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track_pool_from_images_send_to_dynamodb.py
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track_pool_from_images_send_to_dynamodb.py
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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__":
dynamodb = boto3.resource('dynamodb', region_name='eu-west-1')
table = dynamodb.Table('PoolTracker')
job_ID = "protoshape_first_video"
frame_ID = 3
response = table.put_item(
Item={
'job_ID': job_ID,
'frame_ID': frame_ID,
'pool_features': {
'area': decimal.Decimal(34.0),
'mean_intensity': decimal.Decimal(0.0),
'radius': decimal.Decimal(23.5)
}
}
)
print(response)
try:
response = table.get_item(
Key={
'job_ID': job_ID,
'frame_ID': frame_ID,
}
)
except ClientError as e:
print(e.response['Error']['Message'])
else:
item = response['Item']
print("GetItem succeeded:")
print(item)
# df = pd.DataFrame(columns=['Frame_Index', 'Area', 'Mean', 'Radius'])
#
# 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 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)
#
# # Part 1 ROI
# part_1_start_x, part_1_start_y, part_1_end_x, part_1_end_y = [65, 270, 172, 352]
#
# # 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_1_start_x, part_1_end_x)
# height = calculateIntersection(contour_start_y, contour_end_y, part_1_start_y, part_1_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)
# 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)
#
# #print (df)
# df.to_csv('./out/protoshape/interim/pool_data_part_1.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)