def filterRegions(img_orig, regions):
width = 90
height = 30
current_path = os.path.dirname(os.path.realpath(__file__))
clf = joblib.load(os.path.join(current_path, "models", "svm.pkl"))
if len(regions) > 0:
scores = []
for i, region in enumerate(regions):
minr, minc, maxr, maxc = region.bbox
img_window = img_orig[minr: maxr, minc: maxc]
img_window = transform.resize(img_window, (height, width))
img_window = np.absolute(filters.prewitt_v(img_window))
features = img_window.ravel()
scores.append((clf.decision_function(features)[0], i))
# all scores
scores.sort(reverse=True)
scores = np.array(scores)
i = int(scores[0, 1])
return [regions[i]]
return []
def test_vprewitt_vertical():
"""Vertical prewitt on an edge should be a vertical line."""
i, j = np.mgrid[-5:6, -5:6]
image = (j >= 0).astype(float)
result = filters.prewitt_v(image)
# Fudge the eroded points
j[np.abs(i) == 5] = 10000
assert (np.all(result[j == 0] == 1))
assert_allclose(result[np.abs(j) > 1], 0, atol=1e-10)
def prewittk(image_file, path_to_train):
im = imread(image_file, as_gray=True)
edges_prewitt_horizontal = prewitt_h(im)
edges_prewitt_vertical = prewitt_v(im)
imsave(image_file[:-4] + '_p.jpg', edges_prewitt_vertical, cmap='gray')
base = os.path.basename(image_file)
base = base[:-4] + '.txt'
shutil.copy2(path_to_train + "/labels/" + base,
path_to_train + "/labels/" + base[:-4] + '_p.txt')
def refineBoundaries(img_orig, plate):
np.random.seed(7)
minr, minc, maxr, maxc = plate.bbox
img_window = img_orig[minr: maxr, minc: maxc]
plate_points = [(minc, minr), (maxc, minr), (maxc, maxr), (minc, maxr)]
plate_x = minc
plate_y = minr
img_window = np.absolute(filters.prewitt_v(img_window))
thresh = filters.threshold_otsu(img_window)
img_window = img_window <= thresh
labels = measure.label(img_window)
points = []
for region in measure.regionprops(labels):
minr, minc, maxr, maxc = region.bbox
ratio = float(maxr - minr) / float(maxc - minc)
heigh = maxr - minr
area = region.area
if (ratio > 1 and area > 10 and heigh > 10):
points.append((minc, minr, maxc, maxr))
if len(points) > 1:
points = np.array(points)
x1 = np.min(points[:, 0])
x2 = np.max(points[:, 2])
ransac_model, inliers = measure.ransac(points[:, 0:2], measure.LineModel, 5, 3, max_trials=30)
points = points[inliers]
if ransac_model.params[1] != 0:
average_heigh = int(np.mean(points[:, 3]) - np.mean(points[:, 1]))
pad_t = average_heigh / 2
pad_b = average_heigh + (average_heigh / 3)
y1 = ransac_model.predict_y(x1)
y2 = ransac_model.predict_y(x2)
refined_points = [(x1, y1 - pad_t), (x2, y2 - pad_t), (x2, y2 + pad_b), (x1, y1 + pad_b)]
refined_points = [(x + plate_x, y + plate_y) for (x, y) in refined_points]
return refined_points
return plate_points
def detect_motion_blur(image, ratio=2, band=None):
"""
Detect if an image is blurry due to movement, and therefore unreliable
to analyze. Uses the variance of the prewitt edge detectors in the horizontal and vertical directions.
Parameters
----------
image : ndarray
rgb or grayscale image.
ratio : float
Maximum ratio of edge strength variance in x- to y-directions. `threshold=2` means if
`x_edge.var()` / `y_edge.var() is > 2 OR < 0.5, the image is flagged as blurry do to
movement.
band : int
Band of image to use for analysis. Unspecified defaults to 0 (R in RGB).
Returns
-------
bool - does the image meet requirements?
"""
if band is None:
try:
analyze = image[:, :, 0].copy()
except:
analyze = image.copy()
pass
else:
analyze = image[:, :, band]
horiz = img_as_ubyte(filters.prewitt_v(analyze)).var()
vert = img_as_ubyte(filters.prewitt_h(analyze)).var()
test = horiz / vert
if test < ratio and test > 1/ratio:
out = True
else:
out = False
return(out)
def detect_motion_blur(image, ratio=2, band=None):
"""
Detect if an image is blurry due to movement, and therefore unreliable
to analyze. Uses the variance of the prewitt edge detectors in the horizontal and vertical directions.
Parameters
----------
image : ndarray
rgb or grayscale image.
ratio : float
Maximum ratio of edge strength variance in x- to y-directions. `threshold=2` means if
`x_edge.var()` / `y_edge.var() is > 2 OR < 0.5, the image is flagged as blurry do to
movement.
band : int
Band of image to use for analysis. Unspecified defaults to 0 (R in RGB).
Returns
-------
bool - does the image meet requirements?
"""
if band is None:
try:
analyze = image[:, :, 0].copy()
except:
analyze = image.copy()
pass
else:
analyze = image[:, :, band]
horiz = img_as_ubyte(filters.prewitt_v(analyze)).var()
vert = img_as_ubyte(filters.prewitt_h(analyze)).var()
test = horiz / vert
if test < ratio and test > 1 / ratio:
out = True
else:
out = False
return (out)
def getRegions(img_orig):
# sizes = [4, 6, 8, 10]
sizes = [4, 8, 12]
img_processed = (255 * np.absolute(filters.prewitt_v(img_orig))).astype(np.uint8)
img_aux = (255 * np.absolute(filters.prewitt_h(img_orig))).astype(np.uint8)
img_processed[img_aux > 10] = 0
regions_of_interest = []
for s in sizes:
img_processed = morphology.dilation(img_processed, morphology.square(s))
thresh = filters.threshold_otsu(img_processed)
img_processed = img_processed >= thresh
labels = measure.label(img_processed)
for region in measure.regionprops(labels):
minr, minc, maxr, maxc = region.bbox
ratio = float(maxr - minr) / float(maxc - minc)
area = region.area
if area > 1500 and area < 50000 and ratio < 1 and ratio > 0.2:
regions_of_interest.append(region)
return regions_of_interest
def test_vprewitt_horizontal():
"""Vertical prewitt on a horizontal edge should be zero."""
i, j = np.mgrid[-5:6, -5:6]
image = (i >= 0).astype(float)
result = filters.prewitt_v(image)
assert_allclose(result, 0)
def test_vprewitt_mask():
"""Vertical prewitt on a masked array should be zero."""
np.random.seed(0)
result = filters.prewitt_v(np.random.uniform(size=(10, 10)),
np.zeros((10, 10), bool))
assert_allclose(result, 0)
def test_vprewitt_zeros():
"""Vertical prewitt on an array of all zeros."""
result = filters.prewitt_v(np.zeros((10, 10)), np.ones((10, 10), bool))
assert_allclose(result, 0)
from skimage.filters import gaussian, gaussian_filter, laplace,prewitt from skimage.filters import prewitt_v,prewitt_h,scharr, wiener gauss=gaussian(gray0, sigma=5, multichannel=True) plt.imshow(gauss) gauss2=gaussian_filter(gray0, sigma=5, multichannel=True) plt.imshow(gauss2) lap=laplace(gray0,ksize=100) plt.imshow(lap) pre=prewitt(gray0, mask=None) plt.imshow(pre) pre_v=prewitt_v(gray0, mask=None) plt.imshow(pre_v) from skimage import filters edges2 = filters.roberts(gray0) plt.imshow(edges2) plt.imshow(scharr(gray0)) plt.imshow(threshold_mean(gray0)) plt.imshow(wiener(gray0)) ####################################### plt.imshow(img) plt.imshow(gray0) plt.imshow(image) ### TREES
from PIL import Image, ImageChops
import matplotlib.pyplot as plt
import numpy as np
import cv2
from skimage import data, io, filters, color, exposure
img1 = io.imread('../image/pic2.jpg', as_gray=True)
img2 = filters.sobel_v(img1)
img3 = filters.prewitt_v(img1)
img4 = filters.laplace(img1)
plt.figure('show image')
plt.subplot(2, 2, 1)
plt.imshow(img1, cmap='gray')
plt.title('original image')
plt.axis('off')
plt.subplot(2, 2, 2)
plt.imshow(img2, cmap='gray')
plt.title('sobel image')
plt.axis('off')
plt.subplot(2, 2, 3)
plt.imshow(img3, cmap='gray')
plt.title('prewitt image')
plt.axis('off')
plt.subplot(2, 2, 4)
plt.imshow(img4, cmap='gray')
plt.title('laplace image')
plt.axis('off')
def prewitt(self):
filteredImage = filters.prewitt_v(self.rawImage)
self.showImage(filteredImage)
self.rawImage = filteredImage
print("Prewitt")
def getFeatures(img, px, py, w, h):
img_window = img[py : py + h, px : px + w]
img_window = transform.resize(img_window, (window_height, window_width))
img_window = np.absolute(filters.prewitt_v(img_window))
return img_window.ravel()