def _step(self):
# grab image container from port using traits
optical_c = self.grab_input_using_trait('optical_image')
thermal_c = self.grab_input_using_trait('thermal_image')
# Get python image from conatiner (just for show)
optical_npy = optical_c.image().asarray().astype('uint8')
thermal_npy = thermal_c.image().asarray().astype('uint16')
thermal_norm = normalize_thermal(thermal_npy)
if thermal_norm is not None and optical_npy is not None:
# compute transform
ret, transform, _ = compute_transform(
optical_npy,
thermal_norm,
warp_mode=cv2.MOTION_HOMOGRAPHY,
match_low_res=True,
good_match_percent=self._good_match_percent,
ratio_test=self._ratio_test,
match_height=self._match_height,
min_matches=self._min_matches,
min_inliers=self._min_inliers)
else:
ret = False
if ret:
# TODO: Make all of these computations conditional on port connection
inv_transform = np.linalg.inv(transform)
thermal_warped = cv2.warpPerspective( thermal_npy, transform, \
( optical_npy.shape[1], optical_npy.shape[0] ) )
optical_warped = cv2.warpPerspective( optical_npy, inv_transform, \
( thermal_npy.shape[1], thermal_npy.shape[0] ) )
#self.push_to_port_using_trait( 'thermal_to_optical_homog',
# F2FHomography.from_matrix( transform, 'd' )
#self.push_to_port_using_trait( 'optical_to_thermal_homog',
# F2FHomography.from_matrix( inv_transform, 'd' )
self.push_to_port_using_trait(
'warped_thermal_image',
ImageContainer.fromarray(thermal_warped))
self.push_to_port_using_trait(
'warped_optical_image',
ImageContainer.fromarray(optical_warped))
else:
print('alignment failed!')
#self.push_to_port_using_trait( "thermal_to_optical_homog", F2FHomography() )
#self.push_to_port_using_trait( "optical_to_thermal_homog", F2FHomography() )
self.push_to_port_using_trait('warped_optical_image',
ImageContainer())
self.push_to_port_using_trait('warped_thermal_image',
ImageContainer())
self._base_step()
def _step( self ):
# grab image container from port using traits
input_c = self.grab_input_using_trait( 'image' )
# Get python image from conatiner and perform operation
input_npy = input_c.image().asarray()
input_8bit = input_npy.astype( 'uint8' )
input_fft = np.fft.fft2( input_8bit )
filt = input_fft - self._noise_fft
im_filt = np.absolute( np.fft.ifft2( filt ) )
im_filt = np.log( cv2.blur( im_filt, ( self._response_kernel, self._response_kernel ) ) )
im_filt = ( im_filt - im_filt.min() ) / ( im_filt.max() - im_filt.min() )
smoothed_8bit = cv2.blur( input_8bit, ( self._smooth_kernel, self._smooth_kernel ) )
output_image = input_8bit * im_filt + smoothed_8bit * ( 1.0 - im_filt )
self.push_to_port_using_trait( 'image', ImageContainer( \
from_pil( pil_image.fromarray( output_image.astype( 'uint8' ) ) ) ) )
self._base_step()
def _step(self):
print("[DEBUG] ----- start step")
# grab image container from port using traits
in_img_c = self.grab_input_using_trait('image')
# Get image from container
in_img = in_img_c.image()
# convert generic image to PIL image
pil_image = get_pil_image(in_img)
# draw on the image to prove we can do it
num = 37
import PIL.ImageDraw
draw = PIL.ImageDraw.Draw(pil_image)
draw.line((0, 0) + pil_image.size, fill=128, width=5)
draw.line((0, pil_image.size[1], pil_image.size[0], 0),
fill=32768,
width=5)
# x0 y0 x1 y1
draw.rectangle([num, num, num + 100, num + 100], outline=125)
del draw
new_image = from_pil(pil_image) # get new image handle
new_ic = ImageContainer(new_image)
# push object to output port
self.push_to_port_using_trait('out_image', new_ic)
self._base_step()
def test_detect(self):
modules.load_known_modules()
detector = ImageObjectDetector.create("example_detector")
image = Image()
image_container = ImageContainer(image)
detections = detector.detect(image_container)
nose.tools.ok_(detections is not None,
"Unexpected empty detections" )
nose.tools.assert_equal(len(detections), 1)
def test_set_and_get_image_data(self):
dr = DescriptorRequest()
imc_list = [ImageContainer(Image())]
dr.image_data = imc_list
nt.assert_equals(len(dr.image_data), len(imc_list))
nt.assert_equals(len(imc_list), 1)
nt.assert_equals(dr.image_data[0].size(), imc_list[0].size())
nt.assert_equals(imc_list[0].size(), 0)
imc_list.append(ImageContainer(Image(720, 480)))
dr.image_data = imc_list
nt.assert_equals(len(dr.image_data), len(imc_list))
nt.assert_equals(len(imc_list), 2)
nt.assert_equals(dr.image_data[0].size(), imc_list[0].size())
nt.assert_equals(imc_list[0].size(), 0)
nt.assert_equals(dr.image_data[1].size(), imc_list[1].size())
nt.assert_equals(imc_list[1].size(), 720 * 480)
dr.image_data = []
nt.assert_equals(len(dr.image_data), 0)
def test_set_and_get_image_data(self):
qr = self._create_query_result()
imc_list = [ImageContainer(Image())]
qr.image_data = imc_list
nt.assert_equals(len(qr.image_data), len(imc_list))
nt.assert_equals(len(imc_list), 1)
nt.assert_equals(qr.image_data[0].size(), imc_list[0].size())
nt.assert_equals(imc_list[0].size(), 0)
imc_list.append(ImageContainer(Image(720, 480)))
qr.image_data = imc_list
nt.assert_equals(len(qr.image_data), len(imc_list))
nt.assert_equals(len(imc_list), 2)
nt.assert_equals(qr.image_data[0].size(), imc_list[0].size())
nt.assert_equals(imc_list[0].size(), 0)
nt.assert_equals(qr.image_data[1].size(), imc_list[1].size())
nt.assert_equals(imc_list[1].size(), 720 * 480)
qr.image_data = []
nt.assert_equals(len(qr.image_data), 0)
def filter(self, in_img):
img = in_img.image().asarray().astype("uint16")
mi = np.percentile(img, 1)
ma = np.percentile(img, 100)
normalized = (img - mi) / (ma - mi)
normalized = normalized * 255
normalized[normalized < 0] = 0
output = ImageContainer(Image(normalized.astype("uint8")))
return output
def _test_numpy(dtype_name, nchannels, order='c'):
np_img = create_numpy_image(dtype_name, nchannels, order)
img_container = ImageContainer(Image(np_img))
recast = img_container.asarray()
# asarray always returns 3 channels
np_img = np.atleast_3d(np_img)
vital_img = img_container.image()
pixel_type_name = vital_img.pixel_type_name()
pixel_type_name = vital_img.pixel_type_name()
want = map_dtype_name_to_pixel_type(dtype_name)
assert pixel_type_name == want, 'want={} but got={}'.format(
want, pixel_type_name)
if not np.all(np_img == recast):
raise AssertionError(
'Failed dtype={}, nchannels={}, order={}'.format(
dtype_name, nchannels, order))
def test_get_set_mask(self):
# Check default
do = DetectedObject(self.bbox)
nt.ok_(self.check_img_containers_equal(do.mask, None))
# Check setting through setter
do.mask = self.mask
nt.ok_(self.check_img_containers_equal(do.mask, self.mask))
# Check setting through constructor
new_mask = ImageContainer(Image(2048, 1080))
do = DetectedObject(self.bbox, mask=new_mask)
nt.ok_(self.check_img_containers_equal(do.mask, new_mask))
def _step(self):
print("[DEBUG] ----- start step")
# grab image container from port using traits
in_img_c = self.grab_input_using_trait('image')
# Get python image from conatiner (just for show)
in_img = in_img_c.get_image()
# Print out text to screen
print("Text: " + str(self.text))
# push dummy image object (same as input) to output port
self.push_to_port_using_trait('out_image', ImageContainer(in_img))
self._base_step()
def setUp(self):
# Values to setup Detected Object to hold in DOS
self.bbox1 = bb(10, 10, 20, 20)
self.bbox2 = bb(10, 10, 30, 30)
self.bbox3 = bb(5, 5, 20, 20)
self.bbox4 = bb(1, 1, 10, 10)
self.conf = 0.5
self.conf2 = 0.4
self.conf3 = 0.75
self.cm = dot("example_class", 0.4)
self.cm2 = dot("foo2", 3.14)
self.cm3 = dot("foo3", 0.11)
self.mask = ImageContainer(Image(1080, 720))
self.mask2 = ImageContainer(Image(1920, 1080))
self.mask3 = ImageContainer(Image(720, 1080))
# Establish set of DO objects to pass to DOS constructor
self.set = np.array([
do(self.bbox1, self.conf, self.cm, self.mask),
do(self.bbox2, self.conf2, self.cm2, self.mask2),
do(self.bbox3, self.conf3, self.cm3, self.mask3),
do(self.bbox4)
])
def _dowork(self, img_container):
"""
Helper to decouple the algorithm and pipeline logic
CommandLine:
xdoctest viame.processes.camtrawl.processes CamtrawlDetectFishProcess._dowork
Example:
>>> from viame.processes.camtrawl.processes import *
>>> from kwiver.vital.types import ImageContainer
>>> import kwiver.sprokit.pipeline.config
>>> # construct dummy process instance
>>> conf = kwiver.sprokit.pipeline.config.empty_config()
>>> self = CamtrawlDetectFishProcess(conf)
>>> self._configure()
>>> # construct test data
>>> from vital.util import VitalPIL
>>> from PIL import Image as PILImage
>>> pil_img = PILImage.open(ub.grabdata('https://i.imgur.com/Jno2da3.png'))
>>> pil_img = PILImage.fromarray(np.zeros((512, 512, 3), dtype=np.uint8))
>>> img_container = ImageContainer(VitalPIL.from_pil(pil_img))
>>> # Initialize the background detector by sending 10 black frames
>>> for i in range(10):
>>> empty_set = self._dowork(img_container)
>>> # now add a white box that should be detected
>>> np_img = np.zeros((512, 512, 3), dtype=np.uint8)
>>> np_img[300:340, 220:380] = 255
>>> img_container = ImageContainer.fromarray(np_img)
>>> detection_set = self._dowork(img_container)
>>> assert len(detection_set) == 1
>>> obj = detection_set[0]
"""
# This should be read as np.uint8
np_img = img_container.asarray()
detection_set = DetectedObjectSet()
ct_detections = self.detector.detect(np_img)
for detection in ct_detections:
bbox = BoundingBoxD(*detection.bbox.coords)
mask = detection.mask.astype(np.uint8)
vital_mask = ImageContainer.fromarray(mask)
dot = DetectedObjectType("Motion", 1.0)
obj = DetectedObject(bbox, 1.0, dot, mask=vital_mask)
detection_set.add(obj)
return detection_set
def _kwimage_to_kwiver_detections(detections):
"""
Convert kwimage detections to kwiver deteted object sets
Args:
detected_objects (kwimage.Detections)
Returns:
kwiver.vital.types.DetectedObjectSet
"""
from kwiver.vital.types.types import ImageContainer, Image
segmentations = None
# convert segmentation masks
if 'segmentations' in detections.data:
segmentations = detections.data['segmentations']
boxes = detections.boxes.to_tlbr()
scores = detections.scores
class_idxs = detections.class_idxs
if not segmentations:
# Placeholders
segmentations = (None, ) * len(boxes)
# convert to kwiver format, apply threshold
detected_objects = DetectedObjectSet()
for tlbr, score, cidx, seg in zip(boxes.data, scores, class_idxs,
segmentations):
class_name = detections.classes[cidx]
bbox_int = np.round(tlbr).astype(np.int32)
bounding_box = BoundingBoxD(bbox_int[0], bbox_int[1], bbox_int[2],
bbox_int[3])
detected_object_type = DetectedObjectType(class_name, score)
detected_object = DetectedObject(bounding_box, score,
detected_object_type)
if seg:
mask = seg.to_relative_mask().numpy().data
detected_object.mask = ImageContainer(Image(mask))
detected_objects.add(detected_object)
return detected_objects
def demo_image(self):
"""
Returns an image which can be run through the detector
Returns:
ImageContainer: an image of a scallop
"""
from PIL import Image as PILImage
from kwiver.vital.util import VitalPIL
from kwiver.vital.types import ImageContainer
import ubelt as ub
url = 'https://data.kitware.com/api/v1/file/5dcf0d1faf2e2eed35fad5d1/download'
image_fpath = ub.grabdata(
url, fname='scallop.jpg', appname='viame',
hash_prefix='3bd290526c76453bec7', hasher='sha512')
pil_img = PILImage.open(image_fpath)
image_data = ImageContainer(VitalPIL.from_pil(pil_img))
return image_data
def _step(self):
# grab image container from port using traits
img_c = self.grab_input_using_trait('image')
img = img_c.image().asarray().astype("uint16")
mi = np.percentile(img, 1)
ma = np.percentile(img, 100)
normalized = (img - mi) / (ma - mi)
normalized = normalized * 255
normalized[normalized < 0] = 0
output = ImageContainer(Image(normalized.astype("uint8")))
# push dummy image object (same as input) to output port
self.push_to_port_using_trait('image', output)
self._base_step()
def _step(self):
# grab image container from port using traits
in_img_c = self.grab_input_using_trait('image')
tracks = self.grab_input_using_trait('object_track_set')
# Get python image from conatiner (just for show)
in_img = get_pil_image(in_img_c.image()).convert('RGB')
if len(tracks.tracks()) == 0:
# Fill image
in_img = pil_image.new(mode='RGB',
size=in_img.size,
color=(randint(0, 255), randint(0, 255),
randint(0, 255)))
# push dummy image object (same as input) to output port
self.push_to_port_using_trait('image',
ImageContainer(from_pil(in_img)))
self._base_step()
def setUp(self):
self.loc1 = np.array([-73.759291, 42.849631])
self.loc2 = np.array([-149.484444, -17.619482])
self.bbox = BoundingBox(10, 10, 20, 20)
self.conf = 0.5
self.dot = DetectedObjectType("example_class", 0.4)
self.mask = ImageContainer(Image(1080, 720))
# Values to set outside of constructor
self.geo_point = GeoPoint(self.loc1, geodesy.SRID.lat_lon_WGS84)
self.index = 5
self.detector_name = "example_detector_name"
self.descriptor = descriptor.new_descriptor(5)
self.descriptor[:] = 10
self.note_to_add = "example_note"
self.keypoint_to_add = Point2d()
self.keypoint_to_add.value = self.loc2
self.keypoint_id = "example_keypoint_id"
def draw(self, detected_object_set, image):
u_image = cv2.cvtColor(image.asarray(), cv2.COLOR_RGB2BGR)
for detected_object in detected_object_set:
bbox = detected_object.bounding_box()
confidence = detected_object.confidence()
if self.bbox_shape == "rectangle":
u_image = cv2.rectangle(u_image,
(int(bbox.min_x()), int(bbox.min_y())),
(int(bbox.max_x()), int(bbox.max_y())),
self.bbox_color,
self.bbox_thickness)
text_origin = (int(bbox.min_x()),int(bbox.min_y()-self.bbox_thickness))
else:
center = ((int(bbox.min_x()) + int(bbox.max_x()))//2,
(int(bbox.min_y()) + int(bbox.max_y()))//2)
radius = int(math.sqrt(math.pow(float(bbox.max_y()) - \
float(bbox.min_y()), 2) + \
math.pow(float(bbox.max_x()) - \
float(bbox.min_x()), 2)))
u_image = cv2.circle(u_image, center, radius, self.bbox_color,
self.bbox_thickness)
text_origin = (center[0]-radius, center[1]-radius-self.bbox_thickness)
types = detected_object.type()
if types is None:
label = "{0}".format(confidence)
else:
label = "{0}: {1}".format(types.get_most_likely_class(),
types.get_most_likely_score())
u_image = cv2.putText(u_image,
label,
text_origin,
self.font, self.font_scale, self.bbox_color, self.font_thickness)
u_image = cv2.cvtColor(u_image, cv2.COLOR_BGR2RGB)
image_container = ImageContainer.fromarray(u_image)
return image_container
def test_save_directory(self):
dummy_image = np.zeros([100, 100])
image_container = ImageContainer.fromarray(dummy_image)
with tempfile.TemporaryDirectory() as directory_name:
self.instance.save(directory_name, image_container)
def create_image():
return ImageContainer(Image(720, 480))
def test_height(self):
i = Image(720, 480)
ic = ImageContainer(i)
nose.tools.assert_equal(ic.height(), 480)
def test_width(self):
i = Image(720, 480)
ic = ImageContainer(i)
nose.tools.assert_equal(ic.width(), 720)
def test_size(self):
i = Image(720, 480)
ic = ImageContainer(i)
nose.tools.assert_equal(ic.size(), 720 * 480)
def test_new(self):
image = Image()
img_c = ImageContainer(image)
image = Image(100, 100)
img_c = ImageContainer(image)
def extract_chips_for_dets(self, image_files, truth_sets):
import cv2
output_files = []
output_dets = []
for i in range(len(image_files)):
filename = image_files[i]
groundtruth = truth_sets[i]
detections = []
scale = 1.0
if self._target_type_scales:
scale = self.compute_scale_factor(groundtruth)
if len(groundtruth) > 0:
img = cv2.imread(filename)
if len(np.shape(img)) < 2:
continue
img_max_x = np.shape(img)[1]
img_max_y = np.shape(img)[0]
# Optionally scale image
if scale != 1.0:
img_max_x = int(scale * img_max_x)
img_max_y = int(scale * img_max_y)
img = cv2.resize(img, (img_max_x, img_max_y))
# Run optional background detector on data
if self._detector_model:
kw_image = Image(img)
kw_image_container = ImageContainer(kw_image)
detections = self._detector.detect(kw_image_container)
if len(groundtruth) == 0 and len(detections) == 0:
continue
overlaps = np.zeros((len(detections), len(groundtruth)))
det_boxes = []
for det in detections:
bbox = det.bounding_box
det_boxes.append((int(bbox.min_x()), int(bbox.min_y()),
int(bbox.width()), int(bbox.height())))
for i, gt in enumerate(groundtruth):
# Extract chip for this detection
bbox = gt.bounding_box
bbox_min_x = int(bbox.min_x() * scale)
bbox_max_x = int(bbox.max_x() * scale)
bbox_min_y = int(bbox.min_y() * scale)
bbox_max_y = int(bbox.max_y() * scale)
bbox_width = bbox_max_x - bbox_min_x
bbox_height = bbox_max_y - bbox_min_y
max_overlap = 0.0
for j, det in enumerate(det_boxes):
# Compute overlap between detection and truth
(det_min_x, det_min_y, det_width, det_height) = det
# Get the overlap rectangle
overlap_x0 = max(bbox_min_x, det_min_x)
overlap_y0 = max(bbox_min_y, det_min_y)
overlap_x1 = min(bbox_max_x, det_min_x + det_width)
overlap_y1 = min(bbox_max_y, det_min_y + det_height)
# Check if there is an overlap
if overlap_x1 - overlap_x0 <= 0 or overlap_y1 - overlap_y0 <= 0:
continue
# If yes, calculate the ratio of the overlap
det_area = float(det_width * det_height)
gt_area = float(bbox_width * bbox_height)
int_area = float(
(overlap_x1 - overlap_x0) * (overlap_y1 - overlap_y0))
overlap = min(int_area / det_area, int_area / gt_area)
overlaps[j, i] = overlap
if overlap >= self._min_overlap_for_association and overlap > max_overlap:
max_overlap = overlap
bbox_min_x = det_min_x
bbox_min_y = det_min_y
bbox_max_x = det_min_x + det_width
bbox_max_y = det_min_y + det_height
bbox_width = det_width
bbox_height = det_height
if self._chip_method == "fixed_width":
chip_width = int(self._chip_width)
half_width = int(chip_width / 2)
bbox_min_x = int(
(bbox_min_x + bbox_max_x) / 2) - half_width
bbox_min_y = int(
(bbox_min_y + bbox_max_y) / 2) - half_width
bbox_max_x = bbox_min_x + chip_width
bbox_max_y = bbox_min_y + chip_width
bbox_width = chip_width
bbox_height = chip_width
bbox_area = bbox_width * bbox_height
if self._area_lower_bound > 0 and bbox_area < self._area_lower_bound:
continue
if self._area_upper_bound > 0 and bbox_area > self._area_upper_bound:
continue
if self._reduce_category and gt.type() and \
gt.type().get_most_likely_class() == self._reduce_category and \
random.uniform( 0, 1 ) < 0.90:
continue
if self._border_exclude > 0:
if bbox_min_x <= self._border_exclude:
continue
if bbox_min_y <= self._border_exclude:
continue
if bbox_max_x >= img_max_x - self._border_exclude:
continue
if bbox_max_y >= img_max_y - self._border_exclude:
continue
crop = img[bbox_min_y:bbox_max_y, bbox_min_x:bbox_max_x]
self._sample_count = self._sample_count + 1
crop_str = ('%09d' % self._sample_count) + ".png"
new_file = os.path.join(self._chip_directory, crop_str)
cv2.imwrite(new_file, crop)
# Set new box size for this detection
gt.bounding_box = BoundingBoxD(0, 0,
np.shape(crop)[1],
np.shape(crop)[0])
new_set = DetectedObjectSet()
new_set.add(gt)
output_files.append(new_file)
output_dets.append(new_set)
neg_count = 0
for j, det in enumerate(detections):
if max(overlaps[j]) >= self._max_overlap_for_negative:
continue
bbox = det.bounding_box
bbox_min_x = int(bbox.min_x())
bbox_max_x = int(bbox.max_x())
bbox_min_y = int(bbox.min_y())
bbox_max_y = int(bbox.max_y())
bbox_width = bbox_max_x - bbox_min_x
bbox_height = bbox_max_y - bbox_min_y
bbox_area = bbox_width * bbox_height
if self._chip_method == "fixed_width":
chip_width = int(self._chip_width)
half_width = int(chip_width / 2)
bbox_min_x = int(
(bbox_min_x + bbox_max_x) / 2) - half_width
bbox_min_y = int(
(bbox_min_y + bbox_max_y) / 2) - half_width
bbox_max_x = bbox_min_x + chip_width
bbox_max_y = bbox_min_y + chip_width
bbox_width = chip_width
bbox_height = chip_width
if self._area_lower_bound > 0 and bbox_area < self._area_lower_bound:
continue
if self._area_upper_bound > 0 and bbox_area > self._area_upper_bound:
continue
if self._border_exclude > 0:
if bbox_min_x <= self._border_exclude:
continue
if bbox_min_y <= self._border_exclude:
continue
if bbox_max_x >= img_max_x - self._border_exclude:
continue
if bbox_max_y >= img_max_y - self._border_exclude:
continue
# Handle random factor
if self._max_neg_per_frame < 1.0 and random.uniform(
0, 1) > self._max_neg_per_frame:
break
crop = img[bbox_min_y:bbox_max_y, bbox_min_x:bbox_max_x]
self._sample_count = self._sample_count + 1
crop_str = ('%09d' % self._sample_count) + ".png"
new_file = os.path.join(self._chip_directory, crop_str)
cv2.imwrite(new_file, crop)
# Set new box size for this detection
det.bounding_box = BoundingBoxD(0, 0,
np.shape(crop)[1],
np.shape(crop)[0])
det.type = DetectedObjectType(self._negative_category, 1.0)
new_set = DetectedObjectSet()
new_set.add(det)
output_files.append(new_file)
output_dets.append(new_set)
# Check maximum negative count
neg_count = neg_count + 1
if neg_count > self._max_neg_per_frame:
break
return [output_files, output_dets]
def test_save_nonexistant(self):
dummy_image = np.zeros([100, 100])
image_container = ImageContainer.fromarray(dummy_image)
self.instance.save("nonexistant_filename.txt", image_container)
