In [3]:
img = get_img()
plt.imshow(cv2.imread(img))
Out[3]:
<matplotlib.image.AxesImage at 0x1ef13fe7ca0>
import os
import numpy as np
import random
import cv2
from matplotlib import pyplot as plt
data_img = os.listdir('../data/img')
def get_img():
return "../data/img/"+random.choice(data_img)
img = get_img()
plt.imshow(cv2.imread(img))
<matplotlib.image.AxesImage at 0x1ef13fe7ca0>
def methode1(img=img):
#https://docs.opencv.org/master/d3/db4/tutorial_py_watershed.html
img = cv2.imread(img)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
# noise removal
kernel = np.ones((3,3),np.uint8)
opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 2)
# sure background area
sure_bg = cv2.dilate(opening,kernel,iterations=3)
# Finding sure foreground area
dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5)
ret, sure_fg = cv2.threshold(dist_transform,0.7*dist_transform.max(),255,0)
# Finding unknown region
sure_fg = np.uint8(sure_fg)
unknown = cv2.subtract(sure_bg,sure_fg)
# Marker labelling
ret, markers = cv2.connectedComponents(sure_fg)
# Add one to all labels so that sure background is not 0, but 1
markers = markers+1
# Now, mark the region of unknown with zero
markers[unknown==255] = 0
markers = cv2.watershed(img,markers)
img[markers == -1] = [255,0,0]
plt.imshow(img)
from skimage.feature import peak_local_max
from skimage.segmentation import watershed
from scipy import ndimage
def methode2(img=img):
# Load in image, convert to gray scale, and Otsu's threshold
image = cv2.imread(img)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
# Compute Euclidean distance from every binary pixel
# to the nearest zero pixel then find peaks
distance_map = ndimage.distance_transform_edt(thresh)
local_max = peak_local_max(distance_map, indices=False, min_distance=20, 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
for label in np.unique(labels):
if label == 0:
continue
# Create a mask
mask = np.zeros(gray.shape, dtype="uint8")
mask[labels == label] = 255
# Find contours and determine contour area
cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
c = max(cnts, key=cv2.contourArea)
area = cv2.contourArea(c)
total_area += area
cv2.drawContours(image, [c], -1, (36,255,12), 4)
plt.imshow(image)
def methode3(img=img):
# Converting the image to grayscale.
frame = cv2.imread(img)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Using the Canny filter to get contours
edges = cv2.Canny(gray, 20, 30)
# Using the Canny filter with different parameters
edges_high_thresh = cv2.Canny(gray, 60, 120)
# Stacking the images to print them together
# For comparison
images = np.hstack((gray, edges, edges_high_thresh))
# Display the resulting frame
plt.imshow(images)
methode1(img)
methode2(img)
methode3(img)
image = cv2.imread(img)
original = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (3, 3), 0)
canny = cv2.Canny(blurred, 120, 255, 1)
kernel = np.ones((5,5),np.uint8)
dilate = cv2.dilate(canny, kernel, iterations=1)
# Find contours
cnts = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
# Iterate thorugh contours and filter for ROI
image_number = 0
for c in cnts:
x,y,w,h = cv2.boundingRect(c)
cv2.rectangle(image, (x, y), (x + w, y + h), (36,255,12), 2)
ROI = original[y:y+h, x:x+w]
#cv2.imwrite("ROI_{}.png".format(image_number), ROI)
image_number += 1
plt.imshow(canny)
<matplotlib.image.AxesImage at 0x1ef16a8e1f0>
plt.imshow(image)
<matplotlib.image.AxesImage at 0x1ef16ad4ca0>
