def main():
filepath = videopath
print filepath
video = cv2.VideoCapture("./" + videopath)
width = video.get(cv2.CAP_PROP_FRAME_WIDTH) # float video.get(3)
height = video.get(cv2.CAP_PROP_FRAME_HEIGHT) # float video.get(4)
if not video.isOpened():
print("Video not Opened!")
return
#Maybe we should check if the video has at least 2 frames
#Read the first and second frame of the video to start doing processing on them
_, imgFrame1 = video.read()
_, imgFrame2 = video.read()
#start framecount as 2 because we just read 2 frames
atFrame = 2
#up to this point we have none blobs yet
blobs = []
# To count people and pass it as a parameter. It doens't work with primitive variables (int)
peopleCount = [0]
seenPeople = set()
#While the video is open and we don't press q key read, process and show a frame
while (video.isOpened()):
#for every frame, check how many blobs are in the screen
currentBlobs = []
imgFrame1Copy = copy.deepcopy(imgFrame1)
imgFrame2Copy = copy.deepcopy(imgFrame2)
imgFrame1Copy = cv2.cvtColor(imgFrame1Copy, cv2.COLOR_BGR2GRAY)
imgFrame2Copy = cv2.cvtColor(imgFrame2Copy, cv2.COLOR_BGR2GRAY)
if (debugGaussian and debug_mode):
cv2.imshow('gaussianBlurBefore-Img1', imgFrame1Copy)
cv2.imshow('gaussianBlurBefore-Img2', imgFrame2Copy)
imgFrame1Copy = cv2.GaussianBlur(imgFrame1Copy, gaussian_kernel, 0)
imgFrame2Copy = cv2.GaussianBlur(imgFrame2Copy, gaussian_kernel, 0)
if (debugGaussian and debug_mode):
cv2.imshow('gaussianBlurAfter-Img1', imgFrame1Copy)
cv2.imshow('gaussianBlurAfter-Img2', imgFrame2Copy)
imgDifference = cv2.absdiff(imgFrame1Copy, imgFrame2Copy)
if (debugGaussian and debug_mode):
cv2.imshow('dif-Img1-Img2', imgDifference)
# ret value is used for Otsu's Binarization if we want to
# https://docs.opencv.org/3.4.0/d7/d4d/tutorial_py_thresholding.html
ret, imgThresh = cv2.threshold(imgDifference, threshold_value, 255.0,
cv2.THRESH_BINARY)
if debugThreshold and debug_mode:
cv2.imshow('imgThresh', imgThresh)
#all the pixels near boundary will be discarded depending upon the size of kernel. erosion removes white noises
imgThresh = cv2.dilate(imgThresh, kernel_dilate1, iterations=1)
if debug_dilate:
cv2.imshow('dilate-dilate1', imgThresh)
imgThresh = cv2.erode(imgThresh, kernel_erode1, iterations=1)
if debug_erode:
cv2.imshow('dilate-erode1', imgThresh)
imgThresh = cv2.dilate(imgThresh, kernel_dilate2, iterations=1)
if debug_dilate:
cv2.imshow('dilate-dilate2', imgThresh)
imgThresh = cv2.erode(imgThresh, kernel_erode2, iterations=1)
if debug_erode:
cv2.imshow('dilate-erode2', imgThresh)
imgThreshCopy = copy.deepcopy(imgThresh)
# Contours can be explained simply as a curve joining all the continuous points (along the boundary),
# having same color or intensity. The contours are a useful tool for shape analysis and object detection and recognition.
# https://docs.opencv.org/3.1.0/d4/d73/tutorial_py_contours_begin.html
#im2, contours, hierarchy = cv2.findContours(imgThreshCopy, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
imgThreshCopy, contours, hierarchy = cv2.findContours(
imgThreshCopy, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
drawAndShowContours(imgThreshCopy, contours, 'imgContours')
#up here we made all processing image stuff and now we need to work with the info we extrated from the image
#for every thing it's identified on the screen, check if it is a people
for x in contours:
convexHull = cv2.convexHull(x)
blob = Blob(convexHull)
if (blob.isObject()):
currentBlobs.append(blob)
drawAndShowBlobs(imgThresh, currentBlobs, "imgCurrentBlobs")
if atFrame <= 2:
#if it is first iteration there is no comparison, add curBlos to blobs
for curBlob in currentBlobs:
curBlob.id = Blob.getId()
blobs.append(curBlob)
else:
#otherwise check if the curblob is releated to a previous blob and match them
matchCurrentFrameBlobsToExistingBlobs(blobs, currentBlobs)
if debug_all_current_blobs:
for b in blobs:
print b
drawAndShowBlobs(imgThresh, blobs, "imgBlobs")
imgFrame2Copy = copy.deepcopy(imgFrame2)
drawBlobInfoOnImage(blobs, imgFrame2Copy)
#check if the blob crossed the explained
atLeastOneBlobCrossedTheLine = checkIfBlobsCossedTheLine(
blobs, line, peopleCount, seenPeople, case)
#if it has cross draw a colorful line
if atLeastOneBlobCrossedTheLine:
cv2.line(imgFrame2Copy, (line[0].x, line[0].y),
(line[1].x, line[1].y), (255, 0, 255), 2) #yellow line
else:
cv2.line(imgFrame2Copy, (line[0].x, line[0].y),
(line[1].x, line[1].y), (0, 255, 255), 2)
#draw the counter
drawPeopleCounterOnImage(peopleCount, imgFrame2Copy, width, height)
cv2.imshow('imgFrame2Copy', imgFrame2Copy)
# get ready for next iteration
del currentBlobs[:]
imgFrame1 = copy.deepcopy(imgFrame2)
if ((video.get(cv2.CAP_PROP_POS_FRAMES) + 1) <
(video.get(cv2.CAP_PROP_FRAME_COUNT))):
_, imgFrame2 = video.read()
else:
print("end of video")
break
atFrame += 1
#print("frame: " + str(count))
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if debug_mode and cv2.waitKey() & 0xFF == ord('q'):
break
video.release()
cv2.destroyAllWindows()
print("end")
def addNewBlob(curBlob, blobs):
curBlob.id = Blob.getId()
curBlob.isMatchFoundOrNewBlob = True
blobs.append(curBlob)
