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process_kvs_stream.py
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process_kvs_stream.py
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import cv2
import boto3
import os
# Part 1 ROI
part_1_start_x, part_1_start_y, part_1_end_x, part_1_end_y = [65, 270, 172, 352]
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
def process_frame(frame):
gray = cv2.cvtColor(frame, 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_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)
STREAM_NAME="CoolStream"
#video_client = boto3.client('kinesis-video-media',endpoint_url='https://s-5xxxxxx4.kinesisvideo.eu-west-1.amazonaws.com',region_name='eu-west-1')
kinesis_client = boto3.client('kinesisvideo',
aws_access_key_id=os.environ['AWS_ACCESS_KEY_ID'],
aws_secret_access_key=os.environ['AWS_SECRET_ACCESS_KEY'],
region_name=os.environ['AWS_DEFAULT_REGION'])
# # Normal way
# response = kinesis_client.get_data_endpoint(StreamARN='arn:aws:kinesisvideo:eu-west-1:441324442946:stream/CoolStream/1566485232509',APIName='GET_MEDIA')
# HLS way
endpoint = kinesis_client.get_data_endpoint(
APIName="GET_HLS_STREAMING_SESSION_URL",
StreamName=STREAM_NAME
)['DataEndpoint']
# video_client = boto3.client('kinesis-video-media',endpoint_url=response['DataEndpoint'])
#
# stream = video_client.get_media(
# StreamARN='arn:aws:kinesisvideo:eu-west-1:441324442946:stream/CoolStream/1566485232509',
# StartSelector={'StartSelectorType': 'EARLIEST'})
#
# print (stream)
#
# streamingBody = stream["Payload"]
# streamingBody = stream["Payload"]
# datafeed = streamingBody.read(40000)
#
#s3 = boto3.client('s3')
# # Grab the HLS Stream URL from the endpoint
kvam = boto3.client("kinesis-video-archived-media", endpoint_url=endpoint)
url = kvam.get_hls_streaming_session_url(
StreamName=STREAM_NAME,
PlaybackMode="LIVE"
)['HLSStreamingSessionURL']
vcap = cv2.VideoCapture(url)
while(True):
# Capture frame-by-frame
ret, frame = vcap.read()
if frame is not None:
print("Frame is good")
else:
print("Frame is None")
break
vcap.release()
print ("End of stream")