pool-tracker-pro/track_pool_from_images.py at master · dnstanciu/pool-tracker-pro · GitHub

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import cv2
import numpy as np
from imutils import contours
import imutils
import glob
import os
import pandas as pd
import boto3
from botocore.exceptions import ClientError
import decimal

def calculateIntersection(a0, a1, b0, b1):
    """
    Calculate intersection between interval.
    Reference: https://stackoverflow.com/a/48537479
    """
    if a0 >= b0 and a1 <= b1: # Contained
        intersection = a1 - a0
    elif a0 < b0 and a1 > b1: # Contains
        intersection = b1 - b0
    elif a0 < b0 and a1 > b0: # Intersects right
        intersection = a1 - b0
    elif a1 > b1 and a0 < b1: # Intersects left
        intersection = b1 - a0
    else: # No intersection (either side)
        intersection = 0

    return intersection

if __name__ == "__main__":

    # Parts Regions of Interest
    parts = [
        # Format: part_start_x, part_start_y, part_end_x, part_end_y
        [65, 270, 172, 352],    # Part 1
        [102, 233, 150, 278],   # Part 2
        [103, 191, 159, 245],   # Part 3
        [111, 148, 162, 195],   # Part 4
        [115, 107, 167, 153],   # Part 5
        [118, 56, 170, 106],    # Part 6
        [164, 279, 226, 338],   # Part 7
        [161, 228, 225, 289],   # Part 8
        [166, 180, 231, 245],   # Part 9
        [169, 137, 230, 197],   # Part 10
        [170, 90, 232, 148],    # Part 11
        [174, 48, 233, 99],     # Part 12
    ]

    image_file_list = []
    # for filename in glob.glob('./out/protoshape/frames_test_multiple/*.png'):
    for filename in glob.glob('./out/protoshape/frames/*.png'):
        image_file_list.append(filename)

    image_file_list.sort(key=lambda f: int(''.join(filter(str.isdigit, f))))

    for index, part in enumerate(parts):
        df = pd.DataFrame(columns=['Frame_Index', 'Area', 'Mean', 'Radius'])
        (part_start_x, part_start_y, part_end_x, part_end_y) = part
        for image_file_path in image_file_list:
            print ("Processing: " + image_file_path)
            # Load an color image in grayscale
            #image = cv2.imread('./out/protoshape/frames/frame_001521.png')
            image = cv2.imread(image_file_path)

            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

            # TODO convert RGB to HSV for better light sensitivity
            # hsv1 = cv2.cvtColor(frame1, cv2.COLOR_BGR2HSV)
            # h, s, v1 = cv2.split(hsv1)


            # Blur to remove noise (radius must be ODD)
            gray = cv2.GaussianBlur(gray, (5, 5), 0) # 21,21
            # (minVal, maxVal, minLoc, maxLoc) = cv2.minMaxLoc(gray)
            # image = orig.copy()
            # cv2.circle(image, maxLoc, 21, (255, 0, 0), 2)

            # threshold the image to reveal light regions in the blurred image
            thresh = cv2.threshold(gray, 210, 255, cv2.THRESH_BINARY)[1]
            # display the results of the naive attempt
            #cv2.imshow("Naive", image)

            # perform a series of erosions and dilations to remove
            # any small blobs of noise from the thresholded image
            # thresh = cv2.erode(thresh, None, iterations=2)
            # thresh = cv2.dilate(thresh, None, iterations=4)
            thresh = cv2.erode(thresh, None, iterations=1)
            thresh = cv2.dilate(thresh, None, iterations=1)

            # cv2.imwrite('./out/protoshape/interim/denoised2.png', thresh)


            # Contours
            cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

            cnts = imutils.grab_contours(cnts)
            if len(cnts) != 0:
                #Intersecting contours
                intersecting_contours = []
                for contour in cnts:
                    contour_start_x, contour_start_y, w, h = cv2.boundingRect(contour)
                    contour_end_x = contour_start_x + w
                    contour_end_y = contour_start_y + h

                    width = calculateIntersection(contour_start_x, contour_end_x, part_start_x, part_end_x)
                    height = calculateIntersection(contour_start_y, contour_end_y, part_start_y, part_end_y)

                    area = width * height

                    if area > 400:
                        intersecting_contours.append(contour)

                if len(intersecting_contours) != 0:
                    # sorted_intersecting_contours = imutils.contours.sort_contours(intersecting_contours)[0]
                    sorted_intersecting_contours = sorted(intersecting_contours, key=cv2.contourArea, reverse=True)

                    largest_contour = sorted_intersecting_contours[0]

                    # Get area
                    area = cv2.contourArea(largest_contour)

                    # Get average intensity
                    contour_mask = np.zeros(gray.shape, np.uint8)
                    cv2.drawContours(contour_mask, largest_contour, -1, 255, -1) # draw filled contours
                    mean = cv2.mean(gray, mask=contour_mask)

                    # Get radius length TODO: or use perimeter aka length or both?
                    ((cX, cY), radius) = cv2.minEnclosingCircle(largest_contour)

                    df = df.append({'Frame_Index': os.path.basename(image_file_path), 'Area': area, 'Mean': mean[0], 'Radius': radius}, ignore_index=True)
                else:
                    df = df.append({'Frame_Index': os.path.basename(image_file_path), 'Area': 0, 'Mean': 0, 'Radius': 0}, ignore_index=True)
            else:
                df = df.append({'Frame_Index': os.path.basename(image_file_path), 'Area': 0, 'Mean': 0, 'Radius': 0}, ignore_index=True)

        df.to_csv('./out/protoshape/interim/new_pool_data_part_' + str(index) + '.csv')

    #cv2.drawContours(image, [largest_contour], -1, (0, 0, 255), 1)
    #cv2.rectangle(image, (part_1_start_x, part_1_start_y), (part_1_end_x, part_1_end_y), (0, 255, 0))

    #cv2.imwrite('./out/protoshape/interim/contour.png', image)

    #
    #
    #
    #     cnts = imutils.contours.sort_contours(cnts)[0]
    #     cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:5]
    #
    #
    #     # Just get the largest contour
    #     cnt = cnts[0]
    #     area = cv2.contourArea(cnt)
    #
    #     cv2.drawContours(image, [cnt], -1, (0, 0, 255), 2)
    #     # ((cX, cY), radius) = cv2.minEnclosingCircle(cnt)
    #     # cv2.circle(image, (int(cX), int(cY)), int(radius), (0, 0, 255), 3)
    #
    #     cv2.putText(image, str(area), (70, 70), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
    #
    #     # Just get the largest contour
    #     cnt = cnts[2]
    #     area = cv2.contourArea(cnt)
    #
    #     cv2.drawContours(image, [cnt], -1, (0, 0, 255), 2)
    #     # ((cX, cY), radius) = cv2.minEnclosingCircle(cnt)
    #     # cv2.circle(image, (int(cX), int(cY)), int(radius), (0, 0, 255), 3)
    #
    #     # cv2.putText(image, str(area), (70, 70), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
    #
    #     # Just get the largest contour
    #     cnt = cnts[3]
    #     area = cv2.contourArea(cnt)
    #
    #     cv2.drawContours(image, [cnt], -1, (0, 0, 255), 2)
    #     # ((cX, cY), radius) = cv2.minEnclosingCircle(cnt)
    #     # cv2.circle(image, (int(cX), int(cY)), int(radius), (0, 0, 255), 3)
    #
    #     # cv2.putText(image, str(area), (70, 70), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
    #
    # # Write frame
    # cv2.imwrite('./out/protoshape/MTC.png', image)