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Blue White colony screening using cv2 in Python

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1. Suggest hypotheses about the causes of observed phenomena

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# useage: python bacteria_counter.py -i [imagefile] -o [imagefile]
# import the necessary packages
import numpy as np
import argparse
import imutils
import cv2
  
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
    help="path to the input image")
ap.add_argument("-o", "--output", required=True,
    help="path to the output image")
args = vars(ap.parse_args())
  
# dict to count colonies
counter = {}
 
# load the image
image_orig = cv2.imread(args["image"])
height_orig, width_orig = image_orig.shape[:2]
 
# output image with contours
image_contours = image_orig.copy()
 
# DETECTING BLUE AND WHITE COLONIES
colors = ['blue', 'white']
for color in colors:
 
    # copy of original image
    image_to_process = image_orig.copy()
 
    # initializes counter
    counter[color] = 0
 
    # define NumPy arrays of color boundaries (GBR vectors)
    if color == 'blue':
        lower = np.array([ 60, 100,  20])
        upper = np.array([170, 180, 150])
    elif color == 'white':
        # invert image colors
        image_to_process = (255-image_to_process)
        lower = np.array([ 50,  50,  40])
        upper = np.array([100, 120,  80])
 
    # find the colors within the specified boundaries
    image_mask = cv2.inRange(image_to_process, lower, upper)
    # apply the mask
    image_res = cv2.bitwise_and(image_to_process, image_to_process, mask = image_mask)
 
    ## load the image, convert it to grayscale, and blur it slightly
    image_gray = cv2.cvtColor(image_res, cv2.COLOR_BGR2GRAY)
    image_gray = cv2.GaussianBlur(image_gray, (5, 5), 0)
 
    # perform edge detection, then perform a dilation + erosion to close gaps in between object edges
    image_edged = cv2.Canny(image_gray, 50, 100)
    image_edged = cv2.dilate(image_edged, None, iterations=1)
    image_edged = cv2.erode(image_edged, None, iterations=1)
 
    # find contours in the edge map
    cnts = cv2.findContours(image_edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    cnts = cnts[0] if imutils.is_cv2() else cnts[1]
 
    # loop over the contours individually
    for c in cnts:
         
        # if the contour is not sufficiently large, ignore it
        if cv2.contourArea(c) < 5:
            continue
         
        # compute the Convex Hull of the contour
        hull = cv2.convexHull(c)
        if color == 'blue':
            # prints contours in red color
            cv2.drawContours(image_contours,[hull],0,(0,0,255),1)
        elif color == 'white':
            # prints contours in green color
            cv2.drawContours(image_contours,[hull],0,(0,255,0),1)
 
        counter[color] += 1
        #cv2.putText(image_contours, "{:.0f}".format(cv2.contourArea(c)), (int(hull[0][0][0]), int(hull[0][0][1])), cv2.FONT_HERSHEY_SIMPLEX, 0.65, (255, 255, 255), 2)
 
    # Print the number of colonies of each color
    print("{} {} colonies".format(counter[color],color))
 
# Writes the output image
cv2.imwrite(args["output"],image_contours)

2. Assess assumptions on which statistical inference will be based

if (isAwesome){
  return true
}

3. Support the selection of appropriate statistical tools and techniques

4. Provide a basis for further data collection through surveys or experiments

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