def dictionay_learning_MHOF_online(training_samples_num=400):
from MHOF_Extraction import MHOF_Extraction
from MHOF_histogram_block import MHOF_histogram_block
from sklearn.decomposition import MiniBatchDictionaryLearning
import numpy as np
import cv2
import video
cam=video.create_capture('Crowd-Activity-All.avi')
height_block_num=4
width_block_num=5
bin_num=16
ret,prev=cam.read()
ret,img=cam.read()
flow_H=MHOF_Extraction(prev,img)
flow_hist_H=MHOF_histogram_block(flow_H,height_block_num,width_block_num,bin_num)
flow_hist_H=np.reshape(flow_hist_H,[1,flow_hist_H.size])
# error!!!!
dico=MiniBatchDictionaryLearning(1,alpha=1,n_iter=500)
dic=dico.fit(flow_hist_H).components_
for i in range(training_samples_num):
ret,img=cam.read()
flow_H=MHOF_Extraction(prev,img)
flow_hist_H=MHOF_histogram_block(flow_H,height_block_num,width_block_num,bin_num)
dico=MiniBatchDictionaryLearing(i+1,alpha=1,n_iter=500,dict_init=dic)
dic=dico.fit(flow_hist_H).components
return dic
def __init__(self, video_src):
self.track_len = 25
self.detect_interval = 1
self.tracks = []
self.capture = cv2.VideoCapture(video_src)
self.frame_width = int(self.capture.get(cv.CV_CAP_PROP_FRAME_WIDTH) / 2)
self.frame_height = int(self.capture.get(cv.CV_CAP_PROP_FRAME_HEIGHT) / 2)
self.frame_size = (self.frame_width, self.frame_height)
self.grid_width = int(self.frame_width / 8 / 2)
self.grid_height = int(self.frame_height / 8 / 2)
self.grid_size = (self.grid_width, self.grid_height)
self.total_frame_count = int(self.capture.get(cv.CV_CAP_PROP_FRAME_COUNT))
print self.frame_size
print self.grid_size
print self.total_frame_count
self.data_path = str(video_src) + ".oflw"
print self.data_path
self.fp = np.memmap(self.data_path, dtype="float32", mode="w+", shape=(self.total_frame_count, (512 + 128)))
print "FP shape: ", self.fp.shape
self.cam = video.create_capture(video_src)
self.frame_idx = 0
def __init__(self, video_src):
self.track_len = 10
self.detect_interval = 3
self.tracks = {}
self.cam = video.create_capture(video_src)
self.frame_idx = 0
self.prev_gray = {}
def main():
import sys
try:
fn = sys.argv[1]
except:
fn = 0
cap = video.create_capture(fn)
leveln = 6
cv.namedWindow('level control')
for i in xrange(leveln):
cv.createTrackbar('%d'%i, 'level control', 5, 50, nothing)
while True:
ret, frame = cap.read()
pyr = build_lappyr(frame, leveln)
for i in xrange(leveln):
v = int(cv.getTrackbarPos('%d'%i, 'level control') / 5)
pyr[i] *= v
res = merge_lappyr(pyr)
cv.imshow('laplacian pyramid filter', res)
if cv.waitKey(1) == 27:
break
print('Done')
def main():
try:
fn = sys.argv[1]
except:
fn = 0
cv2.namedWindow('edge')
cap = video.create_capture(fn)
cv2.setMouseCallback('edge', onmouse)
global ser
ser = serial.Serial('COM4',9600)
count =0
while True:
#print seed_pt[0]
flag, img = cap.read()
vis=img.copy()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
if count%2==0:
main_check(vis,gray,seed_pt)
cv2.imshow('edge', vis)
count+=1
ch = cv2.waitKey(5) & 0xFF
if ch == 27:
break
def main():
try:
video_src = sys.argv[1]
except:
video_src = 0
cam = video.create_capture(video_src)
mser = cv.MSER_create()
while True:
ret, img = cam.read()
if ret == 0:
break
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
vis = img.copy()
regions, _ = mser.detectRegions(gray)
hulls = [cv.convexHull(p.reshape(-1, 1, 2)) for p in regions]
cv.polylines(vis, hulls, 1, (0, 255, 0))
cv.imshow('img', vis)
if cv.waitKey(5) == 27:
break
print('Done')
def __init__(self, video_src, paused = False):
self.cap = video.create_capture(video_src)
_, self.frame = self.cap.read()
cv2.imshow('frame', self.frame)
self.rect_sel = RectSelector('frame', self.onrect)
self.trackers = []
self.paused = paused
def __init__(self, video_src, skipFrame):
self.track_len = 10
self.detect_interval = 5
self.tracks = []
self.cam = video.create_capture(video_src)
self.frame_idx = 0
self.skipFrame = skipFrame
def main():
try:
fn = sys.argv[1]
except:
fn = 0
def nothing(*arg):
pass
cv.namedWindow('edge')
cv.createTrackbar('thrs1', 'edge', 2000, 5000, nothing)
cv.createTrackbar('thrs2', 'edge', 4000, 5000, nothing)
cap = video.create_capture(fn)
while True:
flag, img = cap.read()
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
thrs1 = cv.getTrackbarPos('thrs1', 'edge')
thrs2 = cv.getTrackbarPos('thrs2', 'edge')
edge = cv.Canny(gray, thrs1, thrs2, apertureSize=5)
vis = img.copy()
vis = np.uint8(vis/2.)
vis[edge != 0] = (0, 255, 0)
cv.imshow('edge', vis)
ch = cv.waitKey(5)
if ch == 27:
break
print('Done')
def __init__(self, src, threshold = 25, doRecord=True, showWindows=True):
self.doRecord = doRecord
self.show = showWindows
self.frame = None
self.cap = video.create_capture(src)
self.cap.set(3,1280)
self.cap.set(4,2316)
self.ret, self.frame = self.cap.read() #Take a frame to init recorder
self.frame_rate = self.cap.get(5)
print self.frame_rate
self.gray_frame = np.zeros((self.cap.get(3), self.cap.get(4), 1), np.uint8)
self.average_frame = np.zeros((self.cap.get(3), self.cap.get(4), 3), np.float32)
self.absdiff_frame = None
self.previous_frame = None
self.surface = self.cap.get(3) * self.cap.get(4)
self.currentsurface = 0
self.currentcontours = None
self.threshold = threshold
self.isRecording = False
self.tracks = []
self.tracks_dist = []
self.track_len = 3
self.frame_idx = 0
self.detect_interval = 5
# self.font = cv.InitFont(cv.CV_FONT_HERSHEY_SIMPLEX, 1, 1, 0, 2, 8) #Creates a font
self.trigger_time = 0
if showWindows:
cv2.namedWindow("Image", cv2.WINDOW_AUTOSIZE)
def detectFace(self):
print "stuff"
import sys, getopt
print help_message
args, video_src = getopt.getopt(sys.argv[1:], '', ['cascade=', 'nested-cascade='])
try: video_src = video_src[0]
except: video_src = 0
args = dict(args)
# cascade_fn = args.get('--cascade', "haarcascade_frontalface_alt.xml")
cascade = cv2.CascadeClassifier("haarcascade_frontalface_alt.xml")
cam = create_capture(video_src, fallback='synth:bg=lena.jpg:noise=0.05')
found = False
while True:
ret, img = cam.read()
rects = self.detect(img, cascade)
vis = img.copy()
if(rects != [] and found == False):
print "here"
if goodOrBad(img,cascade):
found = True
cv2.imshow('facedetect', vis)
if 0xFF & cv2.waitKey(5) == 27:
break
cv2.destroyAllWindows()
def __init__(self, video_src, circles, csvFlag, rotation = 0):
"""
Initializes main function for execution
:param video_src: path to video to be analyzed
:param circles: path to text file containing joints
:param csvFlag: flag if video src is actually a csv that should be read in. ONLY USE THIS WITH DEBUG MODE!
"""
self.roguePoints = []
self.rotation = rotation
self.csvFlag = csvFlag
if not csvFlag:
self.cam = video.create_capture(video_src)
else:
self.cam = csv.reader(open(video_src, 'r'), delimiter=',', quotechar='|')
self.frame_idx = -1#This is because the frame index updates BEFORE anything is done.
#Save frame to start at and the initial circles.
self.allJoints = []
f = open(circles, 'r')
self.initalFrame = int(f.readline())
for line in f:
read = map(float, line.split())#x y r (with spaces)
circle = j.Circle(read[0], read[1] , read[2])
self.allJoints.append( j.Joint(circle, prm.TRACK_LEN ) )# circle in form [(x,y),r]
f.close()
def __init__(self, src):
self.cap = video.create_capture(src)
self.frame = None
self.rect_obj = None
cv2.namedWindow('plane')
self.rect_sel = common.RectSelector('plane', self.on_rect)
def compare(face_to_check,learn=False):
import sys, getopt
detected_time = 0
detected_time_max = 10
video_src = 0
cascade_fn = os.path.join('data','haarcascades','haarcascade_frontalface_alt2.xml')
cascade = cv2.CascadeClassifier(cascade_fn)
cam = create_capture(video_src, fallback='synth:bg=../cpp/lena.jpg:noise=0.05')
while True:
ret, img1 = cam.read()
gray = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
gray = cv2.equalizeHist(gray)
t = clock()
rects = detect(gray, cascade)
if len(rects):
if detected_time<detected_time_max:
detected_time+=1
else:
_found_size = (rects[0][0],rects[0][1],rects[0][2]-rects[0][0],
rects[0][3]-rects[0][1])
_found_face = cv.GetImage(cv.fromarray(img1))
cv.SetImageROI(_found_face,_found_size)
current_face = cv.CreateImage(cv.GetSize(_found_face),
_found_face.depth,
_found_face.nChannels)
if learn:
cv.Copy(_found_face, current_face, None)
cv.SaveImage(os.path.join('data','images',face_to_check),current_face)
cv.ResetImageROI(cv.GetImage(cv.fromarray(img1)))
img2 = cv.LoadImage(os.path.join('data','images',face_to_check))
dest_face = cv.CreateImage(cv.GetSize(img2),
img2.depth,
img2.nChannels)
cv.Resize(_found_face, dest_face)
if cv.Norm(dest_face,img2)<=30000:
return True
else:
return False
sys,exit()
else:
detected_time = 0
dt = clock() - t
def main():
cap = video.create_capture()
classifier_fn = 'digits_svm.dat'
if not os.path.exists(classifier_fn):
print '"%s" not found, run digits.py first' % classifier_fn
return
model = SVM()
model.load('digits_svm.dat')
while True:
ret, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
bin = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 31, 10)
bin = cv2.medianBlur(bin, 3)
contours, heirs = cv2.findContours( bin.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
rects = map(cv2.boundingRect, contours)
valid_flags = [ 16 <= h <= 64 and w <= 1.2*h for x, y, w, h in rects]
for i, cnt in enumerate(contours):
if not valid_flags[i]:
continue
_, _, _, outer_i = heirs[0, i]
if outer_i >=0 and valid_flags[outer_i]:
continue
x, y, w, h = rects[i]
cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0))
sub = bin[y:,x:][:h,:w]
#sub = ~cv2.equalizeHist(sub)
#_, sub_bin = cv2.threshold(sub, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)
s = 1.5*float(h)/SZ
m = cv2.moments(sub)
m00 = m['m00']
if m00/255 < 0.1*w*h or m00/255 > 0.9*w*h:
continue
c1 = np.float32([m['m10'], m['m01']]) / m00
c0 = np.float32([SZ/2, SZ/2])
t = c1 - s*c0
A = np.zeros((2, 3), np.float32)
A[:,:2] = np.eye(2)*s
A[:,2] = t
sub1 = cv2.warpAffine(sub, A, (SZ, SZ), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR)
sub1 = deskew(sub1)
if x+w+SZ < frame.shape[1] and y+SZ < frame.shape[0]:
frame[y:,x+w:][:SZ, :SZ] = sub1[...,np.newaxis]
sample = preprocess_hog([sub1])
digit = model.predict(sample)[0]
cv2.putText(frame, '%d'%digit, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.0, (200, 0, 0), thickness = 1)
cv2.imshow('frame', frame)
cv2.imshow('bin', bin)
if cv2.waitKey(1) == 27:
break
def __init__(self, src):
self.cap = video.create_capture(src)
self.frame = None
self.paused = False
self.tracker = PlaneTracker()
cv2.namedWindow("plane")
self.rect_sel = common.RectSelector("plane", self.on_rect)
def __init__(self, src):
self.cap = video.create_capture(src, presets['book'])
self.frame = None
self.paused = False
self.tracker = PlaneTracker()
cv2.namedWindow('plane')
self.rect_sel = common.RectSelector('plane', self.on_rect)
def __init__(self, video_src):
self.track_len = 10
self.detect_interval = 5
self.tracks = []
self.cam = video.create_capture(video_src)
self.frame_idx = 0
self.screenx=700
self.screeny=550
def __init__(self, src):
self.cap = video.create_capture(src)
self.frame = None
self.paused = False
self.tracker = PlaneTracker()
cv2.namedWindow('plane')
cv2.createTrackbar('focal', 'plane', 25, 50, common.nothing)
self.rect_sel = common.RectSelector('plane', self.on_rect)
def __init__(self, video_src):
self.cam = video.create_capture(video_src)
ret, self.frame = self.cam.read()
cv2.namedWindow('camshift')
cv2.setMouseCallback('camshift', self.onmouse)
self.selection = None
self.drag_start = None
self.tracking_state = 0
self.show_backproj = False
def main():
try:
video_src = sys.argv[1]
except:
print help_message
return
cap = video.create_capture(video_src)
capture = cv.CaptureFromFile(video_src)
fps = cv.GetCaptureProperty(capture, cv.CV_CAP_PROP_FPS)
num_frames = float(cv.GetCaptureProperty(capture, cv.CV_CAP_PROP_FRAME_COUNT))
keyDown = False
states = []
index = 0
while True:
ret, frame = cap.read()
if ret:
if len(states) == 0:
# show first frame and let user decide if it is good or bad
draw_str(frame, (20, 40), 'BAD')
cv2.imshow('', frame)
ch = cv2.waitKey(2500)
else:
ch = cv2.waitKey(int(1000/fps))
if ch != -1: # key pressed
keyDown = True if not keyDown else False
if keyDown:
state = 'GOOD'
states.append(1)
else:
state = 'BAD'
states.append(0)
# draw_str(frame, (20, 40), state)
draw_str(frame, (20, 20), '%s, %2d:%02d\t %2.2f%%' % (state, int((index / fps) / 60), int((index / fps) % 60), 100.0 * index / num_frames))
cv2.imshow('', frame)
index += 1
else:
# no more frames...
break
d = dict(states=states)
content = json.dumps(d)
# do not write a file if json parser fails
if content:
# write to disc
f = open('%s.txt' % video_src,'w')
f.write(content)
f.close()
else:
print 'error in json parser'
def __init__(self, src):
self.cap = video.create_capture(src)
self.frame = None
self.paused = False
self.tracker = PlaneTracker()
cv2.namedWindow("plane")
cv2.setMouseCallback("plane", self.on_mouse)
self.drag_start = None
self.track_window = None
def __init__(self, video_src):
self.cam = video.create_capture(video_src, presets['cube'])
_ret, self.frame = self.cam.read()
cv.namedWindow('camshift')
cv.setMouseCallback('camshift', self.onmouse)
self.selection = None
self.drag_start = None
self.show_backproj = False
self.track_window = None
def __init__(self, video_src):
self.track_len = 10
self.cam = video.create_capture(video_src)
self.screenx=700
self.screeny=550
self.threshold=50
self.switch_time=3
self.codec = cv2.VideoWriter_fourcc('W', 'M', 'V', '2')
cv2.namedWindow("Image")
def __init__(self, video_src):
self.track_len = 10
self.detect_interval = 5
self.tracks = []
self.cam = video.create_capture(video_src)
self.frame_idx = 0
self.last_event = clock()
self.filter = 1
self.min_interval = 5
self.slow_frame=0
self.sin_transform=None
def __init__(self, fp):
"""
Initialize with filepath to .avi file you want to alter.
"""
self._fps = 23.92
self._capture_size = (1920, 1080)
self._fourcc = cv.CV_FOURCC('m','p','4','v')
self._directory = os.path.split(fp)[0]
self._file_name = os.path.splitext(os.path.split(fp)[1])[0]
self._file_ext = os.path.splitext(os.path.split(fp)[1])[1]
self._capture = video.create_capture(fp)
def __init__(self, video_src):
self.track_len = 10
self.cam = video.create_capture(video_src)
self.screenx=700
self.screeny=550
self.threshold=50
self.switch_time=10
self.codec = cv2.VideoWriter_fourcc('W', 'M', 'V', '2')
self.total_writer=cv2.VideoWriter("total"+".wmv", self.codec, 10, (640,480), 1)
cv2.namedWindow("Image")
def __init__(self, src):
self.cap = video.create_capture(src)
self.ref_frame = None
self.detector = cv2.ORB(nfeatures=1000)
self.matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329)
cv2.namedWindow("plane")
self.rect_sel = common.RectSelector("plane", self.on_rect)
self.frame = None
def video_feed(queue):
'''
Reads from a video capture and puts a frame in the queue.
'''
video = create_capture(0)
success, frame = video.read()
while success != 0:
frame = crop_frame(frame)
queue.put(frame)
success, frame = video.read()
def __init__(self, video_src): self.track_len = 10 self.detect_interval = 5 self.tracks = [] self.cam = video.create_capture(video_src) self.frame_idx = 0 self.video_src = video_src capture = cv.CaptureFromFile(video_src) self.num_frames = int(cv.GetCaptureProperty(capture, cv.CV_CAP_PROP_FRAME_COUNT)) self.fps = cv.GetCaptureProperty(capture, cv.CV_CAP_PROP_FPS) self.frame_metadata = []
except:
video_src = 0
args = dict(args) # make args as dictionary
cascade_fn = args.get('--cascade',
"data/haarcascades/haarcascade_frontalface_alt.xml"
) # get trained face detected data
nested_fn = args.get('--nested-cascade',
"data/haarcascades/haarcascade_eye.xml")
cascade = cv.CascadeClassifier(
cv.samples.findFile(cascade_fn)) # Loads the classifier from a file.
nested = cv.CascadeClassifier(cv.samples.findFile(nested_fn))
cam = create_capture(
video_src, # create_capture is a convenience function for capture creation,
# falling back to procedural video in case of error.
fallback='synth:bg={}:noise=0.05'.format(
cv.samples.findFile('samples/data/lena.jpg')))
while True:
t = clock() # start to count the time
time = 0.0 # initialize the time counting
ret, img = cam.read() # capture a frame and store it
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY
) # convert the color of the image from bgr to gray
gray = cv.equalizeHist(
gray) # Equalizes the histogram of a grayscale image.
vis = img.copy() # make a copy of the image
if detected.all(
) == 0 or total_time > 3000: # if we haven't detected a face or can't find face in
# the current sub rectangle more than 3000ms,start to detect in the full image
def __init__(self, video_src, usePiCamera=False):
self.track_len = 10
self.detect_interval = 3
self.tracks = []
self.cam = video.create_capture(video_src, usePiCamera=usePiCamera)
self.frame_idx = 0
def main():
try:
src = sys.argv[1]
except:
src = 0
cap = video.create_capture(src)
classifier_fn = 'digits_svm.dat'
if not os.path.exists(classifier_fn):
print '"%s" not found, run digits.py first' % classifier_fn
return
model = SVM()
model.load(classifier_fn)
while True:
ret, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
bin = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY_INV, 31, 10)
bin = cv2.medianBlur(bin, 3)
contours, heirs = cv2.findContours(bin.copy(), cv2.RETR_CCOMP,
cv2.CHAIN_APPROX_SIMPLE)
try:
heirs = heirs[0]
except:
heirs = []
for cnt, heir in zip(contours, heirs):
_, _, _, outer_i = heir
if outer_i >= 0:
continue
x, y, w, h = cv2.boundingRect(cnt)
if not (16 <= h <= 64 and w <= 1.2 * h):
continue
pad = max(h - w, 0)
x, w = x - pad / 2, w + pad
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0))
bin_roi = bin[y:, x:][:h, :w]
gray_roi = gray[y:, x:][:h, :w]
m = bin_roi != 0
if not 0.1 < m.mean() < 0.4:
continue
'''
v_in, v_out = gray_roi[m], gray_roi[~m]
if v_out.std() > 10.0:
continue
s = "%f, %f" % (abs(v_in.mean() - v_out.mean()), v_out.std())
cv2.putText(frame, s, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.0, (200, 0, 0), thickness = 1)
'''
s = 1.5 * float(h) / SZ
m = cv2.moments(bin_roi)
c1 = np.float32([m['m10'], m['m01']]) / m['m00']
c0 = np.float32([SZ / 2, SZ / 2])
t = c1 - s * c0
A = np.zeros((2, 3), np.float32)
A[:, :2] = np.eye(2) * s
A[:, 2] = t
bin_norm = cv2.warpAffine(bin_roi,
A, (SZ, SZ),
flags=cv2.WARP_INVERSE_MAP
| cv2.INTER_LINEAR)
bin_norm = deskew(bin_norm)
if x + w + SZ < frame.shape[1] and y + SZ < frame.shape[0]:
frame[y:, x + w:][:SZ, :SZ] = bin_norm[..., np.newaxis]
sample = preprocess_hog([bin_norm])
digit = model.predict(sample)[0]
cv2.putText(frame,
'%d' % digit, (x, y),
cv2.FONT_HERSHEY_PLAIN,
1.0, (200, 0, 0),
thickness=1)
cv2.imshow('frame', frame)
cv2.imshow('bin', bin)
ch = cv2.waitKey(1)
if ch == 27:
break
print(__doc__)
args, video_src = getopt.getopt(sys.argv[1:], '',
['cascade=', 'nested-cascade='])
try:
video_src = video_src[0]
except:
video_src = 0
args = dict(args)
cascade_fn = args.get('--cascade',
"data/haarcascades/haarcascade_frontalface_alt.xml")
cascade = cv.CascadeClassifier(cv.samples.findFile(cascade_fn))
cam = create_capture(video_src,
fallback='synth:bg={}:noise=0.05'.format(
cv.samples.findFile('samples/data/lena.jpg')))
# to_detect = []
detected = None
show_backproj = False
while True:
ret, img = cam.read()
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
gray = cv.equalizeHist(gray)
hsv = cv.cvtColor(img, cv.COLOR_BGR2HSV)
# (0, 60, 30) is a dark colour
# (180. 255, 255) is cyan
# masking is thresholding the HSV image to get only colours in
def main():
import sys, getopt
checknum = 0
while True:
try:
# face recognizing code
print('face camera ')
args, video_src = getopt.getopt(sys.argv[1:2], '', ['cascade=', 'nested-cascade='])
try:
video_src = video_src[0]
except:
video_src = 0
args = dict(args)
cascade_fn = args.get('--cascade', "data/haarcascades/haarcascade_frontalface_alt.xml")
nested_fn = args.get('--nested-cascade', "data/haarcascades/haarcascade_eye.xml")
cascade = cv.CascadeClassifier(cv.samples.findFile(cascade_fn))
nested = cv.CascadeClassifier(cv.samples.findFile(nested_fn))
cam = create_capture(video_src, fallback='synth:bg={}:noise=0.05'.format(cv.samples.findFile('samples/data/lena.jpg')))
while True:
ret, img = cam.read()
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
gray = cv.equalizeHist(gray)
rects = detect(gray, cascade)
vis = img.copy()
if len(rects):
if not nested.empty():
print('into nested')
for x1, y1, x2, y2 in rects:
roi = gray[y1:y2, x1:x2]
vis_roi = vis[y1:y2, x1:x2]
print('findrects')
subrects = detect(roi.copy(), nested)
if subrects!='[]':
faceok = 'faceok.wav'
fa = sa.WaveObject.from_wave_file(faceok)
face = fa.play()
face.wait_done()
print('detect!!')
break
cam.release() # face recognition camera off
# helmet detectecting code
print("helmet camera")
# load wav file
filename = 'helmet.wav'
wave_obj = sa.WaveObject.from_wave_file(filename)
helmetok = 'helmetok.wav'
wave = sa.WaveObject.from_wave_file(helmetok)
# load model & label for classifying
labels = "labels.txt"
model = "model_edgetpu.tflite"
interpreter = Interpreter(model, experimental_delegates=[load_delegate('libedgetpu.so.1.0')])
interpreter.allocate_tensors()
_, height, width, _ = interpreter.get_input_details()[0]['shape']
# helmet detect camera on
with picamera.PiCamera(resolution=(640, 480), framerate=30) as camera:
camera.start_preview()
try:
stream = io.BytesIO()
for _ in camera.capture_continuous(stream, format='jpeg', use_video_port=True):
stream.seek(0)
image = Image.open(stream).convert('RGB').resize((width, height),Image.ANTIALIAS)
results = classify_image(interpreter, image)
stream.seek(0)
stream.truncate()
# 헬멧 착용여부 판단
if results==0:
play_obj = wave_obj.play()
play_obj.wait_done()
checknum += 1
# Unless User does not wear helmet in three time of checking, the code will go backward
if checknum==3:
checknum = 0
break
else:
helm = wave.play()
helm.wait_done()
print('GoodBoy')
break
finally:
camera.stop_preview()
# If you want to interrupt the code running, then press Ctrl + C
except KeyboardInterrupt:
break
def main():
import sys
if path.exists('data/coordinates_1.yml'):
os.remove('data/coordinates_1.yml')
points = None
waterLevelSlots = None
try:
fn = sys.argv[1]
except:
fn = 0
cap = video.create_capture(fn)
def fetchShortIntervalVideos(ctrl, motion_detector, lock):
with lock:
videoFilePath, hasIncomingVideoCaptureChanged = ctrl.getVideoFilePath()
return videoFilePath, hasIncomingVideoCaptureChanged, motion_detector
threadn = cv.getNumberOfCPUs()
pending = deque()
lock = Lock()
pool = ThreadPool(processes = threadn, initializer = init_child, initargs=(lock,))
threaded_mode = True
ctrl = None
motionDetector = None
screenWidth = int(round(GetSystemMetrics(0) / 2))
screenHeight = int(round(GetSystemMetrics(1) / 2))
coordinates_data = None
times = None
statuses = None
pointsCaptured = False
while True:
with lock:
while len(pending) > 1 and pending[0].ready() and pending[1].ready():
payload = pending.popleft().get()
if len(payload) == 3:
videoFilePath, hasIncomingVideoCaptureChanged, motion_detector = payload
if videoFilePath == None and hasIncomingVideoCaptureChanged == None:
break
else:
capture, coordinates_data, times, statuses = motion_detector.detect_motion_activity(videoFilePath, hasIncomingVideoCaptureChanged)
while capture.isOpened():
result, frame = capture.read()
if not result:
capture.release()
continue
else:
res, evaluated_areas = motion_detector.process_algo_per_frame(frame, capture, coordinates_data, times, statuses)
#draw_str(res, (5, 20), WaterLevelSlots.LEVEL_REACHED_MSG
# + str(evaluated_waterLevelSlots.get_current_water_level()))
#cv.namedWindow('flood-detection', cv.WINDOW_NORMAL)
#cv.setWindowProperty('flood-detection', 0, 1)
#cv.imshow('flood-detection', res)
cv.namedWindow('OOW 2020 SMART CITY USE CASE - "SMART PEDESTRIAN CROSSING"', cv.WINDOW_NORMAL)
#cv.setWindowProperty('smart-parking', 0, 1)
#print(screenWidth)
#print(screenHeight)
resize = ResizeWithAspectRatio(res, width=screenWidth, height=screenHeight)
cv.imshow('OOW 2020 SMART CITY USE CASE - "SMART PEDESTRIAN CROSSING"', resize)
if len(pending) < threadn:
if not pointsCaptured:
_ret, frame = cap.read()
points = getPoints(frame, points)
area = Area()
ctrl = Controller(points, None, None)
motionDetector = MotionDetector(points, 1, area)
pointsCaptured = True
if threaded_mode:
task_put_videos = pool.apply_async(captureShortIntervalVideos, (cap, lock))
task_get_videos = pool.apply_async(fetchShortIntervalVideos,(ctrl, motionDetector, lock))
pending.append(task_put_videos)
pending.append(task_get_videos)
ch = cv.waitKey(1)
if ch == ord(' '):
threaded_mode = not threaded_mode
if ch == 27:
break
print('Done')
cap.release()
table_number=6, # 12
key_size=12, # 20
multi_probe_level=1) #2
matcher = cv2.FlannBasedMatcher(flann_params,
{}) # bug : need to pass empty dict (#1329)
green, red = (0, 255, 0), (0, 0, 255)
if __name__ == '__main__':
print __doc__
try:
src = sys.argv[1]
except:
src = 0
cap = video.create_capture(src)
ref_kp = None
while True:
ret, img = cap.read()
vis = img.copy()
kp = detector.detect(img)
kp, desc = extractor.compute(img, kp)
for p in kp:
x, y = np.int32(p.pt)
r = int(0.5 * p.size)
cv2.circle(vis, (x, y), r, (0, 255, 0))
draw_str(vis, (20, 20), 'feature_n: %d' % len(kp))
return hog_viz
#https://raw.githubusercontent.com/shiaokai/plex/master/python/peopledetect_cam.py
if __name__ == '__main__':
import sys
from glob import glob
import itertools as it
# -- temporary dimension mismatch
if len(sys.argv)==2:
img = cv2.imread(sys.argv[1])
hog_viz=look_at_hog(img)
imshow(hog_viz,cmap='gray');show()
else:
hog = cv2.HOGDescriptor()
#hog.setSVMDetector( cv2.HOGDescriptor_getDefaultPeopleDetector() )
cam = create_capture(1, fallback='synth:bg=../cpp/lena.jpg:noise=0.05')
while True:
ret, img = cam.read()
vis = img.copy()
t = clock()
if 1:
vis=look_at_hog(vis)
else:
found, w = hog.detectMultiScale(img, winStride=(8,8), padding=(32,32), scale=1.05)
found_filtered = []
for ri, r in enumerate(found):
for qi, q in enumerate(found):
if ri != qi and inside(r, q):
break
else:
found_filtered.append(r)
cx, cy = x+w/2, y+h/2
angle = angle*np.pi/180
cv2.circle(vis, (cx, cy), r, color, 3)
cv2.line(vis, (cx, cy), (int(cx+np.cos(angle)*r), int(cy+np.sin(angle)*r)), color, 3)
if __name__ == '__main__':
import sys
try: video_src = sys.argv[1]
except: video_src = 0
cv2.namedWindow('motempl')
visuals = ['input', 'frame_diff', 'motion_hist', 'grad_orient']
cv2.createTrackbar('visual', 'motempl', 2, len(visuals)-1, nothing)
cv2.createTrackbar('threshold', 'motempl', DEFAULT_THRESHOLD, 255, nothing)
cam = video.create_capture(video_src, fallback='synth:class=chess:bg=../cpp/lena.jpg:noise=0.01')
ret, frame = cam.read()
h, w = frame.shape[:2]
prev_frame = frame.copy()
motion_history = np.zeros((h, w), np.float32)
hsv = np.zeros((h, w, 3), np.uint8)
hsv[:,:,1] = 255
while True:
ret, frame = cam.read()
frame_diff = cv2.absdiff(frame, prev_frame)
gray_diff = cv2.cvtColor(frame_diff, cv2.COLOR_BGR2GRAY)
thrs = cv2.getTrackbarPos('threshold', 'motempl')
ret, motion_mask = cv2.threshold(gray_diff, thrs, 1, cv2.THRESH_BINARY)
timestamp = clock()
cv2.updateMotionHistory(motion_mask, motion_history, timestamp, MHI_DURATION)
mg_mask, mg_orient = cv2.calcMotionGradient( motion_history, MAX_TIME_DELTA, MIN_TIME_DELTA, apertureSize=5 )
print(__doc__)
count = 0
for i in range(1,1192):
print(str(i))
args, video_src = getopt.getopt(sys.argv[1:], '', ['cascade=', 'nested-cascade='])
try:
video_src = video_src[0]
except:
video_src = 0
args = dict(args)
cascade_fn = args.get('--cascade', "../../data/haarcascades/haarcascade_frontalface_alt.xml")
nested_fn = args.get('--nested-cascade', "../../data/haarcascades/haarcascade_eye.xml")
cascade = cv2.CascadeClassifier(cascade_fn)
nested = cv2.CascadeClassifier(nested_fn)
cam = create_capture(video_src, fallback='synth:bg=../data/friend/friendImage/image'+str(i)+'.jpg:noise=0.05')
ret, img = cam.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = cv2.equalizeHist(gray)
rects = detect(gray, cascade)
vis = img.copy()
draw_rects(vis, rects, (0, 255, 0))
if not nested.empty():
if len(rects) == 0:
print('none')
else:
count = count + 1
print(str(count))
end_header
'''
def write_ply(fn, verts, colors):
verts = verts.reshape(-1, 3)
colors = colors.reshape(-1, 3)
verts = np.hstack([verts, colors])
with open(fn, 'w') as f:
f.write(ply_header % dict(vert_num=len(verts)))
np.savetxt(f, verts, '%f %f %f %d %d %d')
if __name__ == '__main__':
print 'loading images...'
camL = video.create_capture(1)
camR = video.create_capture(0)
_, imgL = camL.read()
_, imgR = camR.read()
# disparity range is tuned for 'aloe' image pair
window_size = 3
min_disp = 16
num_disp = 112 - min_disp
stereo = cv2.StereoSGBM(minDisparity=min_disp,
numDisparities=num_disp,
SADWindowSize=window_size,
uniquenessRatio=10,
speckleWindowSize=100,
speckleRange=32,
def main():
import sys
try:
fn = sys.argv[1]
except IndexError:
fn = 0
try:
start_at = int(sys.argv[2])
except IndexError:
start_at = 0
try:
compute_scene = len(sys.argv[3]) <= 0
except IndexError:
compute_scene = True
if compute_scene:
scenelist = getSceneList(fn, start_at)
else:
scenelist = [(start_at, float("inf"))]
if scenelist is None or len(scenelist) == 0:
scenelist = [(start_at, float("inf"))]
cam = video.create_capture(fn)
cam.set(cv.CAP_PROP_POS_FRAMES, start_at)
nb_frames = int(cam.get(cv.CAP_PROP_FRAME_COUNT))
rate = cam.get(cv.CAP_PROP_FPS)
ret, prev = cam.read()
realh, realw = prev.shape[:2]
prev = reduce(prev)
#h, w = realh//3, realw//3
h, w = prev.shape[:2]
#prevgray = cv.cvtColor(prev, cv.COLOR_BGR2GRAY)[w:2*w,h:2*h]
prevgray = cv.cvtColor(prev, cv.COLOR_BGR2GRAY)
pbar = tqdm(total=nb_frames - start_at)
frame = start_at
scene_results = []
for scene_start, scene_end in scenelist:
if frame > scene_start:
continue
for i in range(frame, scene_start):
ret, prev = cam.read()
pbar.update(1)
if scene_start - frame > 0:
prev = reduce(prev)
prevgray = cv.cvtColor(prev, cv.COLOR_BGR2GRAY)
frame = scene_start
data = []
while frame < scene_end:
ret, img = cam.read()
if not ret:
break
img = reduce(img)
#gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)[w:2*w,h:2*h]
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
flow = cv.calcOpticalFlowFarneback(prevgray, gray, None, 0.5, 3,
15, 3, 5, 1.2, 0)
prevgray = gray
# data.append(np.absolute(flow).mean(axis=(0,1)))#Si plusieurs mouvements, ils peuvent s'annuler
flow0 = flow.mean(axis=(0, 1))
if len(data) > 0 and np.absolute(
maxi).sum() < 12: #skip the next frame
data.append(np.stack((data[-1], flow0)).mean(axis=0))
data.append(flow0)
maxi = flow.max(axis=(0, 1))
pbar.update(1)
frame += 1
if frame < scene_end and np.absolute(
maxi).sum() < 12: #skip the next frame
pbar.update(1)
frame += 1
if frame == scene_end:
data.append(flow0)
ret, img = cam.read()
#cv.imshow('flow', draw_flow(gray, flow))
#ch = cv.waitKey(5)
# code.interact(local=locals())
freq = np.fft.rfftfreq(len(data), 1 / rate)
maxima = None
freqs = None
t = None
for i in range(2):
signal = np.array(data)[:, i]
#data = np.linalg.norm(data, axis=1)
#data = np.sum(data, axis=1)
signal = running_mean(signal, 3)
signal = np.array(signal)
f, t, Zxx = sig.stft(signal,
rate,
boundary='even',
nperseg=min(3 * rate, signal.shape[0]),
noverlap=min(3 * rate - 1,
signal.shape[0] - 1))
maxi = np.abs(Zxx).max(axis=0)
if maxima is None:
maxima = maxi
else:
maxima = np.stack((maxima, maxi))
freq = np.take(f, np.abs(Zxx).argmax(axis=0))
if freqs is None:
freqs = freq
else:
freqs = np.stack((freqs, freq))
#plt.show()
#maxima[i] = np.absolute(sp[first:]).max()
#indices[i] = np.unravel_index(np.absolute(
# sp[first:]).argmax(), sp.shape)[0] + first
freq = maxima.argmax(axis=0).choose(freqs)
freq = running_mean(freq, min(int(rate), freq.shape[0]))
#plt.plot(t, freq)
#plt.show()
scene_results.append((t + scene_start / rate, freq))
pbar.close()
#for t, freq in scene_results:
#plt.plot(np.array([i/rate for i in range(nb_frames)]),
fig, ax = plt.subplots()
plt.plot(np.concatenate([t for t, freq in scene_results]),
np.concatenate([freq for t, freq in scene_results]))
formatter = ticker.FuncFormatter(
lambda s, x: time.strftime('%M:%S', time.gmtime(s)))
ax.xaxis.set_major_formatter(formatter)
plt.show()
cv2.imshow('hsv_map', hsv_map)
cv2.namedWindow('hist', 0)
hist_scale = 10
def set_scale(val):
global hist_scale
hist_scale = val
cv2.createTrackbar('scale', 'hist', hist_scale, 32, set_scale)
try:
fn = sys.argv[1]
except:
fn = 0
cam = video.create_capture(
fn, fallback='synth:bg=../data/baboon.jpg:class=chess:noise=0.05')
while True:
flag, frame = cam.read()
cv2.imshow('camera', frame)
small = cv2.pyrDown(frame)
hsv = cv2.cvtColor(small, cv2.COLOR_BGR2HSV)
dark = hsv[..., 2] < 32
hsv[dark] = 0
h = cv2.calcHist([hsv], [0, 1], None, [180, 256], [0, 180, 0, 256])
h = np.clip(h * 0.005 * hist_scale, 0, 1)
vis = hsv_map * h[:, :, np.newaxis] / 255.0
cv2.imshow('hist', vis)
flow[:, :, 1] += np.arange(h)[:, np.newaxis]
#res = cv.remap(img, flow, None, cv.INTER_LINEAR)
res = cv.remap(img, flow, None, cv.INTER_NEAREST)
return res
counter = 1
if __name__ == '__main__':
import sys
print(__doc__)
try:
fn = sys.argv[1]
except IndexError:
fn = 0
cam2 = video.create_capture('walkExtract_720p.mp4')
cam = video.create_capture(fn)
ret, prev = cam.read()
ret1, prev1 = cam2.read()
prevgray = cv.cvtColor(prev, cv.COLOR_BGR2GRAY)
show_hsv = False
show_glitch = True
cur_glitch = prev1.copy()
while True:
ret, img = cam.read()
_, prev1 = cam2.read()
gray2 = cv.cvtColor(prev1, cv.COLOR_BGR2GRAY)
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
flow = cv.calcOpticalFlowFarneback(prevgray, gray2, gray2, 0.5, 3, 15,
3, 5, 1.2, 0)
import sys, getopt
print(__doc__)
args, video_src = getopt.getopt(sys.argv[1:], '', ['cascade=', 'nested-cascade='])
try:
video_src = video_src[0]
except:
video_src = 0
args = dict(args)
cascade_fn = args.get('--cascade', "/usr/local/share/OpenCV/haarcascades/haarcascade_frontalface_alt.xml")
nested_fn = args.get('--nested-cascade', "/usr/local/share/OpenCV/haarcascades/haarcascade_eye.xml")
cascade = cv2.CascadeClassifier(cascade_fn)
nested = cv2.CascadeClassifier(nested_fn)
cam = create_capture(video_src, fallback='synth:bg=../data/lena.jpg:noise=0.05')
while True:
ret, img = cam.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = cv2.equalizeHist(gray)
t = clock()
rects = detect(gray, cascade)
vis = img.copy()
draw_rects(vis, rects, (0, 255, 0))
if not nested.empty():
for x1, y1, x2, y2 in rects:
roi = gray[y1:y2, x1:x2]
vis_roi = vis[y1:y2, x1:x2]
subrects = detect(roi.copy(), nested)
colour = (b, g, r)
balldiameter = 10
bx = 100
by = balldiameter / 2
ballcolour = (255, 100, 100)
bx_direction = 2
by_direction = 3
#The face detector can have minimum and maximum sizes.
mn = 30 # minimum face width
mx = 500 # maximum face width
#these two lines set up the face detector and the video input
cascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
cam = video.create_capture(0)
fimg = cv2.imread("smily.png")
while True:
ret, img = cam.read()
height, width = img.shape[:2]
#print(height,width)
#n make a blank background instead of the input image
# output_image=np.zeros((height,width,3),np.uint8)
# output_image[:]=(0,0,255)
output_image = img
bx = bx + bx_direction
by = by + by_direction
if (bx > width or bx < 0):
args, video_src = getopt.getopt(sys.argv[1:], '',
['cascade=', 'nested-cascade='])
try:
video_src = video_src[0]
except:
video_src = 'synth:bg=../cpp/lena.jpg:noise=0.05'
args = dict(args)
cascade_fn = args.get(
'--cascade', "../../data/haarcascades/haarcascade_frontalface_alt.xml")
nested_fn = args.get('--nested-cascade',
"../../data/haarcascades/haarcascade_eye.xml")
cascade = cv2.CascadeClassifier(cascade_fn)
nested = cv2.CascadeClassifier(nested_fn)
cam = create_capture(video_src)
while True:
ret, img = cam.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = cv2.equalizeHist(gray)
t = clock()
rects = detect(gray, cascade)
vis = img.copy()
draw_rects(vis, rects, (0, 255, 0))
for x1, y1, x2, y2 in rects:
roi = gray[y1:y2, x1:x2]
vis_roi = vis[y1:y2, x1:x2]
subrects = detect(roi.copy(), nested)
draw_rects(vis_roi, subrects, (255, 0, 0))
import cv2.cv as cv
from video import create_capture
from common import clock, draw_str
#GUI
from pgu import gui as pgui, text
#Velleman board
from pyk8055 import *
#Arduino - only one of this and Velleman will be used. Probably...
import pyduino
#There should be a USB camera, but if not use the webcam.
try:
camera = create_capture(1)
ret, im = camera.read()
try:
camera2 = create_capture(2)
ret, im = camera2.read()
except:
camera2 = None
except:
camera = create_capture(0)
def playSound(fileName):
if not pygame.mixer.music.get_busy():
pygame.mixer.music.load(fileName)
pygame.mixer.music.play(1)
# main program
if __name__ == '__main__':
import sys
# print in the program shell window the text at the beginning of the file
print(__doc__)
# if there is no argument in the program invocation default to camera 0
try:
fn = sys.argv[1]
except:
fn = 0
# grab initial frame, create window
cv.waitKey(1) & 0xFF
cap = video.create_capture(fn)
ret, frame = cap.read()
frame_counter += 1
height, width, channels = frame.shape
prevFrame = frame.copy()
cv.namedWindow("video")
# Create video of Frame sequence -- define the codec and create VideoWriter object
fourcc = cv.VideoWriter_fourcc(*'XVID')
cols = np.int(cap.get(cv.CAP_PROP_FRAME_WIDTH))
rows = np.int(cap.get(cv.CAP_PROP_FRAME_HEIGHT))
vid_out = cv.VideoWriter('vid_out.avi', fourcc, 20.0, (cols, rows))
# Set up multiprocessing
threadn = cv.getNumberOfCPUs()
pool = ThreadPool(processes=threadn)
import numpy as np
import cv2
import video
try:
infn = sys.argv[1]
except:
infn = 0
cv2.namedWindow('foreground')
cap = video.create_capture(infn)
fgbg = cv2.BackgroundSubtractorMOG()
fn = 0
while (1):
ret, frame = cap.read()
fgmask = fgbg.apply(frame)
cv2.imshow('foreground', fgmask)
cv2.imshow('input', frame)
k = cv2.waitKey(5)
print "In frame {}".format(fn)
fn += 1
if k == 27:
break
cap.release()
cv2.destroyAllWindows()
ESC - exit
'''
import numpy as np
import cv2
import video
if __name__ == '__main__':
import sys
try:
video_src = sys.argv[1]
except:
video_src = 0
cam = video.create_capture(video_src)
mser = cv2.MSER_create()
while True:
ret, img = cam.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
vis = img.copy()
regions = mser.detectRegions(gray, None)
hulls = [cv2.convexHull(p.reshape(-1, 1, 2)) for p in regions]
cv2.polylines(vis, hulls, 1, (0, 255, 0))
cv2.imshow('img', vis)
if 0xFF & cv2.waitKey(5) == 27:
break
cv2.destroyAllWindows()
if __name__ == '__main__':
import sys, getopt
print help_message
args, video_src = getopt.getopt(sys.argv[1:], '', ['cascade=', 'nested-cascade='])
try: video_src = video_src[0]
except: video_src = 0
args = dict(args)
#cascade_fn = args.get('--cascade', "../../data/haarcascades/haarcascade_frontalface_default.xml")
cascade_fn = args.get('--cascade', "haarcascade_frontalface_default.xml")
cascade = cv2.CascadeClassifier(cascade_fn)
cam = create_capture(0)
x_est = np.zeros((n, 1))
p_est = np.identity(n)
w = 0
h = 0
allRoiHist = []
frist = 0
while(frist == '0'):
ret, img = cam.read()
vis = img.copy()
gray = cv2.cvtColor(vis, cv2.COLOR_BGR2GRAY)
gray = cv2.equalizeHist(gray)
rects = detect(gray, cascade)
if(format(len(rects)) == '1'):
frist = 1
cv2.imshow('facedetect', vis)
def __init__(self, video_src):
self.track_len = 10
self.detect_interval = 5
self.tracks = []
self.cam = video.create_capture(video_src)
self.frame_idx = 0
def run(self):
standup_frame_cnt = 0
sitdown_frame_cnt = 0
#containsEnoughMotion = False
#detected = False
use_spatial_propagation = False
use_temporal_propagation = True
#inst = cv2.optflow.createOptFlow_DIS(cv2.optflow.DISOPTICAL_FLOW_PRESET_MEDIUM)
#inst = cv2.optflow.createOptFlow_DIS(cv2.optflow.DISOPTICAL_FLOW_PRESET_FAST)
inst = cv2.optflow.createOptFlow_DIS(
cv2.optflow.DISOPTICAL_FLOW_PRESET_ULTRAFAST)
inst.setUseSpatialPropagation(use_spatial_propagation)
flow = None
self.prev_gray = None
delayTime = 1
jump = 20
badframeCnt = 0
while True:
ret, frame = self.cam.read()
if not ret:
badframeCnt = badframeCnt + 1
if badframeCnt > 3:
break
else:
continue
#########
# Step 0: preprocessing
#########
frame = cv2.resize(frame, (frame.shape[1] / 2, frame.shape[0] / 2))
vis = frame.copy()
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
#cv2.imshow("before blur", frame_gray)
frame_gray_blur = cv2.medianBlur(frame_gray, 7)
#cv2.imshow("after blur", frame_gray)
#########
# Step 1: BackgroundSubtractor
#########
fgmask = self.fgbg.apply(frame_gray_blur, 0.7)
#fgmask = cv2.medianBlur(fgmask, 7)
cv2.imshow("fgmask", fgmask)
#########
# Step 2: morphology
#########
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
closed = cv2.morphologyEx(fgmask, cv2.MORPH_CLOSE, kernel)
closed = cv2.morphologyEx(closed, cv2.MORPH_OPEN, kernel)
#########
# Step 3: contour and hull
#########
_, contours0, hierarchy = cv2.findContours(closed.copy(),
cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
contours = [cv2.approxPolyDP(cnt, 3, True) for cnt in contours0]
# filter contours
minLength = 2
minArea = 2
new_contours = []
for index, c in enumerate(contours):
if len(c) > minLength and cv2.contourArea(
c) > minArea: # and hierarchy[index] is not None:
new_contours.append(c)
contours = new_contours
# get hulls
hulls = []
for contour in contours:
hull = cv2.convexHull(contour)
hulls.append(hull)
# merge nest hulls
hullMask = np.zeros((closed.shape[0], closed.shape[1], 1),
np.uint8)
cv2.drawContours(hullMask, hulls, -1, 255, 1)
_, contours1, hierarchy = cv2.findContours(hullMask.copy(),
cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
hulls = []
for contour in contours1:
hull = cv2.convexHull(contour)
hulls.append(hull)
cv2.drawContours(hullMask, hulls, -1, 255, -1)
#cv2.imshow("editHull", hullMask)
# get centers of contours
centers = []
for hull in hulls:
M = cv2.moments(hull)
cx = int(M['m10'] / M['m00'])
cy = int(M['m01'] / M['m00'])
centers.append((cx, cy))
# label hulls
hullLables = [-1 for i in range(len(hulls))]
if len(self.prevHulls) == 0:
hullLables = [i for i in range(len(hulls))]
else:
for index, hull in enumerate(hulls):
cx = centers[index][0]
cy = centers[index][1]
if self.prevHullMask[cy][cx] != 0:
# find corresponding hull
minDist = 10000
prevIndex = 0
for i, c in enumerate(self.prevCenters):
dist = abs(int(c[0]) -
int(cx)) + abs(int(c[1]) - int(cy))
if dist < minDist:
minDist = dist
prevIndex = i
hullLables[index] = self.prevHullLabels[prevIndex]
else:
label = 0
while True:
if label in self.prevHullLabels or label in hullLables:
label = label + 1
else:
hullLables[index] = label
break
########
# conpute optflow for each hull
########
flows = []
rois = []
if self.prev_gray is not None:
whole_flow = inst.calc(
self.prev_gray, frame_gray,
None) # if use only roi for compute dense optflow?
vis = draw_flow(vis, whole_flow)
for h in hulls:
x, y, w, h = cv2.boundingRect(h)
rois.append((x, y, w, h))
flows.append(whole_flow[y:y + h, x:x + w])
#if flow is not None and use_temporal_propagation:
#warp previous flow to get an initial approximation for the current flow:
# flow = inst.calc(self.prev_gray, frame_gray, warp_flow(flow,flow))
#elif self.prev_gray is not None:
# flow = inst.calc(self.prev_gray, frame_gray, None)
########
# classify using optflow
########
flags = []
for index, flow in enumerate(flows):
fx = np.mean(flow[:, :, 0])
fy = np.mean(flow[:, :, 1])
flags.append(fy < self.fy_threshold)
#if fy < -1.0:
#draw_str(vis, (rois[index][0], rois[index][1]), "%.2f"%fx, (255,0,0))
#draw_str(vis, (rois[index][0], rois[index][1]-10), "%.2f"%fy, (0,255,0))
########
# Draw result
########
for i, hull in enumerate(hulls):
if self.prev_gray is not None:
cv2.drawContours(vis, [hull], -1, color[flags[i]], 2)
#for i, hull in enumerate(hulls):
# cv2.drawContours( vis, [hull], -1, color[hullLables[i]%len(color)], 2)
for index, flow in enumerate(flows):
flow = flow * 5
#vis = draw_flow_roi(vis, flow, rois[index])
'''
prevHullMask = np.zeros((closed.shape[0], closed.shape[1], 3), np.uint8)
if len(self.prevHulls) != 0:
cv2.drawContours( prevHullMask, self.prevHulls, -1, (255, 255, 255), -1)
for i, c in enumerate(centers):
cv2.drawContours(prevHullMask, [hulls[i]], 0, color[hullLables[i]], 2)
cv2.circle(prevHullMask, c, 1, color[hullLables[i]], 2)
draw_str(prevHullMask, c, "%d"%hullLables[i], color[hullLables[i]])
for i, c in enumerate(self.prevCenters):
draw_str(prevHullMask, (c[0]+10, c[1]), "%d"%self.prevHullLabels[i])
cv2.imshow("hulls", prevHullMask)
'''
#cv2.imshow('mask', fgmask)
#cv2.imshow("close", closed)
#cv2.imshow("contour", hullMask)
#cv2.drawContours( vis, hulls, -1, (128,0,255), 2)
#cv2.drawContours( vis, contours, -1, (255,255,255), 1)
#cv2.drawContours( vis, new_contours, -1, (255,0,255), 1)
cv2.imshow('lk_track', vis)
self.frame_idx += 1
self.prev_gray = frame_gray
self.prevHulls = hulls
self.prevHullMask = hullMask
self.prevHullLabels = hullLables
self.prevCenters = centers
self.ch = 0xFF & cv2.waitKey(delayTime) # 20
ch = self.ch
# Esc
if ch == 27:
break
# faster
if ch == ord('g'):
delayTime = 1
# fast
if ch == ord('f'):
delayTime = 20
if ch == ord('1'):
delayTime = 100
if ch == ord('2'):
delayTime = 300
if ch == ord('3'):
delayTime = 600
# slow
if ch == ord('s'):
delayTime = 1000
# replay
if ch == ord('r'):
self.cam = video.create_capture(self.video_src)
# stop
if ch == ord('d'):
ch = 0xFF & cv2.waitKey(delayTime)
while ch != ord('d'):
ch = 0xFF & cv2.waitKey(delayTime)
continue
# move foreward >
if ch == 82:
jump = jump + 20
print("jupm speed: ", jump)
if ch == 84:
jump = (jump - 20 > 0) and (jump - 20) or jump
print("jupm speed: ", jump)
if ch == 83:
for i in range(jump):
self.cam.read()
def __init__(self, video_src):
self.cam = video.create_capture(video_src)
self.p0 = None
self.use_ransac = True
search_response = urllib.urlopen(url)
search_results = search_response.read()
results = json.loads(search_results)
data = results['responseData']
if debug[1]: print "data is {}".format(data)
try:
hits = int(data['cursor']['estimatedResultCount'])
except (KeyError, TypeError) as e:
hits = sum(map(len, query))
if debug[1]: print "Query is {}, hits are {}".format(query, hits)
return hits
#TODO: For performance, make multithreaded
if __name__ == '__main__':
cam = video.create_capture(0) #cv2.VideoCapture(0)
ret, img = cam.read()
takephoto = False
imgdir = "./img/" #out.jpg" #path of outputfile
textFile = "outtext/out"
textFileExt = "outtext/out.txt"
imgfile = imgdir + "out.jpg"
start = False #Used to denote start taking pictures
startBuffer = 0
startBufferMax = 10 #step size
cand = [] #candidate texts
maxHits = 0
startTime = None
def getVideoFeatures(video_path):
'''
return 光流均值，光流标准差，颜色冷暖，颜色轻重，颜色活跃度，颜色柔和度，暗色比例，亮色比例，饱和度，颜色能量，颜色方差
'''
optical_flow_list = []
cam = video.create_capture(video_path)
ret, prev = cam.read()
# 2次图形下采样
prev = cv.pyrDown(prev)
prev = cv.pyrDown(prev)
prevgray = cv.cvtColor(prev, cv.COLOR_BGR2GRAY)
video_warmc_total = 0
video_heavyl_total = 0
video_activep_total = 0
video_hards_total = 0
video_darkProportion_total = 0
video_lightPropertion_total = 0
video_saturation_total = 0
video_color_energy_total = 0
video_color_std_total = 0
count = 0
n = 0
SAMPLE_BAND = 10
while True:
ret, img = cam.read()
if not ret:
break
count += 1
# 每x帧采一次样
print(count)
if count % SAMPLE_BAND != 0 and count % SAMPLE_BAND != 1:
continue
# 图像下采样
img = cv.pyrDown(img)
img = cv.pyrDown(img)
# 获得该帧的运动矢量
# https://docs.opencv.org/master/d7/d8b/tutorial_py_lucas_kanade.html
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
if count % SAMPLE_BAND == 1:
flow = cv.calcOpticalFlowFarneback(prevgray, gray, None, 0.5, 3,
150, 3, 5, 1.2, 0)
optical_flow_list.append(flow)
# 获得该帧的颜色特征
video_warmc, video_heavyl, video_activep, video_hards, video_darkProportion, video_lightPropertion, video_saturation, video_color_energy, video_color_std = getFrameFeatures.getColorFeatures(
img)
video_warmc_total += video_warmc
video_heavyl_total += video_heavyl
video_activep_total += video_activep
video_hards_total += video_hards
video_darkProportion_total += video_darkProportion
video_lightPropertion_total += video_lightPropertion
video_saturation_total += video_saturation
video_color_energy_total += video_color_energy
video_color_std_total += video_color_std
n += 1
prevgray = gray
cam.release()
optical_flow_array = np.array(optical_flow_list)
print("optical_flow_array.shape: ", optical_flow_array.shape)
optical_flow_mean = np.mean(optical_flow_array, axis=0)
print("optical_flow_mean.shape: ", optical_flow_mean.shape)
optical_flow_std = np.std(optical_flow_array, axis=0)
print("optical_flow_std.shape: ", optical_flow_std.shape)
video_optical_flow_mean = optical_flow_mean.mean(axis=0).mean(axis=0)
video_optical_flow_std = optical_flow_std.mean(axis=0).mean(axis=0)
video_warmc_total /= n
video_heavyl_total /= n
video_activep_total /= n
video_hards_total /= n
video_darkProportion_total /= n
video_lightPropertion_total /= n
video_saturation_total /= n
video_color_energy_total /= n
video_color_std_total /= n
x, y = video_optical_flow_mean
a, b = video_optical_flow_std
return x * x + y * y, (
abs(a) + abs(b)
) / 2, video_warmc_total, video_heavyl_total, video_activep_total, video_hards_total, video_darkProportion_total, video_lightPropertion_total, video_saturation_total, video_color_energy_total, video_color_std_total
def image_cap():
d=[]
print(__doc__)
try:
fn = sys.argv[1]
except:
fn = 0
cap = video.create_capture(fn)
cap.set(cv.CAP_PROP_FPS, 12)
def process_frame(frame, t0):
# some intensive computation...
#frame = cv.medianBlur(frame, 19)
#frame = cv.medianBlur(frame, 19)
return frame, t0
threadn = cv.getNumberOfCPUs()
pool = ThreadPool(processes = threadn)
pending = deque()
threaded_mode = True
latency = StatValue()
frame_interval = StatValue()
last_frame_time = clock()
while True:
while len(pending) > 0 and pending[0].ready():
res, t0 = pending.popleft().get()
latency.update(clock() - t0)
draw_str(res, (20, 20), "threaded : " + str(threaded_mode))
draw_str(res, (20, 40), "latency : %.1f ms" % (latency.value*1000))
draw_str(res, (20, 60), "frame interval : %.1f ms" % (frame_interval.value*1000))
res=cv.resize(res,(176,100))
cv2_im = cv.cvtColor(res, cv.COLOR_BGR2RGB)
pil_im = Image.fromarray(cv2_im)
d.append(np.array(pil_im))
if len(d)==32:
t1 = data_input(d[0:16])
t2 = data_input(d[16:32])
in_x = np.array([t1, t2])
in_x = np.reshape(in_x, (2, 16, 128, 128, 3))
start = time.clock()
#p = Pool(1)
#p.map(evaluate, in_x)
evaluate(in_x)
elapsed = time.clock()
elapsed = elapsed - start
print("Time spent in (function name) is: ", elapsed)
d=[]
cv.imshow('threaded video', res)
if len(pending) < threadn:
ret, frame = cap.read()
t = clock()
frame_interval.update(t - last_frame_time)
last_frame_time = t
if threaded_mode:
task = pool.apply_async(process_frame, (frame.copy(), t))
else:
task = DummyTask(process_frame(frame, t))
pending.append(task)
ch = cv.waitKey(1)
if ch == ord(' '):
threaded_mode = not threaded_mode
if ch == 27:
break
cv.destroyAllWindows()
def __init__(self, video_src):
self.cam = video.create_capture(video_src)
args, video_src = getopt.getopt(sys.argv[1:], '',
['cascade=', 'nested-cascade='])
#-----------------------------------
#Selecting video source(file/camera)
#-----------------------------------
try:
video_src = video_src[0]
except:
#fn = raw_input("Enter Camera/Video (1/0): ")
fn = 0 #int(fn)
if fn == 1:
print "Video File "
cam = create_capture(
'vania.mp4', fallback='synth:bg=../data/lena.jpg:noise=0.05')
elif fn == 0:
print "Web Camera"
cam = create_capture(
fn, fallback='synth:bg=../data/lena.jpg:noise=0.05')
#------------------------
#Defining various cascade
#------------------------
args = dict(args)
cascade_fn = args.get(
'--cascade',
"../../data/haarcascades/haarcascade_frontalface_alt_tree.xml")
nested_fn = args.get('--nested-cascade', "cascade/open.xml")
