One of my friends gently asked me to help them on automatic inspection of PCBs (xray).
I used pytrack to investigate and report soldering failures (bad bubbles).
# -*- coding: utf-8 -*-
# 07.05.2017
# porsuk
from __future__ import division, unicode_literals, print_function # for compatibility with Python 2 and 3
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from pandas import DataFrame, Series # for convenience
from scipy import ndimage
from skimage import morphology, util, filters
import pims
import trackpy as tp
def crop(img):
"""
Crop the image to select the region of interest
"""
x_min = 20#745
x_max = -25#850
y_min = 50#225
y_max = -250#300
return img[y_min:y_max,x_min:x_max]
def rgb2gray(rgb):
r, g, b = rgb[:,:,0], rgb[:,:,1], rgb[:,:,2]
gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
return gray
def preprocess_pcb(img):
img=rgb2gray(img)
"""
Apply image processing functions to return a binary image
"""
# Crop the pictures as for raw images.
img = crop(img)
print(img)
# Apply thresholds
img = filters.threshold_adaptive(img, 57)
threshold = 0.9
idx = img > img.max() * threshold
idx2 = img < img.max() * threshold
img[idx] = 0
img[idx2] = 3
img = ndimage.binary_dilation(img)
img = ndimage.binary_dilation(img)
return util.img_as_int(img)
frames = pims.ImageSequence('*.jpg',process_func=preprocess_pcb)
for frame in frames:
f = tp.locate(frame, percentile=10,diameter=77, minmass= 3.0e+07,separation=80,threshold=1/30)
print('frame = ',f['frame'][0] )
print(f)
print('max bubble percentage: ', 20 * max(f['size']) / 250 )
plt.figure(f['frame'][0]) # make a new figure
tp.annotate(f,frame,plot_style={'markersize':35});
