Droplet Detection

You can download this example as jupyter notebook

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2
import glob

from ipywidgets import interact

get_ipython().magic('matplotlib inline')
plt.rcParams['figure.figsize'] = (20.0, 15.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
plt.rcParams['axes.titlesize'] = 8

%matplotlib inline

Detecting Background Pixels

bg = cv2.imread('./samples/bg_nolbl.jpg', 0)
droplet = cv2.imread('./samples/full_droplet_1.jpg', 0)

# crop the top
bg = bg[30:, :]
droplet = droplet[30:, :]

droplet = cv2.blur(droplet, ksize=(9,9))
bg = cv2.blur(bg, ksize=(3,3))

fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(20, 20))

ax1.imshow(droplet)
ax2.imshow(bg)
ax3.hist(droplet.ravel(), 256, [0, 256])
ax4.hist(bg.ravel(), 256, [0, 256])

plt.show()

Apply thresholds

low_thresh = 100
high_thresh = 250

def apply_thresh(low, high):
    ret, binary = cv2.threshold(droplet, low, high, 0)
    plt.figure(figsize=(20, 20))
    plt.imshow(binary, cmap='gray')
    plt.title('Result of binary thresholding')
    plt.show()

interact(apply_thresh, low=(0,250), high=(0, 255));

Finding Contours

low_thresh = 70
high_thresh = 255

ret, binary = cv2.threshold(droplet, low_thresh, high_thresh, 0)

im2, contours, hierarchy = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
segmented_img = np.dstack((droplet, droplet, droplet))
cv2.drawContours(segmented_img, contours, -1, (0, 255, 0), 3)

plt.figure(figsize=(20, 20))
plt.title('Contours on original image')
plt.imshow(segmented_img)
plt.show()

Fitting an Ellipse

final = np.dstack((droplet, droplet, droplet))

for cnt in contours:
    if cnt.shape[0] > 5:
        an_ellipse = cv2.fitEllipse(cnt)
        cv2.ellipse(final, an_ellipse, (0, 0, 255), 3)

plt.figure(figsize=(20, 20))
plt.title('Ellipse')
plt.imshow(final)
plt.show()