How to detect rectangular elements in an image using Python

Question

I found a lot of questions regarding finding “things” in images using openCV et al. In Python, but so far I have not been able to put them together to reliably solve my problem.

I'm trying to use computer vision to count the tiny pieces of electronics on the surface. The idea is that I dump the details onto solid colored paper, click the image, and the software tells me how many items are in it.

“things” differ from one image to another, but will always be the same in any image. It seems I can manually adjust the settings for things like hue / saturation for a specific part, but every time a change to a new part requires a change in settings.

My current, semi-functional code is posted below:

import imutils
import numpy
import cv2
import sys

def part_area(contours, round=10):
    """Finds the mode of the contour area.  The idea is that most of the parts in an image will be separated and that
    finding the most common area in the list of areas should provide a reasonable value to approximate by.  The areas
    are rounded to the nearest multiple of 200 to reduce the list of options."""
    # Start with a list of all of the areas for the provided contours.
    areas = [cv2.contourArea(contour) for contour in contours]
    # Determine a threshold for the minimum amount of area as 1% of the overall range.
    threshold = (max(areas) - min(areas)) / 100
    # Trim the list of areas down to only those that exceed the threshold.
    thresholded = [area for area in areas if area > threshold]
    # Round the areas to the nearest value set by the round argument.
    rounded = [int((area + (round / 2)) / round) * round for area in thresholded]
    # Remove any areas that rounded down to zero.
    cleaned = [area for area in rounded if area != 0]
    # Count the areas with the same values.
    counts = {}
    for area in cleaned:
        if area not in counts:
            counts[area] = 0
        counts[area] += 1
    # Reduce the areas down to only those that are in groups of three or more with the same area.
    above = []
    for area, count in counts.iteritems():
        if count > 2:
            for _ in range(count):
                above.append(area)
    # Take the mean of the areas as the average part size.
    average = sum(above) / len(above)
    return average

def find_hue_mode(hsv):
    """Given an HSV image as an input, compute the mode of the list of hue values to find the most common hue in the
    image.  This is used to determine the center for the background color filter."""
    pixels = {}
    for row in hsv:
        for pixel in row:
            hue = pixel[0]
            if hue not in pixels:
                pixels[hue] = 0
            pixels[hue] += 1
    counts = sorted(pixels.keys(), key=lambda key: pixels[key], reverse=True)
    return counts[0]


if __name__ == "__main__":
    # load the image and resize it to a smaller factor so that the shapes can be approximated better
    image = cv2.imread(sys.argv[1])

    # define range of blue color in HSV
    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
    center = find_hue_mode(hsv)
    print 'Center Hue:', center

    lower = numpy.array([center - 10, 50, 50])
    upper = numpy.array([center + 10, 255, 255])
    # Threshold the HSV image to get only blue colors
    mask = cv2.inRange(hsv, lower, upper)
    inverted = cv2.bitwise_not(mask)

    blurred = cv2.GaussianBlur(inverted, (5, 5), 0)
    edged = cv2.Canny(blurred, 50, 100)
    dilated = cv2.dilate(edged, None, iterations=1)
    eroded = cv2.erode(dilated, None, iterations=1)

    # find contours in the thresholded image and initialize the shape detector
    contours = cv2.findContours(eroded.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    contours = contours[0] if imutils.is_cv2() else contours[1]

    # Compute the area for a single part to use when setting the threshold and calculating the number of parts within
    # a contour area.
    part_area = part_area(contours)
    # The threshold for a part area - can't be too much smaller than the part itself.
    threshold = part_area * 0.5

    part_count = 0
    for contour in contours:
        if cv2.contourArea(contour) < threshold:
            continue

        # Sometimes parts are close enough together that they become one in the image.  To battle this, the total area
        # of the contour is divided by the area of a part (derived earlier).
        part_count += int((cv2.contourArea(contour) / part_area) + 0.1)  # this 0.1 "rounds up" slightly and was determined empirically

        # Draw an approximate contour around each detected part to give the user an idea of what the tool has computed.
        epsilon = 0.1 * cv2.arcLength(contour, True)
        approx = cv2.approxPolyDP(contour, epsilon, True)
        cv2.drawContours(image, [approx], -1, (0, 255, 0), 2)

    # Print the part count and show off the processed image.
    print 'Part Count:', part_count
    cv2.imshow("Image", image)
    cv2.waitKey(0)

Here is an example of the type of input image I'm using: or this:

And currently I am getting the following results:

The results clearly show that the script is having difficulty defining certain parts, and this is the true Achilles' heel, it seems when the parts touch each other.

So, my question / task: what can I do to increase the reliability of this script?

The script must be integrated into an existing Python tool, so I'm looking for a solution using Python. The solution should not be pure Python, as I am ready to install any third-party libraries.

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