63 lines
1.7 KiB
Python
63 lines
1.7 KiB
Python
def remove_dirt(image):
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image = morphology.area_closing(image, area_threshold=250, connectivity=1)
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# image = morphology.opening(image, morphology.square(5))
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return image
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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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img = remove_dirt(thresh_gray)
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def rg_ratio_normalize(imgarr):
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# set max & min to most extreme values,
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# work up & down respectively from there
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tmin = MAX_TEMP
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tmax = 0
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imgnew = imgarr
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for i in range(len(imgarr)):
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for j in range(len(imgarr[i])):
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px = imgarr[i][j]
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r_norm = normalization_func(px[0])
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g_norm = normalization_func(px[1])
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# apply camera calibration func
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temp_C = pyrometry_calibration_formula(g_norm, r_norm)
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# remove pixels outside calibration range
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if MAX_TEMP != None and temp_C > MAX_TEMP or MIN_TEMP != None and temp_C < MIN_TEMP:
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temp_C = 0
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# update min & max
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if temp_C < tmin and temp_C >= 0:
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tmin = temp_C
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if temp_C > tmax:
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tmax = temp_C
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imgnew[i][j] = [temp_C, temp_C, temp_C]
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return imgnew, tmin, tmax
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def pyrometry_calibration_formula(i_ng, i_nr):
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return 362.73 * math.log10(
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(i_ng/i_nr) ** 3
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) + 2186.7 * math.log10(
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(i_ng/i_nr) ** 2
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) + 4466.5 * math.log10(
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(i_ng / i_nr)
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) + 3753.5
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