Added projected area code for images

master
Your Name 2022-10-28 04:16:34 +00:00
parent 407a103913
commit 55110d6736
6 changed files with 80 additions and 0 deletions

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import cv2 as cv
import numpy as np
from skimage import measure, morphology, color, segmentation
import matplotlib.pyplot as plt
file = 'proj-area-3.jpg'
original = cv.imread(file)
original = cv.cvtColor(original, cv.COLOR_BGR2RGB)
img = original
img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
retval, thresh_gray = cv.threshold(img, 200, 255, cv.THRESH_BINARY)
def remove_dirt(image):
image = morphology.area_closing(image, area_threshold=250, connectivity=1)
# image = morphology.opening(image, morphology.square(5))
return image
def calculate_area(countour):
c = np.expand_dims(countour.astype(np.float32), 1)
c = cv.UMat(c)
return cv.contourArea(c)
def center_of_mass(X):
x = X[:,0]
y = X[:,1]
g = (x[:-1]*y[1:] - x[1:]*y[:-1])
A = 0.5*g.sum()
cx = ((x[:-1] + x[1:])*g).sum()
cy = ((y[:-1] + y[1:])*g).sum()
return 1./(6*A)*np.array([cx,cy])
img = remove_dirt(thresh_gray)
# alpha = 1 # Contrast control (1.0-3.0)
# beta = 1 # Brightness control (0-100)
# img = cv.convertScaleAbs(img, alpha=alpha, beta=beta)
# img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
# img = cv.medianBlur(img, 5)
# retval, thresh_gray = cv.threshold(img, 0, 255, cv.THRESH_BINARY_INV | cv.THRESH_OTSU)
# img = cv.medianBlur(img, 5)
# img = cv.adaptiveThreshold(img, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY_INV, 51, 15)
contours = measure.find_contours(array=img, level=100)
fig, ax = plt.subplots()
ax.imshow(img, cmap=plt.cm.gray, alpha=0)
index = 1
for contour in contours:
if calculate_area(contour) > 300:
ax.plot(contour[:, 1], contour[:, 0], linewidth=0.5, color='orangered')
cX, cY = center_of_mass(contour)
plt.text(cY, cX, index, color='black', fontsize=6)
index += 1
ax.axis('image')
ax.set_xticks([])
ax.set_yticks([])
plt.savefig('output.png', dpi=300)
# cv.imwrite('proj-area-1-processed.jpg', remove_dirt(img))