def detect_lines(img: Any, debug: bool = True) -> Any:
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
lsd = cv2.createLineSegmentDetector(0)
lines = lsd.detect(img)[0]
if debug:
drawn_img = lsd.drawSegments(img, lines)
show_image(drawn_img, wait=True)
return lines
def uahsd2():
# file = "360_F_108702068_Z9VGab1DfiyPzq2v5Xgm2wRljttzRGgq.jpg"
file = "NdNLO.jpg"
from pathlib import Path
img = cv2.imread(str(Path.home() / "Pictures" / file))
img = to_gray(img)
ret, img = cv2.threshold(img, 127, 255, 0)
skel = skeletonise(img)
show_image(skel, wait=True)
cv2.destroyAllWindows()
def main(method: ComArchEnum = ComArchEnum.pubsub,
topic: Optional[str] = None) -> None:
"""
topic: "" is all
:param method:
:param topic:
"""
if topic is None:
topic = "" # "" is all
with zmq.Context() as context:
with context.socket(method.value["dst"].value) as zmq_socket:
if method is ComArchEnum.pubsub:
zmq_socket.subscribe(topic)
# zmq_socket.setsockopt(zmq.ZMQ_RCVHWM, 1)
zmq_socket.setsockopt(zmq.CONFLATE, 1)
zmq_socket.setsockopt(zmq.LINGER, 0)
# zmq_socket.connect(SOCKET_ADDRESS2)
zmq_socket.bind(SOCKET_ADDRESS2)
while True:
frame = zmq_socket.recv_pyobj(
# flags=zmq.NOBLOCK
)
if show_image(frame):
break
def uhasd():
file = (
"white-paper-table-13912022.jpg",
"2.jpg",
"NdNLO.jpg",
"sudoku.jpg",
"sudoku2.jpg",
"sudoku3.jpg",
)
for f in file:
img = cv2.imread(str(Path.home() / "Pictures" / f))
lines = hough_lines(img, debug=True)
for line in lines: # Draw lines on the image
x1, y1, x2, y2 = line[0]
cv2.line(img, (x1, y1), (x2, y2), (255, 0, 0), 3)
if show_image(cv2.resize(img, dsize=(600, 600)), wait=True):
break
cv2.destroyAllWindows()
if __name__ == "__main__":
# file = "360_F_108702068_Z9VGab1DfiyPzq2v5Xgm2wRljttzRGgq.jpg"
# file = "istockphoto-529081402-170667a.jpg"
# file = "white-paper-table-13912022.jpg"
# file = "2.jpg"
file = "3.jpg"
# file = "NdNLO.jpg"
image = cv2.imread(str(Path.home() / "Pictures" / file))
gray = to_gray(image)
blur = noise_filter(gray,
method=NoiseFilterMethodEnum.median_blur,
ksize=5)
adapt_type = cv2.ADAPTIVE_THRESH_GAUSSIAN_C
thresh_type = cv2.THRESH_BINARY_INV
bin_img = cv2.adaptiveThreshold(blur, 255, adapt_type, thresh_type, 11, 2)
if False:
show_image(bin_img, wait=True)
lines = cv2.HoughLines(bin_img, rho=2, theta=numpy.pi / 180,
threshold=350) # Detect lines
"""
if False:
img_with_all_lines = numpy.copy(img)
draw_lines(img_with_all_lines, lines)
show_image(img_with_all_lines, wait=True)
"""
centroids = find_intersections(lines)
show_image(draw_markers(image.copy(), centroids), wait=True)
def main(model_name: str = "maskrcnn_pennfudanped", score_threshold=0.55):
"""
Args:
model_name:
score_threshold:
"""
base_path = PROJECT_APP_PATH.user_data / "maskrcnn"
dataset_root = Path.home() / "Data"
seed_stack(3825)
dataset = (
PennFudanDataset # (dataset_root / "PennFudanPed", SplitEnum.training)
)
categories = dataset.categories
if True:
model = load_model(model_name=model_name,
model_directory=base_path / "models")
else:
model = get_pretrained_instance_segmentation_maskrcnn(
dataset.response_channels)
model.to(global_torch_device())
cpu_device = torch.device("cpu")
with torch.no_grad():
with TorchEvalSession(model):
for image in tqdm(
to_tensor_generator(
frame_generator(cv2.VideoCapture(0)),
device=global_torch_device(),
)):
prediction = model(
# torch_vision_normalize_batch_nchw(
uint_hwc_to_chw_float_tensor(image).unsqueeze(0)
# )
)[0]
(boxes, labels, scores) = (
prediction["boxes"].to(cpu_device).numpy(),
prediction["labels"].to(cpu_device).numpy(),
torch.sigmoid(
prediction["scores"]).to(cpu_device).numpy(),
)
indices = scores > score_threshold
if show_image(
draw_bounding_boxes(
quick_to_pil_image(image),
boxes[indices],
labels=labels[indices],
scores=scores[indices],
categories=categories,
),
model_name,
wait=True,
):
break # esc to quit
def run_webcam_demo(
cfg: NOD,
categories: Sequence[str],
model_checkpoint: Path,
score_threshold: float = 0.5,
window_name: str = "SSD",
):
"""
:param categories:
:type categories:
:param cfg:
:type cfg:
:param model_checkpoint:
:type model_checkpoint:
:param score_threshold:
:type score_threshold:
:param window_name:
:type window_name:
:return:
:rtype:"""
cpu_device = torch.device("cpu")
transforms = SSDTransform(cfg.input.image_size,
cfg.input.pixel_mean,
split=SplitEnum.testing)
model = SingleShotDetection(cfg)
checkpointer = CheckPointer(model,
save_dir=ensure_existence(
PROJECT_APP_PATH.user_data / "results"))
checkpointer.load(model_checkpoint, use_latest=model_checkpoint is None)
print(
f"Loaded weights from {model_checkpoint if model_checkpoint else checkpointer.get_checkpoint_file()}"
)
model.post_init()
model.to(global_torch_device())
with TorchEvalSession(model):
for infos in tqdm(DictUnityEnvironment(connect_to_running=True)):
info = next(iter(infos.values()))
new_images = extract_all_cameras(info)
image = next(iter(new_images.values()))[..., :3][..., ::-1]
image = gamma_correct_float_to_byte(image)
result = model(
transforms(image)[0].unsqueeze(0).to(global_torch_device()))[0]
height, width, *_ = image.shape
result["boxes"][:, 0::2] *= width / result["img_width"]
result["boxes"][:, 1::2] *= height / result["img_height"]
(boxes, labels, scores) = (
result["boxes"].to(cpu_device).numpy(),
result["labels"].to(cpu_device).numpy(),
result["scores"].to(cpu_device).numpy(),
)
indices = scores > score_threshold
if show_image(
draw_bounding_boxes(
image,
boxes[indices],
labels=labels[indices],
scores=scores[indices],
categories=categories,
).astype(numpy.uint8),
window_name,
wait=1,
):
break # esc to quit
Created on 03/02/2022
"""
__all__ = []
from pathlib import Path
import cv2
# Load image, convert to grayscale, and find edges
from draugr.opencv_utilities import show_image
image = cv2.imread("1.jpg")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU + cv2.THRESH_BINARY)[1]
# Find contour and sort by contour area
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
ROI = None
# Find bounding box and extract ROI
for c in cnts:
x, y, w, h = cv2.boundingRect(c)
ROI = image[y:y + h, x:x + w]
break
show_image(ROI, wait=True, save_path=Path("ROI.png"))
def run_webcam_demo(
cfg: NOD,
input_cfg: NOD,
categories: List,
model_checkpoint: Path,
score_threshold: float = 0.7,
window_name: str = "SSD",
):
"""
:param categories:
:type categories:
:param cfg:
:type cfg:
:param model_checkpoint:
:type model_checkpoint:
:param score_threshold:
:type score_threshold:
:param window_name:
:type window_name:
:return:
:rtype:"""
cpu_device = torch.device("cpu")
transforms = SSDTransform(
input_cfg.image_size, input_cfg.pixel_mean, split=SplitEnum.testing
)
model = SingleShotDetectionNms(cfg)
checkpointer = CheckPointer(
model, save_dir=ensure_existence(PROJECT_APP_PATH.user_data / "results")
)
checkpointer.load(model_checkpoint, use_latest=model_checkpoint is None)
print(
f"Loaded weights from {model_checkpoint if model_checkpoint else checkpointer.get_checkpoint_file()}"
)
model.post_init()
model.to(global_torch_device())
with TorchEvalSession(model):
for image in tqdm(frame_generator(cv2.VideoCapture(0))):
result = model(transforms(image)[0].unsqueeze(0).to(global_torch_device()))
height, width, *_ = image.shape
result.boxes[:, 0::2] *= width / result.img_width.cpu().item()
result.boxes[:, 1::2] *= height / result.img_height.cpu().item()
(boxes, labels, scores) = (
result.boxes.to(cpu_device).numpy(),
result.labels.to(cpu_device).numpy(),
result.scores.to(cpu_device).numpy(),
)
indices = scores > score_threshold
if show_image(
draw_bounding_boxes(
image,
boxes[indices],
labels=labels[indices],
scores=scores[indices],
categories=categories,
score_font=ImageFont.truetype(
PACKAGE_DATA_PATH / "Lato-Regular.ttf",
24,
),
).astype(numpy.uint8),
window_name,
wait=1,
):
break # esc to quit
def hough_lines(
img,
kernel_size=11,
sigma=1.4, # 0
aperture_size=3,
rho=1,
theta=numpy.pi / 180,
min_votes=99,
lines=100,
min_line_length=10,
max_line_gap=250,
debug: bool = False,
) -> Any:
gray = to_gray(img)
if True: # remove noise
# gray = cv2.medianBlur(gray, kernel_size)
gray = cv2.GaussianBlur(gray, (kernel_size, kernel_size), sigma)
if False:
if False:
edges = cv2.Canny(gray,
threshold1=50,
threshold2=200,
apertureSize=aperture_size)
else:
adapt_type = cv2.ADAPTIVE_THRESH_GAUSSIAN_C
thresh_type = cv2.THRESH_BINARY_INV
edges = cv2.adaptiveThreshold(gray, 255, adapt_type, thresh_type,
11, 2)
else:
laplacian = cv2.Laplacian(
gray, cv2.CV_8UC1,
ksize=3) # ,cv2.CV_16UC1, #cv2.CV_16S, # cv2.CV_64F
# blurryness = resLap.var()
# sobelx = cv.Sobel(img, cv.CV_64F, 1, 0, ksize=5)
# sobely = cv.Sobel(img, cv.CV_64F, 0, 1, ksize=5)
edges = cv2.threshold(
laplacian,
0,
255,
ThresholdTypeFlag.otsu.value +
ThresholdTypeFlag.binary.value, # ThresholdTypeFlag.to_zero.value
)[1]
if True:
lines = cv2.HoughLinesP(
edges,
rho=rho,
theta=theta,
threshold=min_votes,
minLineLength=min_line_length,
maxLineGap=max_line_gap,
)
else:
lines = None
if debug:
show_image(gray)
# show_image(laplacian)
show_image(edges, wait=True)
if False:
for line in lines: # Draw lines on the image
x1, y1, x2, y2 = line[0]
cv2.line(img, (x1, y1), (x2, y2), (255, 0, 0), 3)
return lines
# the facial landmark predictor
from draugr.opencv_utilities import AsyncVideoStream, show_image
from draugr.opencv_utilities.dlib_utilities import shape_to_ndarray
p = "shape_predictor_68_face_landmarks.dat"
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(p)
cap = cv2.VideoCapture(0)
upsample_num_times = 0
for image in AsyncVideoStream():
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
for (i, rect) in enumerate(detector(gray, upsample_num_times)):
# determine the facial landmarks for the face region, then
# convert the facial landmark (x, y)-coordinates to a NumPy
# array
# loop over the (x, y)-coordinates for the facial landmarks
# and draw them on the image
for (x, y) in shape_to_ndarray(predictor(gray, rect)):
cv2.circle(image, (x, y), 2, (0, 255, 0), -1)
if show_image(
image, wait=5, char="q"
): # show the output image with the face detections + facial landmarks
break
cv2.destroyAllWindows()
cap.release()
In some cases, we may need all the points which comprises that object. It can be done as follows:
:param cnt:
:type cnt:
:return:
:rtype:
"""
mask = numpy.zeros(im_shape, numpy.uint8)
cv2.drawContours(mask, [cnt], 0, 255, -1)
return numpy.transpose(numpy.nonzero(mask))
def convexity_defects(cnt: numpy.ndarray) -> numpy.ndarray:
"""
Notice that "returnPoints = False" in first line to get indices of the contour points, because input to convexityDefects() should be these indices, not original points.
It returns a defects structure, an array of four values - [ start point, end point, farthest point, approximate distance to farthest point ]
"""
return cv2.convexityDefects(cnt, cv2.convexHull(cnt, returnPoints=False))
if __name__ == "__main__":
from neodroidvision.utilities.misc.perlin import generate_perlin_noise
from draugr.opencv_utilities import threshold_channel, show_image
a = generate_perlin_noise() * 255
a = numpy.uint8(a)
t = threshold_channel(a)
show_image(t, wait=True)
from draugr.opencv_utilities import show_image from neodroidvision.utilities.misc.perlin import generate_perlin_noise show_image(generate_perlin_noise((100, 100)), wait=True)
