72 lines
1.9 KiB
Python
72 lines
1.9 KiB
Python
import base64
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import cv2 as cv
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import numpy as np
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import matplotlib.pyplot as plt
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from skimage import measure, morphology, color, segmentation
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import io
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def get_projected_area(image, area_threshold, display_threshold):
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total_px = image.size
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total_mm = 60322.46
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output = []
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original = cv.imdecode(image, cv.IMREAD_UNCHANGED)
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original = cv.cvtColor(original, cv.COLOR_BGR2RGB)
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img = cv.cvtColor(original, cv.COLOR_BGR2GRAY)
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_retval, thresh_gray = cv.threshold(img, 200, 255, cv.THRESH_BINARY)
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img = morphology.area_closing(thresh_gray, area_threshold=area_threshold, connectivity=1)
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contours = measure.find_contours(array=img, level=100)
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fig, ax = plt.subplots()
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ax.imshow(original, cmap=plt.cm.gray, alpha=0.3)
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index = 1
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for contour in contours:
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area = calculate_area(contour)
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if calculate_area(contour) > display_threshold:
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ax.plot(contour[:, 1], contour[:, 0], linewidth=0.5, color='orangered')
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cX, cY = center_of_mass(contour)
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plt.text(cY, cX, index, color='black', fontsize=6)
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output.append((index, round(area / total_px * total_mm, 2)))
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# print(area, total_px)
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index += 1
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ax.axis('image')
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ax.set_xticks([])
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ax.set_yticks([])
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ax.margins(0)
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my_stringIObytes = io.BytesIO()
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plt.savefig(my_stringIObytes, format='png', dpi=500, bbox_inches='tight')
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my_stringIObytes.seek(0)
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image_arr = base64.b64encode(my_stringIObytes.read()).decode(encoding='utf-8')
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return image_arr, output
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def calculate_area(countour):
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c = np.expand_dims(countour.astype(np.float32), 1)
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c = cv.UMat(c)
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return cv.contourArea(c)
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def center_of_mass(X):
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x = X[:,0]
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y = X[:,1]
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g = (x[:-1]*y[1:] - x[1:]*y[:-1])
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A = 0.5*g.sum()
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cx = ((x[:-1] + x[1:])*g).sum()
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cy = ((y[:-1] + y[1:])*g).sum()
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return 1./(6*A)*np.array([cx,cy])
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