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detct.py
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detct.py
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import numpy as np
import cv2
import math
from blob import Blob
# Load Video
cap = cv2.VideoCapture('video/1.mp4')
# Define functions
def distance(start, end):
x = start[0] - end[0]
y = start[1] - end[1]
return math.sqrt((x*x)+(y*y))
def theta(start, end):
x = start[0] - end[0]
y = start[1] - end[1]
return math.atan(y/x)
# Trackbar requires an function input
def nothing(x):
pass
# Track bars
blank = np.zeros((200, 400), dtype=np.uint8)
instruction1 = "Please read readme.md"
instruction2 = "before tweaking the"
instruction3 = "values below"
cv2.putText(blank, instruction1 ,(10, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
cv2.putText(blank, instruction2,(10, 90), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
cv2.putText(blank, instruction3 ,(10, 130), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
cv2.namedWindow('Output')
cv2.namedWindow('Controller')
cv2.createTrackbar('BGR - HSV ', 'Controller',1,1,nothing)
cv2.createTrackbar('BGR Threshold ', 'Controller',4,10,nothing)
cv2.createTrackbar('HSV Threshold ', 'Controller',9,10,nothing)
cv2.createTrackbar('Stablize Display Count ', 'Controller',0,1,nothing)
cv2.createTrackbar('Car Count on Average ', 'Controller',5,10,nothing)
cv2.createTrackbar('Resize Value ', 'Controller',7,10,nothing)
cv2.createTrackbar('Headlight Min Area in Pixels ', 'Controller',5,10,nothing)
cv2.createTrackbar('Show Blob Detected ', 'Controller',0,1,nothing)
cv2.createTrackbar('Show Cars ', 'Controller',1,1,nothing)
cv2.createTrackbar('Car Direction Vertical - Horizontal ', 'Controller',0,1,nothing)
cv2.createTrackbar('Headlight max horizontal distance', 'Controller',5,10,nothing)
cv2.createTrackbar('Headlight max vertical distance ', 'Controller',5,10,nothing)
cv2.createTrackbar('Camera FPS * 10 ', 'Controller',3,5,nothing)
# Initialize Variable
blobs = []
index = 0 # Index for blobs
displayCount = 0 # Count Frames
displayText = 0 # Car Count Display
carCount = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] # Car count per frame, up to 10 frames
# Start video
while (cap.isOpened()):
ret, frame = cap.read()
if ret:
BGR_HSV = cv2.getTrackbarPos('BGR - HSV ', 'Controller')
BGRThreshold = (cv2.getTrackbarPos('BGR Threshold ', 'Controller') * 10) + 155
HSVThreshold = cv2.getTrackbarPos('HSV Threshold ', 'Controller') + 245
toggleStableCount = cv2.getTrackbarPos('Stablize Display Count ', 'Controller')
stableCountValue = cv2.getTrackbarPos('Car Count on Average ', 'Controller')
resizeValue = 11 - cv2.getTrackbarPos('Resize Value ', 'Controller')
blobMinSize = (cv2.getTrackbarPos('Headlight Min Area in Pixels ', 'Controller') * 10) + 1
showBlob = cv2.getTrackbarPos('Show Blob Detected ', 'Controller')
showCar = cv2.getTrackbarPos('Show Cars ', 'Controller')
carDirection = cv2.getTrackbarPos('Car Direction Vertical - Horizontal ', 'Controller')
carGroupX = cv2.getTrackbarPos('Headlight max horizontal distance', 'Controller')
carGroupY = cv2.getTrackbarPos('Headlight max vertical distance ', 'Controller')
camFPS = (6 - cv2.getTrackbarPos('Camera FPS * 10 ', 'Controller')) * 10
# Resize the video
resized = cv2.resize(frame,(int(frame.shape[1]/resizeValue), int(frame.shape[0]/resizeValue)), fx=0.5, fy=0.5, interpolation=cv2.INTER_LINEAR)
####### HSV or BGR #######
if BGR_HSV:
# HSV alternative code
hsv = cv2.cvtColor(resized, cv2.COLOR_BGR2HSV)
# Grab the V layer of HSV
v = hsv[:,:,2]
# Set threshold for V value at 254, max values at 255 (python uses 0 - 255 instead of 0 - 100)
_, t = cv2.threshold(v, HSVThreshold, 255, cv2.THRESH_BINARY)
contours, hierarchy = cv2.findContours(t, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
else:
# BGR alternative code
gray = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)
blur = cv2.blur(gray, (5,5))
_, t = cv2.threshold(blur, BGRThreshold, 255, cv2.THRESH_BINARY)
contours, hierarchy = cv2.findContours(t, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
####### Remove Bad Blob #######
badBlob = []
for countCnt, cnt in enumerate(contours, start=0):
# Get min and max XY coordinates
# Contours has a weird structure, max at axis = 1 is to extract the list from the structure
low = np.min(np.max(cnt,axis=1),axis=0)
high = np.max(np.max(cnt,axis=1),axis=0)
# Get length and height
x = high[0] - low[0]
y = high[1] - low[1]
# Removing blob smaller than specific sizes
if cv2.contourArea(cnt) < blobMinSize:
badBlob.append(countCnt)
contours = np.delete(contours, badBlob)
####### Update Blob movement #######
missingBlobs = []
potentialCar = []
for countBlob, blob in enumerate(blobs, start=0):
# Check previous frame blob saved with new frame blob
for countCnt, cnt in enumerate(contours, start=0):
isUpdated = False
# Check if area moved only by small range
low = np.min(np.max(cnt,axis=1),axis=0)
high = np.max(np.max(cnt,axis=1),axis=0)
# Get the center of blob
centerCnt = [(high[0]+low[0])/2, (high[1]+low[1])/2]
# Check is blob is in range defined by FPS
if (abs(centerCnt[0]-blob.center[0]) > camFPS or
abs(centerCnt[1]-blob.center[1]) > camFPS ):
continue
# Check if area is similar
areaCnt = cv2.contourArea(cnt)
# Check the difference in area using ratio
if ((blob.area/areaCnt) >= 1.2 or
(blob.area/areaCnt) <= 0.8 ):
continue
# If the blob survived all the checks above, update its new position
blob.update(cnt, low, high, centerCnt, areaCnt)
isUpdated = True
# Interested blob is found, the loop can be broke
break
# If the blob is not missing
if isUpdated:
# If it existed for few frames, it can potentially be a car
if blob.existed >= 3:
if blob.existed >= 100:
if distance(blob.origin, blob.center) < (max(resized.shape)/20):
blob.notACar()
if blob.isCar:
potentialCar.append(blob)
# Remove Matched Contour
if (len(contours) > 0):
contours = np.delete(contours, countCnt)
# If blob does not get updated, it simply is missing, flag it for removal later
else:
missingBlobs.append(countBlob)
####### Remove missing blobs #######
for i in sorted(missingBlobs, reverse=True):
del blobs[i]
####### Add newly detected blob into list #######
if (len(contours) > 0):
for cnt in contours:
index += 1
# Determing the lowest and highest point on the Y-axis of the contour
low = np.min(np.max(cnt,axis=1),axis=0)
high = np.max(np.max(cnt,axis=1),axis=0)
# Compute the area of the contour
area = cv2.contourArea(cnt)
# Compute the center point ob the Y-axis of the contour
centerCnt = [(high[0]+low[0])/2, (high[1]+low[1])/2]
# Create blob object and add into the list
blobs.append(Blob(index, cnt, low, high, centerCnt, area))
######## Group potential blobs as cars ########
matched = False
groupedBlob = []
cars = [[]]
for countA, a in enumerate(potentialCar, start=0):
# Skip blob that had been grouped
if countA in groupedBlob:
continue
if not a.isCar:
continue
for countB, b in enumerate(potentialCar[(countA + 1):], start=0):
# Skip blob that had been grouped
if (countA + countB + 1) in groupedBlob:
continue
if not b.isCar:
continue
# Check if blob is witin a certain range
minValX = min(a.minVal[0], b.minVal[0])
maxValX = max(a.maxVal[0], b.maxVal[0])
if carDirection:
# Set horizontal search limits for blobs
if (abs(minValX - maxValX) > (carGroupY * 40)):
continue
# Set vertical search limits for blobs
if (abs(a.center[1] - b.center[1]) > (carGroupX * 3)):
continue
else:
# Set horizontal search limits for blobs
if (abs(minValX - maxValX) > (carGroupX * 40)):
continue
# Set vertical search limits for blobs
if (abs(a.center[1] - b.center[1]) > (carGroupY * 3)):
continue
# if ((a.area/b.area) >= 1.2 or
# (a.area/b.area) <= 0.8 ):
# continue
# Check if blob are moving in same direction
for countM, m in enumerate(blob.movement[-3:], start=0):
matched = True
# The if else for zero is simply because I screwed up the blob class structure and too lazy to fix it
if countM == 0:
# For theta (vector) movement in Q1 and Q3 result in positive while moving in Q2 and Q4 result in negative number
# If multiplication of two vectore result in negative, they are moving in different direction
if (theta(a.origin, a.movement[countM]) * theta(b.origin, b.movement[countM])) < 0:
matched = False
break
# Above only remove the possibility of two adjacent quadrant movement, we still need to check opposite quadrant
# This can be achive by checking the vertical movement
if ((a.origin[1] - a.movement[countM][1]) * (b.origin[1] - b.movement[countM][1])) < 0:
matched = False
break
else:
# SAME AS ABOVE LOL
if (theta(a.movement[countM - 1], a.movement[countM]) * theta(b.movement[countM - 1], b.movement[countM])) < 0:
matched = False
break
if ((a.movement[countM - 1][1] - a.movement[countM][1]) * (b.movement[countM - 1][1] - b.movement[countM][1])) < 0:
matched = False
break
# It's a match! GROUP EM
if matched:
# GROUPING EM
cars.append([a,b])
# Append second for loop count is enough, as first loop proceeds on
# If you are confused
# First loop moved on to next iteration because it din't find a match, so there's no need to look back to those when look into second loop
# If you are STILL confused, just accept the fact that this improve the efficiency of the code
groupedBlob.append((countA + countB + 1))
break
####### Draw stuff around the car headlights #######
if showCar:
for countCar, car in enumerate(cars):
if car:
# Look for borders
minValX = min(car[0].minVal[0], car[1].minVal[0])
minValY = min(car[0].minVal[1], car[1].minVal[1])
maxValX = max(car[0].maxVal[0], car[1].maxVal[0])
maxValY = max(car[0].maxVal[1], car[1].maxVal[1])
# Look for center
centerX = (minValX + maxValX)/2
centerY = (minValY + maxValY)/2
# # Draw em
cv2.putText(resized, str(countCar),
(int(centerX - 10), int(centerY - 20)),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
cv2.rectangle(resized, (minValX, minValY), (maxValX, maxValY), (0,0,0), 2)
####### Text of cars detected #######
carCount.append(countCar)
carCount.pop(0)
displayCount += 1
if toggleStableCount:
if stableCountValue == 0:
stableCountValue = 1
if (displayCount % stableCountValue) == 0:
carCountSum = 0
for x in carCount[-stableCountValue:]:
carCountSum += x
displayText = int(carCountSum/stableCountValue)
if displayText == 0:
if not (carCountSum == 0):
displayText = 1
if displayCount >= 1000:
displayCount = 0
else:
displayText = countCar
text = "Car Detected: " + str(displayText)
cv2.putText(resized, text,
(10, 50),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
####### Draw blobs #######
if showBlob:
for blob in blobs:
cv2.putText(resized, str(blob.index),
(int(blob.center[0] - 10), int(blob.center[1] - 20)),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
cv2.drawContours(resized, [blob.contour], -1, (0,0,0), 3)
####### Display results #######
cv2.imshow('Output', resized)
cv2.imshow('Controller',blank)
### Restart video ###
else:
cap.set(cv2.CAP_PROP_POS_FRAMES, 0)
### Play video ###
# Change the number in waitkey brackets to alter play speed, bigger number slower speed
# 'q' means press q to exit
if cv2.waitKey() & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()