadratic)
print()
print("====== Finish ======")
return output_img, output_bound
def transform(self, X, y=None):
t0_tot = time.time()
self.true_idx = []
self.n_too_small = 0
self.filter_time = 0
self.segment_time = 0
self.smileTime = 0
for i, sig in enumerate(X):
if i % 100 == 0:
print('\n' * 30)
print(i)
print('\n' * 30)
t0 = time.time()
filtered_sig = filter_sig(sig, width=self.width,
ripple_db=self.ripple_db,
cutoff_hz=self.cutoff_hz)
self.filter_time += time.time() - t0
self.debug = filtered_sig # To remove
self.debug_i = i # To remove
t0 = time.time()
segmented_sig = segmentation(filtered_sig)
self.segment_time += time.time() - t0
segmented_sig = list(filter(lambda x: len(x) > 1600,
segmented_sig))
if not segmented_sig:
segmented_sig = [filtered_sig]
for syllabe in segmented_sig:
print(len(syllabe))
t0 = time.time()
df = runOpenSmile(syllabe)
self.smileTime += time.time() - t0
self.mfcc_shape.append(df.shape[0])
self.true_idx.append(i)
yield aggregateMfcc(df, use_kurt=self.use_kurt,
use_skew=self.use_skew)
print('Filter time :', self.filter_time)
print('Segment time:', self.segment_time)
print('Smile time:', self.smileTime)
print('Total time:', time.time() - t0_tot)
def transform_one_signal(self, sig, i):
if i % 100 == 0:
print('\n' * 30)
print(i)
print('\n' * 30)
t0 = time.time()
filtered_sig = filter_sig(sig, width=self.width,
ripple_db=self.ripple_db,
cutoff_hz=self.cutoff_hz)
self.filter_time += time.time() - t0
self.debug = filtered_sig # To remove
self.debug_i = i # To remove
t0 = time.time()
segmented_sig = segmentation(filtered_sig)
self.segment_time += time.time() - t0
segmented_sig = list(filter(lambda x: len(x) > 1600,
segmented_sig))
if not segmented_sig:
segmented_sig = [filtered_sig]
self.true_idx.extend([i] * len(segmented_sig))
return Parallel(n_jobs=self.n_jobs)(
delayed(transform_one_syllabe(self, syllabe, i))
for syllabe in segmented_sig)
def run():
segmentation(image='./src/test9.png')
test_list_images(model='final_model_5.h5')
def callback_cloud(self, msg):
cloud_points = list(
pc2.read_points(msg,
field_names=("x", "y", "z", "intensity"),
skip_nans=True))
bbox_3d, obj_points = segmentation(cloud_points)
fields = [
PointField('x', 0, PointField.FLOAT32, 1),
PointField('y', 4, PointField.FLOAT32, 1),
PointField('z', 8, PointField.FLOAT32, 1),
PointField('intensity', 16, PointField.FLOAT32, 1)
]
if obj_points:
obj_types, obj_classified = classification(obj_points)
rospy.loginfo('obj_classified: {}'.format(
[obj_types[i] for i in obj_classified]))
if sum(obj_classified == 0) > 0:
pedestrian_points = [
obj for indx, obj in enumerate(obj_points)
if obj_classified[indx] == 0
]
pedestrian_cloud = np.row_stack(pedestrian_points)
pc_pedestrian = pc2.create_cloud(msg.header, fields,
pedestrian_cloud)
else:
pc_pedestrian = pc2.create_cloud(msg.header, fields, [])
if sum(obj_classified == 1) > 0:
car_points = [
obj for indx, obj in enumerate(obj_points)
if obj_classified[indx] == 1
]
car_cloud = np.row_stack(car_points)
pc_car = pc2.create_cloud(msg.header, fields, car_cloud)
# publish the centers of the vehicle object as geometry_msgs.PoseArray
car_poses = [
np.mean(bbox, axis=0) for indx, bbox in enumerate(bbox_3d)
if obj_classified[indx] == 1
]
car_PoseArray = self.Arr2poseArray(car_poses)
car_PoseArray.header = msg.header
else:
pc_car = pc2.create_cloud(msg.header, fields, [])
car_PoseArray = PoseArray()
car_PoseArray.header = msg.header
if sum(obj_classified == 2) > 0:
cyclist_points = [
obj for indx, obj in enumerate(obj_points)
if obj_classified[indx] == 2
]
cyclist_cloud = np.row_stack(cyclist_points)
pc_cyclist = pc2.create_cloud(msg.header, fields,
cyclist_cloud)
else:
pc_cyclist = pc2.create_cloud(msg.header, fields, [])
if sum(obj_classified == 3) > 0:
misc_points = [
obj for indx, obj in enumerate(obj_points)
if obj_classified[indx] == 3
]
misc_cloud = np.row_stack(misc_points)
pc_misc = pc2.create_cloud(msg.header, fields, misc_cloud)
else:
pc_misc = pc2.create_cloud(msg.header, fields, [])
else:
pc_pedestrian = pc2.create_cloud(msg.header, fields, [])
pc_car = pc2.create_cloud(msg.header, fields, [])
pc_cyclist = pc2.create_cloud(msg.header, fields, [])
pc_misc = pc2.create_cloud(msg.header, fields, [])
self._pub1.publish(pc_pedestrian)
self._pub2.publish(pc_car)
self._pub3.publish(pc_cyclist)
self._pub4.publish(pc_misc)
self._pub5.publish(car_PoseArray)
# check if screenshot or photograph
if find_scan_screenshot(img) == 1:
img_rotated = skew_correction(new_img)
else:
#img_rotated = new_img
img_rotated = skew_correction_scanned(new_img)
# img_rotated = new_img
img_rotated[img_rotated == 1] = 255
plt.imshow(img_rotated, cmap='gray')
plt.title("skew corrected image")
plt.show()
# plt.imsave("misc/skew_corrected_image.png",img_rotated,cmap='gray')
# character segmentation
centroids, bboxs, convex_hulls, imgs = segmentation(img_rotated)
#draw bounding box and show different segments
draw_bounding_box(img_rotated, bboxs)
#for i in range(0,len(imgs)):
# str_ele2 = skimage.morphology.selem.disk(2)
# str_ele = skimage.morphology.selem.square(3)
# imgs[i] = skimage.morphology.binary_erosion(imgs[i],selem=str_ele2)
# imgs[i] = skimage.morphology.binary_dilation(imgs[i],selem=str_ele)
#plt.imshow(imgs[i],cmap='gray')
#shift centroid origin
centroids_new = create_new_centroids(centroids, bboxs, imgs)
detected_chars = []
#character identification and matching
