def process(self, image, **kwargs):
gray = GrayScale().apply(image)
edges = Canny(low=kwargs["low"],
high=kwargs["high"],
size=kwargs["size"]).apply(gray)
if kwargs["method"] == "normal":
h_lines = cv2.HoughLines(edges, kwargs["rho"], kwargs["theta"],
kwargs["threshold"])
lines = []
if h_lines is not None:
for rho, theta in h_lines[:, 0]:
x1 = int(rho * np.cos(theta))
y1 = int(rho * np.sin(theta))
if x1 > y1:
y1 = 0
elif y1 > x1:
x1 = 0
x2 = (rho - image.shape[0] * np.sin(theta)) / np.cos(theta)
y2 = (rho - image.shape[1] * np.cos(theta)) / np.sin(theta)
if 0 <= x2 <= image.shape[1] or np.sin(theta) == 0:
y2 = image.shape[0]
elif 0 <= y2 <= image.shape[0] or np.cos(theta) == 0:
x2 = image.shape[1]
lines.append([[int(x1), int(y1)], [int(x2), int(y2)]])
else:
lines = cv2.HoughLines(
edges,
kwargs["rho"],
kwargs["theta"],
kwargs["threshold"],
kwargs["min_size"],
kwargs["max_gap"],
)
return {"lines": lines}
def test_compute():
image = Image("tests/images/lenna.png", lazy=True)
image = image.apply(GrayScale())
assert image.pending.num_transforms() == 1
computed = image.compute()
assert computed.pending.num_transforms() == 0
image.compute(in_place=True)
assert image.pending.num_transforms() == 0
def process(self, image, **kwargs):
blur = easycv.transforms.filter.Blur(method="median",
size=kwargs["size"]).apply(image)
gray = GrayScale().apply(blur)
circles = cv2.HoughCircles(
gray,
cv2.HOUGH_GRADIENT,
kwargs["dp"],
kwargs["min_dist"],
param1=kwargs["high"],
param2=kwargs["threshold"],
minRadius=kwargs["min_radius"],
maxRadius=kwargs["max_radius"],
)
return {"circles": circles[0]}
def process(self, image, **kwargs):
image = GrayScale().apply(image)
if kwargs["method"] == "sobel":
if kwargs["axis"] == "both":
x = cv2.Sobel(image, cv2.CV_64F, 1, 0, ksize=kwargs["size"])
y = cv2.Sobel(image, cv2.CV_64F, 1, 0, ksize=kwargs["size"])
return (x**2 + y**2)**0.5
if kwargs["axis"] == "x":
return cv2.Sobel(image, cv2.CV_64F, 1, 0, ksize=kwargs["size"])
else:
return cv2.Sobel(image, cv2.CV_64F, 0, 1, ksize=kwargs["size"])
elif kwargs["method"] == "laplace":
return cv2.Laplacian(image, cv2.CV_64F)
else:
kernel = np.ones((kwargs["size"], kwargs["size"]), np.uint8)
return cv2.morphologyEx(image, cv2.MORPH_GRADIENT, kernel)
def process(self, image, **kwargs):
cascade = cv2.CascadeClassifier(kwargs["cascade"])
gray = GrayScale().apply(image)
detections = cascade.detectMultiScale(
gray,
scaleFactor=kwargs["scale"],
minNeighbors=kwargs["min_neighbors"],
minSize=kwargs["min_size"]
if kwargs["min_size"] != "auto" else None,
maxSize=kwargs["max_size"]
if kwargs["max_size"] != "auto" else None,
)
rectangles = []
for x, y, w, h in detections:
rectangles.append([(x, y), (x + w, y + h)])
return {"rectangles": rectangles}
def process(self, image, **kwargs):
grayscale = GrayScale().apply(image)
if kwargs["method"] == "laplace":
sharpness = (easycv.transforms.edges.Gradient(
method="laplace").apply(grayscale).var())
else:
h, w = grayscale.shape
centerx, centery = (int(w / 2.0), int(h / 2.0))
fft = np.fft.fft2(grayscale)
fft_shift = np.fft.fftshift(fft)
size = kwargs["size"]
fft_shift[centery - size:centery + size,
centerx - size:centerx + size] = 0
fft_shift = np.fft.ifftshift(fft_shift)
recon = np.fft.ifft2(fft_shift)
sharpness = np.mean(20 * np.log(np.abs(recon)))
return {
"sharpness": sharpness,
"sharpen": sharpness >= kwargs["threshold"]
}
def test_apply():
image = Image("tests/images/lenna.png")
pipe = Pipeline([Blur(), GrayScale()])
image2 = image.apply(Blur()).apply(GrayScale())
image = image.apply(pipe)
assert image == image2
def test_pending():
image = Image("tests/images/lenna.png", lazy=True)
image = image.apply(FilterChannels(channels=[0, 1]))
image.apply(GrayScale(), in_place=True)
assert image.pending.num_transforms() == 2
def process(self, image, **kwargs):
image = GrayScale().apply(image)
x = cv2.Sobel(image, cv2.CV_64F, 1, 0, ksize=kwargs["size"])
y = cv2.Sobel(image, cv2.CV_64F, 0, 1, ksize=kwargs["size"])
return np.arctan2(x, y)