File size: 3,487 Bytes
aecbb99 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 | # -*- coding: utf-8 -*-
"""
Created on Sun May 17 14:53:24 2020
@author: serdarhelli
"""
import cv2
import numpy as np
from skimage.feature import peak_local_max
from skimage.morphology import watershed
from scipy import ndimage
import matplotlib.pyplot as plt
from imutils import perspective
from imutils import contours
from scipy.spatial import distance as dist
def midpoint(ptA, ptB):
return ((ptA[0] + ptB[0]) * 0.5, (ptA[1] + ptB[1]) * 0.5)
# Load in image, convert to gray scale, and Otsu's threshold
kernel =( np.ones((3,3), dtype=np.float32))
image = cv2.imread('pred_image')
image=cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
erosion = cv2.erode(thresh,kernel,iterations =5)
gradient = cv2.morphologyEx(erosion, cv2.MORPH_GRADIENT, kernel)
# Compute Euclidean distance from every binary pixel
# to the nearest zero pixel then find peaks
distance_map = ndimage.distance_transform_edt(erosion)
distance_map = ndimage.maximum_filter(distance_map, size=15, mode='constant')
local_max = peak_local_max(distance_map, indices=False, min_distance=40, labels=thresh)
# Perform connected component analysis then apply Watershed
markers = ndimage.label(local_max, structure=np.ones((3, 3)))[0]
labels = watershed(-distance_map, markers, mask=thresh)
# Iterate through unique labels
total_area = 0
a=np.unique(labels)
area=np.zeros(len(a))
for label in a:
if label == 0:
continue
# Create a mask
mask = np.zeros(thresh.shape, dtype="uint8")
mask[labels == label] = 255
# Find contours and determine contour area
cnts,hieararch = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0]
(x,y),radius = cv2.minEnclosingCircle(cnts)
rect = cv2.minAreaRect(cnts)
box = cv2.boxPoints(rect)
box = np.array(box, dtype="int")
box = perspective.order_points(box)
color1 = (list(np.random.choice(range(256), size=3)))
color =[int(color1[0]), int(color1[1]), int(color1[2])]
cv2.drawContours(image,[box.astype("int")],0,color,2)
(tl,tr,br,bl)=box
(tltrX,tltrY)=midpoint(tl,tr)
(blbrX,blbrY)=midpoint(bl,br)
# compute the midpoint between the top-left and top-right points,
# followed by the midpoint between the top-righ and bottom-right
(tlblX,tlblY)=midpoint(tl,bl)
(trbrX,trbrY)=midpoint(tr,br)
# draw the midpoints on the image
cv2.circle(image, (int(tltrX), int(tltrY)), 5, (255, 0, 0), -1)
cv2.circle(image, (int(blbrX), int(blbrY)), 5, (255, 0, 0), -1)
cv2.circle(image, (int(tlblX), int(tlblY)), 5, (255, 0, 0), -1)
cv2.circle(image, (int(trbrX), int(trbrY)), 5, (255, 0, 0), -1)
cv2.line(image, (int(tltrX), int(tltrY)), (int(blbrX), int(blbrY)),color, 2)
cv2.line(image, (int(tlblX), int(tlblY)), (int(trbrX), int(trbrY)),color, 2)
dA = dist.euclidean((tltrX, tltrY), (blbrX, blbrY))
dB = dist.euclidean((tlblX, tlblY), (trbrX, trbrY))
##your pixel size of your x_ray image
pixelsPerMetric=0.096
dimA = dA * pixelsPerMetric
dimB = dB *pixelsPerMetric
cv2.putText(image, "{:.1f}mm".format(dimA),(int(tltrX - 15), int(tltrY - 10)), cv2.FONT_HERSHEY_SIMPLEX,0.65, color, 2)
cv2.putText(image, "{:.1f}mm".format(dimB),(int(trbrX + 10), int(trbrY)), cv2.FONT_HERSHEY_SIMPLEX,0.65, color, 2)
cv2.imwrite('water_pred_image',image)
cv2.waitKey()
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