def get_motions(f, fMask, thickness=1, color=(170, 170, 170)):
'''
Iterates over the contours in a mask and draws a bounding box
around the ones that encompas an area greater than a threshold.
This will return an image of just the draw bock (black bg), and
also an array of the box points.
'''
rects_mot = []
f_rects = np.zeros(f.shape, np.uint8)
# get contours
if imutils.is_cv3():
_, cnts, hierarchy = cv2.findContours(
fMask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
elif imutils.is_cv2():
cnts, hierarchy = cv2.findContours(
fMask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# loop over the contours
for c in cnts:
# if the contour is too small, ignore it
if cv2.contourArea(c) < contourThresh:
continue
if imutils.is_cv3():
box = cv2.boxPoints(cv2.minAreaRect(c))
elif imutils.is_cv2():
box = cv2.cv.BoxPoints(cv2.minAreaRect(c))
box = np.int0(box)
cv2.drawContours(f_rects, [box], 0, color, thickness)
rects_mot.append(cv2.boundingRect(c))
return f_rects, rects_mot
def contours():
# load the image, convert it to grayscale, and blur it slightly
image = cv2.imread("e.jpg")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(image, (5, 5), 0)
# threshold the image, then perform a series of erosions +
# dilations to remove any small regions of noise
thresh = cv2.threshold(gray, 45, 255, cv2.THRESH_BINARY)[1]
thresh = cv2.erode(thresh, None, iterations=2)
thresh = cv2.dilate(thresh, None, iterations=2)
# find contours in thresholded image, then grab the largest
# one
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
c = max(cnts, key=cv2.contourArea)
extLeft = tuple(c[c[:, :, 0].argmin()][0])
extRight = tuple(c[c[:, :, 0].argmax()][0])
extTop = tuple(c[c[:, :, 1].argmin()][0])
extBot = tuple(c[c[:, :, 1].argmax()][0])
cv2.drawContours(image, [c], -1, (0, 255, 255), 2)
cv2.circle(image, extLeft, 8, (0, 0, 255), -1)
cv2.circle(image, extRight, 8, (0, 255, 0), -1)
cv2.circle(image, extTop, 8, (255, 0, 0), -1)
cv2.circle(image, extBot, 8, (255, 255, 0), -1)
# show the output image
cv2.imwrite("/home/anushka/Documents/f.jpg", image)
cv2.waitKey(0)
def extractFeatures(self, image):
# convert the image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# detect and extract features from the image
# --> need SIFT because the mosaic can be made from camera that takes images with different Scale and Rotation
# if we are using opencv 2.4.9
if imutils.is_cv2():
# the default parameters for cv2.SIFT()
nfeatures = 0
nOctaveLayers = 3
contrastTreshold = 0.04
edgeTreshold = 10
sigma = 1.6
sift = cv2.SIFT(nfeatures, nOctaveLayers, contrastTreshold, edgeTreshold, sigma)
(kps, features) = sift.detectAndCompute(gray, None) # (image, None)
# check to see if we are using OpenCV 3
elif imutils.is_cv3():
descriptor = cv2.xfeatures2d.SIFT_create()
(kps, features) = descriptor.detectAndCompute(gray, None)
self.logger.info("Found {} keypoints in frame".format(len(kps)))
# convert the keypoints from KeyPoint objects to np
kps = np.float32([kp.pt for kp in kps])
# return a tuple of keypoints and features
return (kps, features)
def recognize(images_path, training_path, pass_info = False):
if "camera_1" in training_path:
threshhold =75
area = 110
elif "camera_2" in training_path:
threshhold =151
area = 70
elif "camera_3" in training_path:
threshhold = 151
area = 70
####### training part ###############
samples = np.loadtxt(training_path+'generalsamples.data', np.float32)
responses = np.loadtxt(training_path+'generalresponses.data', np.float32)
responses = responses.reshape((responses.size, 1))
model = cv2.ml.KNearest_create()
model.train(samples, cv2.ml.ROW_SAMPLE, responses)
############################# testing part #########################
image = cv2.imread(images_path)
out = np.zeros(image.shape, np.uint8)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
thresh = cv2.threshold(blurred, threshhold, 255, cv2.THRESH_BINARY)[1]#dont change 151
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
# new = cv2.drawContours(image, cnts, -1, (0, 255, 0), 3)
# cv2.imshow('im', new)
# cv2.imshow('out', out)
# cv2.waitKey(0)
content = []
for c in cnts:
# compute the center of the contour
M = cv2.moments(c)
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
# draw the contour and center of the shape on the image
cv2.circle(image, (cX, cY), 7, (255, 255, 255), -1)
[x, y, w, h] = cv2.boundingRect(c)
if cv2.contourArea(c)>area: #the higher the threshold, the smaller the area
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
roi = thresh[y:y + h, x:x + w]
roismall = cv2.resize(roi, (10, 10))
roismall = roismall.reshape((1, 100))
roismall = np.float32(roismall)
retval, results, neigh_resp, dists = model.findNearest(roismall, k=1)
string = str(int((results[0][0])))
if pass_info: #returns temp between C and F symbols (camera 1)
content.append((string, [cX,cY]))
else:
if string != str(42):
cv2.putText(out, string, (x, y + h), 0, 1, (0, 255, 0))
content.append(string)
content.reverse()
return content
def vidFeed(self):
self.takeFrame()
# draw reference points and lines
cv2.line(self.image, (self.widthHalf, 0), (self.widthHalf, self.height), (0, 255, 0), 2) # center line
cv2.line(self.image, (0, self.heightHalf), (self.width, self.widthHalf), (0, 255, 0), 2) # center line
cv2.circle(self.image, (self.widthHalf, self.heightHalf), 6, (200, 255, 0), -1) # CenterPoint
self.roiMask()
# Process the image
gray = cv2.cvtColor(self.masked_image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
th = cv2.threshold(gray, 127,255, cv2.THRESH_BINARY )[1]
th = cv2.erode(th, None, iterations=2)
th = cv2.dilate(th, None, iterations=2)
#finding the coutours from
cnts = cv2.findContours(th.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
self.thresh = th
if cnts is not None:
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
c = max(cnts, key=cv2.contourArea)
self.rohitC = c
#find the moments of image
M = cv2.moments(c)
self.cX = int((M["m10"] / M["m00"]) )
self.cY = int((M["m01"] / M["m00"]) )
#bounding Rectangle
self.boux, self.bouy, self.bouw, self.bouh = cv2.boundingRect(c)
cv2.rectangle(self.image, (self.boux, self.bouy), (self.boux + self.bouw, self.bouy + self.bouh), (100, 100, 0), 2)
cv2.putText(self.image, str(self.boux) + str(" ") + str(self.bouy) + str(" ")+ str(self.boux+self.bouw) + str(" ")+ str(self.bouy+self.bouh), (self.width - 400, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (20, 255, 255), 2)
#drawpoints of countours
cv2.circle(self.image, (self.cX,self.cY), 6, (100, 200, 255), -1) # red
extLeft = tuple(c[c[:, :, 0].argmin()][0])
extRight = tuple(c[c[:, :, 0].argmax()][0])
extTop = tuple(c[c[:, :, 1].argmin()][0])
extBot = tuple(c[c[:, :, 1].argmax()][0])
centerContoursX = self.cX
error = self.centerFrameX - centerContoursX
print(error)
self.control(error)
# draw the outline of the object, then draw each of the
# extreme points, where the left-most is red, right-most
# is green, top-most is blue, and bottom-most is teal
cv2.drawContours(self.image, [c], -1, (0, 255, 255), 2)
cv2.circle(self.image, extLeft, 6, (0, 0, 255), -1) # red
cv2.circle(self.image, extRight, 6, (0, 255, 0), -1) # green
cv2.circle(self.image, extTop, 6, (255, 0, 0), -1) # blue
cv2.circle(self.image, extBot, 6, (255, 255, 0), -1) # bottom
def colorShapeFromImg(request):
if request.method == 'POST':
# load the image and resize it to a smaller factor so that
# the shapes can be approximated better
image = cv2.imdecode(np.fromstring(request.FILES['file'].read(), np.uint8), cv2.CV_LOAD_IMAGE_UNCHANGED)
resized = imutils.resize(image, width=300)
ratio = image.shape[0] / float(resized.shape[0])
# blur the resized image slightly, then convert it to both
# grayscale and the L*a*b* color spaces
blurred = cv2.GaussianBlur(resized, (5, 5), 0)
gray = cv2.cvtColor(blurred, cv2.COLOR_BGR2GRAY)
lab = cv2.cvtColor(blurred, cv2.COLOR_BGR2LAB)
thresh = cv2.threshold(gray, 60, 255, cv2.THRESH_BINARY)[1]
# find contours in the thresholded image
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
# initialize the shape detector and color labeler
sd = ShapeDetector()
cl = ColorLabeler()
response = {}
# loop over the contours
for c in cnts:
# compute the center of the contour
M = cv2.moments(c)
cX = int((M["m10"] / M["m00"]) * ratio)
cY = int((M["m01"] / M["m00"]) * ratio)
# detect the shape of the contour and label the color
shape = sd.detect(c)
color = cl.label(lab, c)
response['shape']=shape
response['color']=color
# multiply the contour (x, y)-coordinates by the resize ratio,
# then draw the contours and the name of the shape and labeled
# color on the image
c=c.astype(np.float_)
c *= ratio
c=c.astype(np.int32)
text = "{} {}".format(color, shape)
cv2.drawContours(image, [c], -1, (0, 255, 0), 2)
cv2.putText(image, text, (cX, cY),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
return response
result = 'result.png'
cv2.imwrite(result, image)
result_data = open(result, "rb").read()
return HttpResponse(result_data, content_type="image/png")
def draw_str(dst, target, s, fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=0.5, color=(255, 255, 255), bgcolor=(0, 0, 0), thickness=1):
x, y = target
if imutils.is_cv3():
line_type = cv2.LINE_AA
if imutils.is_cv2():
line_type = cv2.cv.CV_AA
cv2.putText(dst, s, (x + 1, y + 1), fontFace, fontScale,
bgcolor, thickness=thickness + 1, lineType=line_type)
cv2.putText(dst, s, (x, y), fontFace, fontScale, color,
thickness=thickness, lineType=line_type)
def trapezoid_mask(trapezoid, blur_radius, threshold, path, out_dir = './tmp/'):
if not os.path.exists(out_dir):
os.makedirs(out_dir)
result = {}
result["S"] = 0
result["L"] = 0
result["R"] = 0
# failures = {}
# failures["S"] = []
# failures["L"] = []
# failures["R"] = []
#print("[TrapezoidMask] Processing " + path)
img = cv2.imread(path,0)
# Darken
img = adjust_gamma(img)
# Mask
mask = np.zeros(img.shape, dtype=np.uint8)
roi_corners = np.array([trapezoid], dtype=np.int32)
channel_count = 1 # i.e. 3 or 4 depending on your image
ignore_mask_color = (255,)*channel_count
cv2.fillPoly(mask, roi_corners, ignore_mask_color)
masked_image = cv2.bitwise_and(img, mask)
basename = os.path.basename(path)
#cv2.imwrite(out_dir + basename.replace('.png', '.masked.png'), masked_image)
# Find connected regions
path = out_dir + basename.replace('.png', '.connected.png')
labeled, n = connected_regions(masked_image, path, blur_radius, threshold)
# Find extreme points on contour
thresh = cv2.threshold(masked_image, 45, 255, cv2.THRESH_BINARY)[1]
#thresh = cv2.erode(thresh, None, iterations=2)
#thresh = cv2.dilate(thresh, None, iterations=2)
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
# Classify
if len(cnts) > 0:
c = max(cnts, key=cv2.contourArea)
extLeft = tuple(c[c[:, :, 0].argmin()][0])
extRight = tuple(c[c[:, :, 0].argmax()][0])
if extLeft[1] < extRight[1]: # Left curve
# print("[TrapezoidMask] " + str(n) + " parts and " + str(extLeft) + " < " + str(extRight) + ": this is a LEFT curve")
result["L"] += 1
#if basename[0] != 'L': failures["L"].append(basename)
else: # Right curve
#print("[TrapezoidMask] " + str(n) + " parts and " + str(extLeft) + " < " + str(extRight) + ": this is a RIGHT curve")
result["R"] += 1
#if basename[0] != 'R': failures["R"].append(basename)
else:
#print("[TrapezoidMask] " + str(n) + " parts and no contours: this is a STRAIGHT lane")
result["S"] += 1
#if basename[0] != 'S': failures["S"].append(basename)
#print("[TrapezoidMask] Intermediate result: " + str(result))
return result #, failures
def __init__(self, accumWeight=0.5, deltaThresh=5, minArea=5000): # determine the OpenCV version, followed by storing the # the frame accumulation weight, the fixed threshold for # the delta image, and finally the minimum area required # for "motion" to be reported self.isv2 = imutils.is_cv2() self.accumWeight = accumWeight self.deltaThresh = deltaThresh self.minArea = minArea # initialize the average image for motion detection self.avg = None
def remove_blobs(full_image, resized_image, gray, ratio, show_plots=False):
if show_plots:
cv2.imshow("Thresh2", gray)
cv2.waitKey(0)
cnts = cv2.findContours(gray.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
# loop over the contours
hsv = cv2.cvtColor(resized_image, cv2.COLOR_BGR2HSV)
def shapes2(request):
# load the image and resize it to a smaller factor so that
# the shapes can be approximated better
image = cv2.imread('LcKe78Bca.png')
resized = imutils.resize(image, width=300)
ratio = image.shape[0] / float(resized.shape[0])
# convert the resized image to grayscale, blur it slightly,
# and threshold it
gray = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
thresh = cv2.threshold(blurred, 60, 255, cv2.THRESH_BINARY)[1]
# find contours in the thresholded image and initialize the
# shape detector
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
sd = ShapeDetector()
# loop over the contours
for c in cnts:
# compute the center of the contour, then detect the name of the
# shape using only the contour
M = cv2.moments(c)
cX = int((M["m10"] / M["m00"]) * ratio)
cY = int((M["m01"] / M["m00"]) * ratio)
shape = sd.detect(c)
# multiply the contour (x, y)-coordinates by the resize ratio,
# then draw the contours and the name of the shape on the image
c=c.astype(np.float_)
c *= ratio
c=c.astype(np.int32)
cv2.drawContours(image, [c], -1, (0, 255, 0), 2)
cv2.putText(image, shape, (cX, cY), cv2.FONT_HERSHEY_SIMPLEX,
0.5, (60, 255, 123), 2)
result = 'result.png'
cv2.imwrite(result, image)
result_data = open(result, "rb").read()
return HttpResponse(result_data, content_type="image/png")
def get_contours(image, to_hsv=None, thresh=None, smooth=None, edge=None):
"""
A function to get contours from a BGR image using opencv with optional thresholding
and smoothing first.
Args:
to_hsv: if this value is set to True, the BGR image will be transformed
to hsv colorspace. Default leaves it as BGR colorspace.
thresh: If thresh is given, a threshold will be applied to the image.
thresh should be a 2 x 3 numpy array, where the first row is the
lower bound and the second row is the upper bound of the threshold.
smooth: If smooth is given, cv2's bilateralFilter will be applied to
the image. smooth should be a 3-vector, indicating the arguments
for the smoothing function.
edge: If edge is given, cv2's canny edge detector will be used on the
image. It should consist of a 2-vector signifying the lower bound
and upper bound.
"""
if to_hsv is True:
image=cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
if thresh is not None:
if np.shape(thresh)!=(2, 3):
raise TypeError('thresh must be a 2x3 array.')
else:
mask=cv2.inRange(image, thresh[0, :].astype(int), thresh[1, :].astype(int))
if smooth is not None:
if 'mask' in locals():
mask=cv2.bilateralFilter(np.uint8(mask), smooth[0], smooth[1], smooth[2])
else:
mask=cv2.bilateralFilter(np.uint8(image), smooth[0], smooth[1], smooth[2])
if edge is not None:
if 'mask' in locals():
mask=cv2.Canny(mask, edge[0], edge[1])
else:
mask=cv2.Canny(image, edge[0], edge[1])
cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
return cnts[0] if imutils.is_cv2() else cnts[1]
def returnPoints(image):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
cl1 = clahe.apply(gray)
thresh = cv2.Canny(cl1, 50, 100)
thresh = cv2.dilate(thresh, None, iterations=3)
thresh = cv2.erode(thresh, None, iterations=3)
cv2.bitwise_not ( thresh, thresh );
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
(cnts, _) = contours.sort_contours(cnts)
pixelsPerMetric = None
for c in cnts:
# if the contour is not sufficiently large, ignore it
if (cv2.contourArea(c) < 300 or cv2.contourArea(c) > 400):
continue
# compute the rotated bounding box of the contour
'''orig = image.copy()
box = cv2.minAreaRect(c)
box = cv2.cv.BoxPoints(box) if imutils.is_cv2() else cv2.boxPoints(box)
box = np.array(box, dtype="int")
box = perspective.order_points(box)
cv2.drawContours(orig, [box.astype("int")], -1, (0, 255, 0), 2)
# loop over the original points and draw them
for (x, y) in box:
cv2.circle(orig, (int(x), int(y)), 5, (0, 0, 255), -1)'''
M = cv2.moments(c)
cX = int(M["m10"] / M["m00"]) if (M["m00"]!=0) else int(M["m10"])
cY = int(M["m01"] / M["m00"]) if (M["m00"]!=0) else int(M["m01"])
cv2.drawContours(image, [c], -1, (0, 255, 0), 2)
#return orig
return image
def countcotours(img):
nc=0
dir="img/"+img+".png"
image = cv2.imread(dir)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.imshow("Image", gray)
cv2.waitKey(0)
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
cv2.imshow("Image", blurred)
cv2.waitKey(0)
thresh = cv2.threshold(blurred, 60, 255, cv2.THRESH_BINARY)[1]
cv2.imshow("Image", thresh)
cv2.waitKey(0)
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
sd = ShapeDetector()
for c in cnts:
# compute the center of the contour
if cv2.contourArea(c)<800:
continue
else:
M = cv2.moments(c)
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
shape = sd.detect(c)
#if(shape=="square" or shape=="rectangle"):
if 1==1:
cv2.drawContours(image, [c], -1, (0, 255, 0), 2)
cv2.circle(image, (cX, cY), 7, (255, 255, 255), -1)
cv2.putText(image, "center", (cX - 20, cY - 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
nc=nc+1
cv2.imshow("Image", image)
cv2.waitKey(0)
return nc
def findcolors(nc,im):
cont=0
image = cv2.imread(im)
blurred = cv2.GaussianBlur(image, (5, 5), 0)
gray = cv2.cvtColor(blurred, cv2.COLOR_BGR2GRAY)
lab = cv2.cvtColor(blurred, cv2.COLOR_BGR2LAB)
thresh = cv2.threshold(gray, 60, 255, cv2.THRESH_BINARY)[1]
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
cl = ColorLabeler()
sd = ShapeDetector()
for c in cnts:
if cv2.contourArea(c)< 800:
continue
else:
M = cv2.moments(c)
cX = int((M["m10"] / M["m00"]))
cY = int((M["m01"] / M["m00"]))
shape = sd.detect(c)
color = cl.label(lab, c)
print(shape)
print(color)
if (shape=="square" or shape=="rectangle"):
if (color==nc):
text = "{}".format(color)
cv2.drawContours(image, [c], -1, (0, 255, 0), 2)
cv2.putText(image, text, (cX, cY),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
cont=cont+1
cv2.imshow("Image", image)
cv2.waitKey(0)
return cont
def get_cropped_img(image):
grey = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
grey = cv2.GaussianBlur(grey, (7, 7), 0)
edged = cv2.Canny(grey, 50, 100)
edged = cv2.dilate(edged, None, iterations=1)
edged = cv2.erode(edged, None, iterations=1)
# cv2.imshow('edged', edged)
# find contours in the edge map
cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
largest_contour_area = 1000000000
largest_contour = None
for (i, c) in enumerate(cnts):
# if the contour is not sufficiently large, ignore it
if cv2.contourArea(c) < 100:
continue
if cv2.contourArea(c) < largest_contourArea:
largest_contour_area = cv2.contourArea(c)
largest_contour = c
# bounding_box = cv2.boundingRect(c)
# x1, y1, h, w = bounding_box
# cv2.drawContours(image, c, -1, (0, 255, 0), 20)
# # cv2.rectangle(image, (x1, y1), (x1 + h, y1 + w), (0, 255, 0), 2)
# # image = image[bounding_box[2]:bounding_box[0], bounding_box[3]:bounding_box[1]]
if largest_contour is not None:
bounding_box = cv2.boundingRect(largest_contour)
x1, y1, h, w = bounding_box
# cv2.drawContours(image, bounding_box, -1, (0, 255, 0), -1)
cv2.rectangle(image, (x1, y1), (x1+ h, y1+w), (255, 255, 0), 5)
image = image[y1:y1+w, x1:x1+h]
return image
return None
def scaleAndCrop(self, img, out_width):
# scale
resized = imutils.resize(img, width=out_width)
# crop where?
grey = cv2.cvtColor(resized,cv2.COLOR_BGR2GRAY)
[ret, thresh] = cv2.threshold(grey,10,255,cv2.THRESH_BINARY)
#TODO: solve isssue : x,y,w,h = cv2.boundingRect(cnt) , TypeError: points is not a numpy array, neither a scalar
# check to see if we are using OpenCV 2.X
if imutils.is_cv2():
[contours, hierarchy] = cv2.findContours(thresh, 1, 2)
cnt = contours[0]
# check to see if we are using OpenCV 3
elif imutils.is_cv3():
(_, cnts, _) = cv2.findContours(thresh, 1, 2)
cnt = cnts[0]
#x,y,w,h = cv2.boundingRect(cnt)
[x, y, w, h] = cv2.boundingRect(cnt)
crop = resized[y:y + h, x:x + w]
return crop
def preProsses(self, frame):
resized = imutils.resize(frame, width=300)
ratio = frame.shape[0] / float(resized.shape[0])
print(ratio)
gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
blurresd = cv2.GaussianBlur(gray_frame, (5,5), 0)
#bilateral = cv2.bilateralFilter(gray_frame, 9, 90, 90)
thresh = cv2.threshold(blurresd, 70, 255, cv2.THRESH_BINARY)[1]
#canny = cv2.Canny(blurresd, 50, 200)
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
for c in cnts:
M = cv2.moments(c)
print(M)
cX = int((M["m10"] / M["m00"]) * ratio)
cY = int((M["m01"] / M["m00"]) * ratio)
shape = self.detector(c)
c = ratio
cv2.drawContours(frame, c[0], -1, (0, 255, 0), 2)
cv2.putText(frame, shape, (cX, cY), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
cv2.imshow("Frame", frame)
pass
cv2.imshow("Frame", thresh)
pass
def shapes(request):
# load the image, convert it to grayscale, blur it slightly,
# and threshold it
image = cv2.imread('octa.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
thresh = cv2.threshold(blurred, 60, 255, cv2.THRESH_BINARY)[1]
result = 'result.png'
# find contours in the thresholded image
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
arr = []
# loop over the contours
for c in cnts:
# compute the center of the contour
M = cv2.moments(c)
if (M["m00"] == 0):
M["m00"]=1
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
# draw the contour and center of the shape on the image
cv2.drawContours(image, [c], -1, (0, 255, 0), 2)
cv2.circle(image, (cX, cY), 7, (0, 255, 0), -1)
cv2.putText(image, "center", (cX - 20, cY - 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
cv2.imwrite(result, image)
result_data = open(result, "rb").read()
return HttpResponse(result_data, content_type="image/png")
def remove_blobs(self, full_image, resized_image, gray, ratio, show_plots=False):
if show_plots:
cv2.imshow("Thresh2", gray)
cv2.waitKey(0)
cnts = cv2.findContours(gray.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
# loop over the contours
hsv = cv2.cvtColor(resized_image, cv2.COLOR_BGR2HSV)
# minv = 1000
# for c in cnts:
# mask = np.zeros(gray.shape,np.uint8)
# cv2.drawContours(mask,[c],0,255,-1)
# mean_val = np.array(cv2.mean(hsv,mask = mask))
# minv = min(mean_val[2], minv)
# print minv
for c in cnts:
mask = np.zeros(gray.shape,np.uint8)
cv2.drawContours(mask,[c],0,255,-1)
mean_val = np.array(cv2.mean(hsv,mask = mask))
if np.max(mean_val) < 100 and cv2.contourArea(c) > 5000:
continue
else:
print cv2.contourArea(c)
if cv2.contourArea(c) < 5000:
cv2.drawContours(gray, [c], -1, (0, 0, 0), -1)
# else:
# pass
if show_plots:
cv2.imshow("a", gray)
cv2.waitKey(0)
return gray
def calculate_point(filename, name, ANSWER_KEY = []):
# # construct the argument parse and parse the arguments
# ap = argparse.ArgumentParser()
# ap.add_argument("-i", "--image", required=True,
# help="path to the input image")
# args = vars(ap.parse_args())
# define the answer key which maps the question number
# to the correct answer
# load the image, convert it to grayscale, blur it
# slightly, then find edges
image = cv2.imread('{}/media/{}'.format(settings.BASE_DIR, filename))
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(blurred, 75, 200)
print('step 1')
# find contours in the edge map, then initialize
# the contour that corresponds to the document
cnts = cv2.findContours(edged.copy(), cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
docCnt = None
print('step 2')
# ensure that at least one contour was found
found = False
if len(cnts) > 0:
# sort the contours according to their size in
# descending order
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
print('step 3')
# loop over the sorted contours
round = 0
for c in cnts:
# approximate the contour
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.02 * peri, True)
# if our approximated contour has four points,
# then we can assume we have found the paper
if len(approx) == 4:
docCnt = approx
if found:
color = (0, 255, 0)
cv2.drawContours(image, [docCnt], -1, color, 3)
break
found = True
cv2.imwrite('media/mask/{}-image.png'.format(name), image)
print('step 4')
# apply a four point perspective transform to both the
# original image and grayscale image to obtain a top-down
# birds eye view of the paper
paper = four_point_transform(image, docCnt.reshape(4, 2))
warped = four_point_transform(gray, docCnt.reshape(4, 2))
correct_paper = four_point_transform(image, docCnt.reshape(4, 2))
paper = cv2.resize(paper, (1024, 961))
warped = cv2.resize(warped, (1024, 961))
correct_paper = cv2.resize(correct_paper, (1024, 961))
print('step 5')
# apply Otsu's thresholding method to binarize the warped
# piece of paper
thresh = cv2.threshold(warped, 0, 255,
cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
# store mask and bird view paper
cv2.imwrite('media/mask/{}-bird.png'.format(name), paper)
cv2.imwrite('media/mask/{}-mask.png'.format(name), thresh)
# find contours in the thresholded image, then initialize
# the list of contours that correspond to questions
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
questionCnts = []
print('step 6')
# loop over the contours
count = 0
color = (0, 255, 0)
for c in cnts:
# compute the bounding box of the contour, then use the
# bounding box to derive the aspect ratio
(x, y, w, h) = cv2.boundingRect(c)
ar = w / float(h)
# in order to label the contour as a question, region
# should be sufficiently wide, sufficiently tall, and
# have an aspect ratio approximately equal to 1
if w >= 15 and h >= 15 and w <= 40 and h <= 40:
# if count >= 480 and count < 500:
cv2.drawContours(paper, [c], -1, color, 3)
questionCnts.append(Contour(y, c))
count = count +1
def sort_by_x(countour):
return countour.x
questionCnts = sorted(questionCnts, key=sort_by_x, reverse=False)
questionCnts = [c.cnt for c in questionCnts]
cv2.imwrite('media/mask/{}-circle.png'.format(name), paper)
#
# # sort the question contours top-to-bottom, then initialize
# # the total number of correct answers
# # questionCnts = contours.sort_contours(questionCnts,
# # method="top-to-bottom")[0]
print('step 7')
correct = 0
print(len(questionCnts))
if(len(questionCnts) != 500):
raise ValueError('Wrong number of answer.')
# each question has 5 possible answers, to loop over the
# question in batches of 5
for (q, i) in enumerate(np.arange(0, len(ANSWER_KEY), 1)):
# draw the outline of the correct answer on the test
# sort the contours for the current question from
# left to right, then initialize the index of the
# bubbled answer
start = matched[str(i+1)]['start']
no = i+1
rownum = 0
startInRow = 0
if no >= 1 and no <= 25:
rownum = start
startInRow = 0
elif no >= 26 and no <= 50:
rownum = start - 5
startInRow = 5
elif no >= 51 and no <= 75:
rownum = start - 10
startInRow = 10
elif no >= 76 and no <= 100:
rownum = start - 15
startInRow = 15
cnts = []
rowCnts = questionCnts[rownum: rownum + 20]
for c in rowCnts:
(x, y, w, h) = cv2.boundingRect(c)
cnts.append(Contour(x, c))
cnts = sorted(cnts, key=sort_by_x, reverse=False)
cnts = cnts[startInRow: startInRow + 5]
cnts = [c.cnt for c in cnts]
# for c in cnts:
# cv2.drawContours(paper, [c], -1, color, 3)
# cv2.imwrite('media/mask/{}-answer.png'.format(name), paper)
bubbled = None
color = (0, 0, 255)
count_chosen = 0
chosen_list = []
# # loop over the sorted contours
for (j, c) in enumerate(cnts):
# construct a mask that reveals only the current
# "bubble" for the question
mask = np.zeros(thresh.shape, dtype="uint8")
cv2.drawContours(mask, [c], -1, 255, -1)
# apply the mask to the thresholded image, then
# count the number of non-zero pixels in the
# bubble area
mask = cv2.bitwise_and(thresh, thresh, mask=mask);
total = cv2.countNonZero(mask); print('========'); print((total/len(mask)) * 100)
# if the current total has a larger number of total
# non-zero pixels, then we are examining the currently
# bubbled-in answer
if (total/len(mask)) * 100 > PERCENT_CHOSEN:
count_chosen = count_chosen + 1
chosen_list.append(j + 1)
if bubbled is None or total > bubbled[0]:
bubbled = (total, j + 1)
# initialize the contour color and the index of the
# *correct* answer
k = ANSWER_KEY[q]['correct']
color = (0, 0, 255);print('-----------')
if k == str(bubbled[1]) and count_chosen is 1:
color = (0, 255, 0)
correct += 1
if count_chosen is 0:
pass
elif count_chosen is 1:
cv2.drawContours(correct_paper, [cnts[bubbled[1]-1]], -1, color, 3)
elif count_chosen > 1:
for cir in chosen_list:
cv2.drawContours(correct_paper, [cnts[cir-1]], -1, color, 3)
cv2.imwrite('media/mask/{}-answer.png'.format(name), correct_paper)
print('step 8')
return correct
def readAndGenerateInstanceSegmentation(outputPath, transformers, imagePath):
image = cv2.imread(imagePath)
name = imagePath.split(os.path.sep)[-1]
labelPath = '/'.join(imagePath.split(
os.path.sep)[:-1]) + "/" + name[0:name.rfind(".")] + ".txt"
maskLabels = []
(h, w) = image.shape[:2]
with open(labelPath) as f:
data = json.load(f)
annotations = data['annotations']
for annotation in annotations:
mask = np.zeros((w, h), dtype="uint8")
key = list(annotation)[0]
if key == 'rectangle':
x = annotation['rectangle']['x']
y = annotation['rectangle']['y']
width = annotation['rectangle']['width']
height = annotation['rectangle']['height']
label = annotation['rectangle']['label']
cv2.rectangle(mask, (x, y), (x + width, y + height), 255, -1)
maskLabels.append((mask, label))
if key == 'circle':
x = annotation['circle']['x']
y = annotation['circle']['y']
diameter = annotation['circle']['radius']
label = annotation['circle']['label']
cv2.circle(mask, (int(x + diameter / 2), int(y + diameter / 2)),
int(diameter / 2), 255, -1)
maskLabels.append((mask, label))
if key == 'polygon':
xpoints = annotation['polygon']['xpoints']
ypoints = annotation['polygon']['ypoints']
label = annotation['polygon']['label']
pts = np.array([[x, y] for x, y in zip(xpoints, ypoints)],
np.int32)
pts = pts.reshape((-1, 1, 2))
cv2.fillPoly(mask, [pts], True, 255)
maskLabels.append((mask, label))
for (j, transformer) in enumerate(transformers):
(newimage, newmasklabels) = transformer.transform(image, maskLabels)
cv2.imwrite(outputPath + str(j) + "_" + name, newimage)
newSegmentations = []
for (mask, label) in newmasklabels:
cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
segmentation = [[x[0][0], x[0][1]] for x in cnts[0]]
# Closing the polygon
segmentation.append(segmentation[0])
newSegmentations.append((label, segmentation))
data = {}
data['name'] = name
data['width'] = w
data['height'] = h
data['annotations'] = []
for (l, segmentation) in newSegmentations:
xpoints = [int(x[0]) for x in segmentation]
ypoints = [int(x[1]) for x in segmentation]
data['annotations'].append({
'polygon': {
'ypoints': ypoints,
'xpoints': xpoints,
'label': label
}
})
with open(outputPath + str(j) + "_" + name[0:name.rfind(".")] + ".txt",
'w') as outfile:
json.dump(data, outfile)
# loads image
# grayscale for fun
# blur to filter out noise
image = cv2.imread(args["image"])
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (5,5), 0) # a 5x5 kernel is applied
# threshold turns image into binary
# if intensity of a pixel is larger than 60,
# replace the value with 255, which is white in OpenCV
intensityThreshold = 60
thresh = cv2.threshold(blurred, intensityThreshold, 255, cv2.THRESH_BINARY)[1]
# now find the contours
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1] # they change the position in OpenCV3
# defines what shape an object is
sd = ShapeDetector()
print "// Escape key = Exit; Any other key = Continue //"
# process on each contour
for cnt in cnts:
# object center
M = cv2.moments(cnt)
cntX = int(M["m10"] / M["m00"])
cntY = int(M["m01"] / M["m00"])
# detect shape type
shapeName = sd.detect(cnt)
if shapeName is None: shapeName = ""
# import the necessary packages
from __future__ import print_function
import imutils
import cv2
# print the current OpenCV version on your system
print("Your OpenCV version: {}".format(cv2.__version__))
# check to see if you are using OpenCV 2.X
print("Are you using OpenCV 2.X? {}".format(imutils.is_cv2()))
# check to see if you are using OpenCV 3.X
print("Are you using OpenCV 3.X? {}".format(imutils.is_cv3()))
def init_props(cap, config):
'''
set and store the real camera properties
'''
prop = None
if imutils.is_cv3():
if hasattr(cv2, "CAP_PROP_FPS"):
prop = cv2.CAP_PROP_FPS
elif imutils.is_cv2():
if hasattr(cv2.cv, "CV_CAP_PROP_FPS"):
prop = cv2.cv.CV_CAP_PROP_FPS
if prop is None:
print("OpenCV not compiled with camera framerate property!")
else:
try:
cap.set(prop, config.camera.fps)
config.camera.fps = cap.get(prop)
except:
print(
"Unable to set framerate to {:.1f}!".format(config.camera.fps))
config.camera.fps = cap.get(prop)
finally:
print("-- framerate: {}".format(config.camera.fps))
# set the resolution as specified at the top
prop = [None, None]
if imutils.is_cv3():
if hasattr(cv2, "CAP_PROP_FRAME_WIDTH"):
prop = [cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT]
elif imutils.is_cv2():
if hasattr(cv2.cv, "CV_CAP_PROP_FRAME_WIDTH"):
prop = [cv2.cv.CV_CAP_PROP_FRAME_WIDTH,
cv2.cv.CV_CAP_PROP_FRAME_HEIGHT]
if any(p is None for p in prop):
print("OpenCV not compiled with camera resolution properties!")
else:
try:
for i in range(1, 2):
cap.set(prop[i], config.camera.res[i])
config.camera.res[i] = int(cap.get(prop[i]))
except:
print(
"Unable to set resolution to {}x{}!".format(*config.camera.res))
config.camera.res = [int(cap.get(p)) for p in prop]
finally:
print("-- resolution: {}x{}".format(*config.camera.res))
# try to find the fourcc of the attached camera
prop = None
if imutils.is_cv3():
if hasattr(cv2, "CAP_PROP_FOURCC"):
prop = cv2.CAP_PROP_FOURCC
elif imutils.is_cv2():
if hasattr(cv2.cv, "CV_CAP_PROP_FOURCC"):
prop = cv2.cv.CV_CAP_PROP_FOURCC
if prop is None:
print("OpenCV not compiled with fourcc property!")
else:
try:
config.camera.fourcc = cap.get(prop)
except:
print("Unable to read camera's codec!")
finally:
print("-- fourcc: {}".format(config.camera.fourcc))
# set initial positions
cv2.setTrackbarPos(
'Motion Hist.', config.window.name, config.computing.bg_sub_hist)
cv2.setTrackbarPos(
'Motion Thresh.', config.window.name, int(config.computing.bg_sub_thresh))
cv2.createTrackbar('Processing Width', config.window.name,
100, config.camera.res[0], update_processing_width)
cv2.setTrackbarPos(
'Processing Width', config.window.name, config.computing.width)
# background subtractor
if imutils.is_cv3():
fgbg = cv2.createBackgroundSubtractorMOG2(
config.computing.bg_sub_hist, config.computing.bg_sub_thresh)
elif imutils.is_cv2():
fgbg = cv2.BackgroundSubtractorMOG2(
config.computing.bg_sub_hist, config.computing.bg_sub_thresh)
def get_motions(f, fMask, thickness=1, color=(170, 170, 170)):
'''
Iterates over the contours in a mask and draws a bounding box
around the ones that encompas an area greater than a threshold.
This will return an image of just the draw bock (black bg), and
also an array of the box points.
'''
rects_mot = []
f_rects = np.zeros(f.shape, np.uint8)
# get contours
if imutils.is_cv3():
# ap.add_argument("-t", "--target", required=True, help="path to the target image")
args = vars(ap.parse_args())
# Check that the files exist.
if not os.path.isfile(args['source']):
print 'Source file', args['source'], 'does not exist.'
sys.exit()
# Source: read, gray, blur, thresh, contour.
image = cv2.imread(args['source'])
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
thresh = cv2.threshold(blurred, 60, 255, cv2.THRESH_BINARY)[1]
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
# Get the shape of the object and create an empty image.
height, width, dimensions = image.shape
Color = np.zeros((height, width, 3), 'uint8')
# Re-create the image (proof of concept).
for vv in range(0, height):
for hh in range(0, width):
if cv2.pointPolygonTest(cnts[0], (hh, vv), False) != -1:
Color[vv][hh] = [255, 255, 0]
# Show the comparison.
plt.subplot(3, 2, 1)
plt.imshow(image, 'gray')
plt.subplot(3, 2, 2)
# perform edge detection, then perform a dilation + erosion to
# close gaps in between object edges
edged = cv2.Canny(img, 20,
50) # Adjust these parameters according to your image
dilate = cv2.dilate(edged, None, iterations=1)
# We don't perform erosion, it completely depends on the image and need
# erode = cv2.erode(dilate, None, iterations=1)
# make an empty mask
mask = np.ones(img.shape[:2], dtype="uint8") * 255
# find contours
cnts = cv2.findContours(dilate.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
orig = img.copy()
for c in cnts:
# if the contour is not sufficiently large, ignore it
if cv2.contourArea(c) < 200:
cv2.drawContours(mask, [c], -1, 0, -1)
x, y, w, h = cv2.boundingRect(c)
# Filter and remove more contours according to your need
if (w > h):
cv2.drawContours(mask, [c], -1, 0, -1)
# Remove those ignored contours
newimage = cv2.bitwise_and(dilate.copy(), dilate.copy(), mask=mask)
def Begin(Type):
avg = None
count = 0
flag = 0
cnt = 0
first = None
detectflag = None
while True:
if Type == 0:
frame = vs.read()
elif Type == 1:
if vs.more() == False:
break
frame = vs.read()
if first is None:
frame = imutils.resize(frame, width=500)
r = selectRoi(frame)
frameroi = frame[int(r[1]):int(r[1] + r[3]),
int(r[0]):int(r[0] + r[2])]
grayf = cv2.cvtColor(frameroi, cv2.COLOR_BGR2GRAY)
grayfb = cv2.GaussianBlur(grayf, (21, 21), 0)
first = 1
text = "Unoccupied"
frame = imutils.resize(frame, width=500)
frameroi = frame[int(r[1]):int(r[1] + r[3]),
int(r[0]):int(r[0] + r[2])]
gray = cv2.cvtColor(frameroi, cv2.COLOR_BGR2GRAY)
grayb = cv2.GaussianBlur(gray, (5, 5), 0)
if avg is None:
print("[INFO] starting background model...")
avg = grayb.copy().astype("float")
continue
cv2.accumulateWeighted(grayb, avg, 0.6)
frameDelta = cv2.absdiff(grayb, cv2.convertScaleAbs(avg))
thresh = cv2.threshold(frameDelta, 10, 255, cv2.THRESH_BINARY)[1]
thresh = cv2.dilate(thresh, None, iterations=2)
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
for c in cnts:
(x, y, w, h) = cv2.boundingRect(c)
if w < 20 or h < 20:
continue
cv2.rectangle(frameroi, (x, y), (x + w, y + h), (0, 255, 0), 2)
text = "Occupied"
if len(cnts) != 0:
cnt += 1
flag = 1
count = 0
if len(cnts) == 0:
count += 1
if count >= 100:
if flag == 0:
grayf = gray
cnt = 0
else:
flag = 0
if cnt >= 50:
grayfb = cv2.GaussianBlur(grayf, (21, 21), 0)
grayb = cv2.GaussianBlur(gray, (21, 21), 0)
frameD = cv2.absdiff(grayb, grayfb)
threshd = cv2.threshold(frameD, 35, 255,
cv2.THRESH_BINARY)[1]
threshd = cv2.dilate(threshd, None, iterations=2)
#cv2.imshow("thred",threshd)
cntsd = cv2.findContours(threshd.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cntsd = cntsd[0] if imutils.is_cv2() else cntsd[1]
fram = frameroi.copy()
#cv2.drawContours(fram, cntsd, -1, (0,255,0), 3)
for c in cntsd:
(x, y, w, h) = cv2.boundingRect(c)
area = w * h
if area < 81:
continue
cv2.rectangle(fram, (x, y), (x + w, y + h),
(0, 255, 0), 2)
image1 = grayf[int(y):int(y + h), int(x):int(x + w)]
image2 = gray[int(y):int(y + h), int(x):int(x + w)]
s = ssim(image1, image2)
if s < 0.4:
if detectflag == None:
print("object is detected")
detectflag = 1
cv2.rectangle(frameroi, (x, y), (x + w, y + h),
(0, 255, 0), 2)
continue
if area > 2500:
c = compare(image1, image2)
if c >= 3:
if detectflag == None:
print("object is detected")
detectflag = 1
cv2.rectangle(frameroi, (x, y), (x + w, y + h),
(0, 255, 0), 2)
#cv2.imshow("Security", frame
detectflag = None
cv2.imshow("Security", frameroi)
#cv2.imshow("Sec", fram)
cv2.waitKey(1)
cv2.imshow("Security Feed", frameroi)
cv2.imshow("thresh", thresh)
key = cv2.waitKey(1) & 0xFF
if key == ord("q"):
break
def detect_color_and_shape(image_file):
'''
Finds the shape and color of each tile in the puzzle.
Returns these values in a 2d list.
'''
new_graph = [[[] for _ in range(9)] for _ in range(9)]
#my_image = 'bc_myedit.jpg'
image = cv2.imread(image_file)
# blur the resized image slightly, then convert it to both
# graysale and the L*a*b* color space
blurred = cv2.GaussianBlur(image, (5, 5), 0)
gray = cv2.cvtColor(blurred, cv2.COLOR_BGR2GRAY)
lab = cv2.cvtColor(blurred, cv2.COLOR_BGR2LAB)
thresh = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY)[1]
binary = cv2.bitwise_not(thresh)
cv2.imshow('image', binary)
cv2.waitKey(0)
# find contours in the thresholded image
cnts = cv2.findContours(binary.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
# make copy of image
image_copy = np.copy(image)
# find contours of large enough area
min_cnt_area = 750
large_cnts = [cnt for cnt in cnts if cv2.contourArea(cnt) > min_cnt_area]
# draw contours
cv2.drawContours(image_copy, large_cnts, -1, (255, 0, 0))
# init
sd = ShapeDetector()
cl = ColorLabeler()
# loop over cnts
for c in large_cnts:
# compute the center of the contour
M = cv2.moments(c)
try:
cX = int((M['m10'] / M['m00']))
cY = int((M['m01'] / M['m00']))
except:
cX = 0
cY = 0
# detect the shape of the contour and label the color
shape = sd.detect(c)
color = cl.label(lab, c)
# draw the contours and the name of the shape and labeled
# color on the image
c = c.astype('float')
c = c.astype('int')
txt_color = color
txt_shape = shape
#text = '{} {}'.format(color, shape)
cv2.drawContours(image, [c], -1, (0, 0, 0), 2)
cv2.putText(image, txt_color, (cX, cY), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)
cv2.putText(image, txt_shape, (cX, cY - 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)
y_graph = int(round(cX / 80))
x_graph = int(round(cY / 80))
new_graph[x_graph][y_graph] = [color, shape]
# show image
cv2.imshow('image', image)
cv2.waitKey(0)
return new_graph
def CamMovement(qc):
global Cam_Run
firstFrame = None
hits = 0 # counter for us to cycle over files
CHNG_THRESH = 65 # Change Threshold used to be 25
HR_Cam = qc.get()
cc = qc.get()
i = 0
vs = [] # init VS array
while i < cam_count:
vs.append(cv2.VideoCapture(i))
if not vs[i].isOpened():
print('Could not open webcam #' + str(i) + ' \n')
vs[i].release()
vs.pop(i)
i = i - 1
break
i = i + 1
signal.signal(signal.SIGTERM, sigterm_cam)
signal.signal(signal.SIGINT, sigterm_cam)
while Cam_Run:
# grab the current frame and initialize the occupied/unoccupied
retval, frame = vs[HR_Cam].read()
text = "Unoccupied"
# if the frame could not be grabbed, then we have reached the end
# of the video
if frame is None:
break
# resize the frame, convert it to grayscale, and blur it
frame = imutils.resize(frame, width=500)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (21, 21), 0)
# if the first frame is None, initialize it
if firstFrame is None:
firstFrame = gray
continue
# compute the absolute difference between the current frame and
# first frame
frameDelta = cv2.absdiff(firstFrame, gray)
thresh = cv2.threshold(frameDelta, CHNG_THRESH, 255,
cv2.THRESH_BINARY)[1]
# dilate the thresholded image to fill in holes, then find contours
# on thresholded image
thresh = cv2.dilate(thresh, None, iterations=2)
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
# loop over the contours
Caption = "Empty"
for c in cnts:
# compute the bounding box for the contour, draw it on the frame,
# and update the text
(x, y, w, h) = cv2.boundingRect(c)
if (w > 10) and (h > 10): # trying to eliminate tiny changes
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
text = "Occupied"
Caption = text + ' !'
# draw the text and timestamp on the frame
cv2.putText(frame, "Room Status: {}".format(Caption), (10, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
cv2.putText(frame,
datetime.datetime.now().strftime("%A %d %B %Y %I:%M:%S%p"),
(10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.35,
(0, 0, 255), 1)
# show the frame
if text == "Occupied":
cv2.imwrite(str(hits) + '_Security' + '.png', frame)
for x in range(cc):
if x != HR_Cam:
retval, frame = vs[x].read()
Caption = str(x)
cv2.putText(frame, "Camera: {}".format(Caption), (10, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
cv2.putText(
frame,
datetime.datetime.now().strftime(
"%A %d %B %Y %I:%M:%S%p"),
(10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX,
0.35, (0, 0, 255), 1)
cv2.imwrite(
str(hits) + '_Cam' + str(x) + '_' + '.png', frame)
hits = hits + 1
qc.put([True, datetime.datetime.now()])
sleep(4) # give it 4 secs before you grab more frames
if hits > 19: # recycle videos so as not to eat space
hits = 0
i = 0
while i < (cam_count - 1):
vs[i].release()
vs.pop(i)
i = i + 1
def main():
"""Annotate images
"""
# construct the argument parse and parse the arguments
args = argparse.ArgumentParser()
args.add_argument("-i",
"--input",
required=True,
help="path to input directory of images")
args.add_argument("-a",
"--annot",
required=True,
help="path to output directory of annotations")
args = vars(args.parse_args())
# grab the image paths then initialize the dictionary of character counts
image_paths = list(paths.list_images(args["input"]))
counts = {}
# loop over the image paths
for (i, image_paths) in enumerate(image_paths):
# display an update to the user
print("[INFO] processing image {}/{}".format(i + 1, len(image_paths)))
try:
# load the image and convert it to grayscale, then pad the image to ensure
# digits caught on the border of the image are retained
image = cv2.imread(image_paths)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.copyMakeBorder(gray, 8, 8, 8, 8, cv2.BORDER_REPLICATE)
# threshold the image to reveal the digits
thresh = cv2.threshold(gray, 0, 255,
cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
# find contours in the image, keeping only the four largest ones
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:4]
# loop over the contours
for contour in cnts:
# compute the bounding box for the contour then extract the digit
(x, y, w, h) = cv2.boundingRect(contour)
roi = gray[y - 5:y + h + 5, x - 5:x + w + 5]
# display the character, making it large enough for us to see,
# then wait for a keypress
cv2.imshow("ROI", imutils.resize(roi, width=28))
key = cv2.waitKey(0)
# if the '`' key is pressed, then ignore the character
if key == ord("`"):
print("[INFO] ignoring character")
continue
# grab the key that was pressed and construct the path the output directory
key = chr(key).upper()
dir_path = os.path.sep.join([args["annot"], key])
# if the output directory does not exist, create it
if not os.path.exists(dir_path):
os.makedirs(dir_path)
# write the labeled character to file
count = counts.get(key, 1)
path = os.path.sep.join(
[dir_path, "{}.png".format(str(count).zfill(6))])
cv2.imwrite(path, roi)
# increment the count for the current key
counts[key] = count + 1
# we are trying to control-contour out of the script, so break from the loop (you still
# need to press a key for the active window to trigger this)
except KeyboardInterrupt:
print("[INFO] manually leaving script")
break
# an unknown error has occurred for this particular image
except BaseException: # pylint: disable=broad-except
print("[INFO] skipping image...")
def doc_scan(image_path):
"""
Converts an image of a document into a top-down scan of it.
This function uses the imutils, numpy, and opencv python library to
take a path to a given image and automatically detect the corners of
the document within the picture. The picture is then reshaped and
transformed using the four_point_transform function of transform.py
to an image of just the document. This is adapted from the
www.pyimagesearch.com python scanner tutorial.
The document within the picture must be a rectangle with 4 corners,
otherwise contour selection will return an error.
:param image_path:
Path to the image to be scanned.
:return: final_image -
Top-down, full document version of the original image as an
opencv image object.
"""
# STEP 1 : Edge Detection
# Load the image, get ratio of old to new height, clone & resize it.
image = cv2.imread(image_path)
ratio = image.shape[0] / NEW_IMG_HEIGHT
original = image.copy()
image = imutils.resize(image, height=int(NEW_IMG_HEIGHT))
# convert to gray-scale, blur, and find edges.
gray_img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray_img = cv2.GaussianBlur(gray_img, (GAUSS_KERN, GAUSS_KERN), SIGMA_X)
img_edges = cv2.Canny(gray_img, THRESHOLD_BOT, THRESHOLD_TOP)
# STEP 2 : Detecting Contours
# Find the contours in the edge image, keep the largest contours, &
# initialize the screen contour.
contours = cv2.findContours(img_edges.copy(), cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if imutils.is_cv2() else contours[1]
contours = sorted(contours, key=cv2.contourArea, reverse=True)[:5]
# Loop over contours
for cont in contours:
# Approximate the contour
perimeter = cv2.arcLength(cont, True)
approx_cont = cv2.approxPolyDP(cont, 0.02 * perimeter, True)
# If the approximated contour has 4 pts, assume it's the screen.
if len(approx_cont) == 4:
screen_cont = approx_cont
break
else:
raise Exception("Document not found, please try again")
# STEP 3 : Apply Perspective Transform & Threshold
# Apply four_point_transform to get top-down view of original image.
top_down = four_point_transform(original,
screen_cont.reshape(4, 2) * ratio)
# Convert top-down image to gray-scale, and threshold it to give the
# effect of a 'black and white' copy.
top_down = cv2.cvtColor(top_down, cv2.COLOR_BGR2GRAY)
threshold = threshold_local(top_down,
BLOCK_SIZE,
offset=OFFSET,
method=METHOD)
top_down = (top_down > threshold).astype("uint8") * 255
final_image = imutils.resize(top_down, height=FIN_IMG_HEIGHT)
return final_image
def main():
cap = cv2.VideoCapture(vid_path)
status1, previous_frame = cap.read()
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
copy_frame = cv2.cvtColor(previous_frame, cv2.COLOR_BGR2GRAY)
fgbg = cv2.createBackgroundSubtractorMOG2()
hsv = np.zeros_like(previous_frame)
hsv[..., 1] = 255
t = 20
dc = 6
start = 0
i = 0
while (i < total_frames - 1):
ret, frame = cap.read()
i = i + 1
frame1 = frame.copy()
current_frame = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)
current_frame = cv2.GaussianBlur(current_frame, (var_blur, var_blur),
0)
# optical Flow
flow = cv2.calcOpticalFlowFarneback(copy_frame, current_frame, None,
0.5, 3, 15, 3, 5, 1.2, 0)
mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1])
hsv[..., 0] = ang * 180 / np.pi / 2
hsv[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)
bgr = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
grayscaled = cv2.cvtColor(bgr, cv2.COLOR_BGR2GRAY)
retval2, binary_image2 = cv2.threshold(
grayscaled, 125, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
# Background Subtraction
binary_image3 = fgbg.apply(current_frame)
# combination of two methods
final_binary = cv2.bitwise_and(binary_image2, binary_image3)
lab_val = 255
n_labels, img_labeled, lab_stats, _ = \
cv2.connectedComponentsWithStats(final_binary, connectivity=8,
ltype=cv2.CV_32S)
if lab_stats[1:, 4].size > 2:
start = 1
dc = dc + 1
if dc > 6:
dc = 0
re = lab_stats[1:, 4].argsort()[-3:][::-1] + 1
largest_mask = np.zeros(final_binary.shape, dtype=np.uint8)
largest_mask[img_labeled == re[0]] = lab_val
cnts1 = cv2.findContours(largest_mask.copy(),
cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts1 = cnts1[0] if imutils.is_cv2() else cnts1[1]
cv2.putText(frame, 'Breathing', (10, 40), cv2.FONT_HERSHEY_SIMPLEX,
1, (0, 255, 255), 1, cv2.LINE_AA)
cv2.imshow('Frame', frame)
else:
t = t + 1
if t > 40:
if lab_stats[1:, 4].size > 0 and start == 1:
t = 0
cv2.putText(frame, 'Not Breathing', (10, 40),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 1,
cv2.LINE_AA)
cv2.imshow('Frame', frame)
else:
cv2.putText(frame, 'Breathing', (10, 40),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 1,
cv2.LINE_AA)
cv2.imshow('Frame', frame)
previous_frame = current_frame
k = cv2.waitKey(1) & 0xff
if k == 27:
break
cap.release()
cv2.destroyAllWindows()
# load our YOLO object detector trained on COCO dataset (80 classes)
# and determine only the *output* layer names that we need from YOLO
print("[INFO] loading YOLO from disk...")
net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)
ln = net.getLayerNames()
ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]
# initialize the video stream, pointer to output video file, and
# frame dimensions
vs = cv2.VideoCapture(args["input"])
writer = None
(W, H) = (None, None)
# try to determine the total number of frames in the video file
try:
prop = cv2.cv.CV_CAP_PROP_FRAME_COUNT if imutils.is_cv2() \
else cv2.CAP_PROP_FRAME_COUNT
total = int(vs.get(prop))
print("[INFO] {} total frames in video".format(total))
# an error occurred while trying to determine the total
# number of frames in the video file
except:
print("[INFO] could not determine # of frames in video")
print("[INFO] no approx. completion time can be provided")
total = -1
# loop over frames from the video file stream
while True:
# read the next frame from the file
(grabbed, frame) = vs.read()
def detectShapes(mask, image):
X = 20
Y = 20
line = 0
rectangle = 0
circle = 0
triangle = 0
kernelopen = np.ones((5, 5), np.uint8)
kernelClose = np.ones((3, 3), np.uint8)
opening = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernelopen)
closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernelClose)
R = closing.shape[0]
C = closing.shape[1]
temp = closing[0:R, 0:C]
edges = DoG(temp)
ratio = image.shape[0] / float(image.shape[0])
mx = np.amax(edges)
mn = np.amin(edges)
img = image
thresh_val = np.average(np.arange(mn, mx))
_, thresh = cv2.threshold(edges, thresh_val, 255, cv2.THRESH_BINARY)
cv2.imshow("edge", thresh)
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
#cnts=sorted(cnts, key=cv2.contourArea, reverse=True)
for c in cnts:
if cv2.arcLength(
c,
True) > 50: #this is the condition to eliminate small curves
shape = detect(c, img, cnts, thresh)
if (shape == "triangle"):
triangle += 1
cv2.drawContours(img, c, -1, (0, 255, 0), 3)
#triangle = shapes_count_safety_check(triangle)
elif (shape == "circle"):
circle += 1
cv2.drawContours(img, c, -1, (189, 255, 30), 3)
#circle = shapes_count_safety_check(circle)+
elif (shape == "rectangle"):
rectangle += 1
cv2.drawContours(img, c, -1, (0, 0, 0), 3)
#rectangle = shapes_count_safety_check(rectangle)
elif (shape == "line"):
line += 1
cv2.drawContours(img, c, -1, (0, 0, 255), 3)
#line = shapes_count_safety_check(line)
cv2.putText(image, str(line), (X, Y), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(0, 0, 255), 2)
cv2.line(image, (X + 30, Y - 2), (X + 70, Y - 2), (0, 0, 255), 4)
cv2.putText(image, str(circle), (X, Y + 40), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(0, 0, 255), 2)
cv2.circle(image, (X + 40, Y + 35), 15, (0, 0, 255), -1)
cv2.putText(image, str(rectangle), (X, Y + 80), cv2.FONT_HERSHEY_SIMPLEX,
0.5, (0, 0, 255), 2)
cv2.rectangle(image, (X + 25, Y + 60), (X + 55, Y + 90), (0, 0, 255), -1)
pt1 = (X + 40, Y + 100)
pt2 = (X + 20, Y + 130)
pt3 = (X + 60, Y + 130)
triangle_cnt = np.array([pt1, pt2, pt3])
cv2.drawContours(image, [triangle_cnt], 0, (0, 0, 255), -1)
cv2.putText(image,
str(triangle) + " ", (X, Y + 120), cv2.FONT_HERSHEY_SIMPLEX,
0.5, (0, 0, 255), 2)
img2 = cv2.dilate(grayC, kernel)
img3 = cv2.erode(img2, kernel)
# img3 = cv2.erode(img2, kernel)
# kernel = np.ones((5, 5), np.uint8)
# img4 = cv2.erode(img3, kernel)
img4 = cv2.dilate(img3, kernel)
img5 = cv2.dilate(img4, kernel)
# kernel = np.ones((7, 7), np.uint8)
img6 = cv2.erode(img5, kernel)
thresh_new = cv2.threshold(img4, 0, 255,
cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
cnts = cv2.findContours(thresh_new.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
time_3 = datetime.datetime.now()
max_size = 0
max_list = [0, 0, 0, 0]
my_array = np.zeros((480, 640), dtype=np.uint8)
for c in cnts:
(x, y, w, h) = cv2.boundingRect(c)
if w < 50 or h < 50:
continue
my_array[y:y + h, x:x + w] = 1
new_cnts = cv2.findContours(my_array.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
# initialize the distance colors and reference object
(cnts, _) = contours.sort_contours(cnts)
colors = ((0, 0, 255), (240, 0, 159), (0, 165, 255), (255, 255, 0), (255, 0,
255))
refObj = None
# loop over the contours individualy
for c in cnts:
if cv2.contourArea(c) < 100:
continue
box = cv2.minAreaRect(c)
box = cv2.cv.BoxPoints(box) if imutils.is_cv2() else cv2.boxPoints(box)
box = np.array(box, dtype="int")
box = perspective.order_points(box)
cX = np.average(box[:, 0])
cY = np.average(box[:, 1])
if refObj is None:
(tl, tr, br, bl) = box
(tlblX, tlblY) = midpoint(tl, bl)
(trbrX, trbrY) = midpoint(tr, br)
D = dist.euclidean((tlblX, tlblY), (trbrX, trbrY))
refObj = (box, (cX, cY), D / 70)
continue
orig = image.copy()
cv2.drawContours(orig, [box.astype("int")], -1, (0, 255, 0), 2)
cv2.drawContours(orig, [refObj[0].astype("int")], -1, (0, 255, 0), 2)
refCoords = np.vstack([refObj[0], refObj[1]])
def run(self, result):
'''输入一个参数result传递结果（cx, cy, flagLightFinded，cnt）,cnt=(cnt++)%1000，用cnt来判断结果是否有更新'''
with picamera.PiCamera() as camera:
camera.resolution = (240, 160)
camera.framerate = 30
camera.iso = 400
camera.awb_mode = 'off'
camera.awb_gains = 1
camera.shutter_speed = 4000
# camera.start_recording('test.h264')
stream = PiRGBArray(camera, size=(240, 160))
timer = threading.Timer(1, self.showfps)
timer.start()
inf = 666666666
cnt = 0
for frame in camera.capture_continuous(stream,
format="bgr",
use_video_port=True):
cx = 0
cy = 0
src = frame.array
# cv2.imshow('Capture',src)
# cv2.imwrite('test.jpg',src)
hsv = cv2.cvtColor(src, cv2.COLOR_BGR2HSV)
# grayscaled = cv2.cvtColor(src,cv2.COLOR_BGR2GRAY)
mask = cv2.inRange(hsv, self.args_dict['lower_red'],
self.args_dict['upper_red'])
# mask_2 = cv2.inRange(hsv, lower_red_1, upper_red_1)
# mask = cv2.bitwise_or(mask_1,mask_2)
# blur = cv2.GaussianBlur(imageG,(5,5),0)
# retval,fixed=cv2.threshold(imageG,150,255,cv2.THRESH_BINARY)
kernel = np.ones((15, 15), np.uint8)
# mask = cv2.erode(mask,kernel)
mask = cv2.dilate(mask, kernel, 1)
# Find the Middle of the Light Blur
cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
distance = [None] * len(cnts)
i = 0
for c in cnts:
M = cv2.moments(c)
cX = int(M["m10"] / (M["m00"] + 1))
cY = int(M["m01"] / (M["m00"] + 1))
if [cX, cY] != [0, 0]:
distance[i] = (cX - 104) * (cX - 104) + (160 - cY) * (
160 - cY)
else:
distance[i] = inf
i = i + 1
flagLightFinded = 0
if distance != []:
M = cv2.moments(cnts[distance.index(min(distance))])
cx = round((M["m10"] / (M["m00"] + 1)) / 240.0 * 128)
cy = round((M["m01"] / (M["m00"] + 1)) / 160.0 * 128)
flagLightFinded = 1
cv2.rectangle(src, (cX - 40, cY - 30), (cX + 40, cY + 30),
(0, 255, 0), 4)
# str = "A%d,%d,%dFF " % (cx, cy, flagLightFinded);
self.result = [cx, cy, flagLightFinded, cnt]
cnt = (cnt + 1) % 1000
# ser.write('A100,100,1FF ')
# print(str)
if self.gui.imshow_flag:
cv2.imshow('Mask', mask)
# cv2.imwrite('test_1.jpg',src);
cv2.imshow('Image', src)
self.fps = self.fps + 1
stream.truncate(0)
cv2.waitKey(1)
ap = argparse.ArgumentParser()
ap.add_argument("-n", "--new", type=int, default=-1,
help="whether or not the new order points should should be used")
args = vars(ap.parse_args())
image = cv2.imread("Res/X_windows.png")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (7, 7), 0)
edged = cv2.Canny(gray, 50, 100)
edged = cv2.dilate(edged, None, iterations=1)
edged = cv2.erode(edged, None, iterations=1)
cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
(cnts, _) = contours.sort_contours(cnts)
colors = ((0, 0, 255), (240, 0, 159), (255, 0, 0), (255, 255, 0))
for (i, c) in enumerate(cnts):
if cv2.contourArea(c) < 100:
continue
box = cv2.minAreaRect(c)
box = cv2.cv.BoxPoints(box) if imutils.is_cv2() else cv2.boxPoints(box)
box = np.array(box, dtype="int")
cv2.drawContours(image, [box], -1, (0, 255, 0), 2)
# show the original coordinates
image = cv2.imread(image_file)
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Add some extra padding around the image
image = cv2.copyMakeBorder(image, 20, 20, 20, 20, cv2.BORDER_REPLICATE)
# threshold the image (convert it to pure black and white)
thresh = cv2.threshold(image, 0, 255,
cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
# find the contours (continuous blobs of pixels) the image
contours = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
# Hack for compatibility with different OpenCV versions
contours = contours[0] if imutils.is_cv2() else contours[1]
letter_image_regions = []
# Now we can loop through each of the four contours and extract the letter
# inside of each one
for contour in contours:
# Get the rectangle that contains the contour
(x, y, w, h) = cv2.boundingRect(contour)
# Compare the width and height of the contour to detect letters that
# are conjoined into one chunk
if w / h > 1.25:
# This contour is too wide to be a single letter!
# Split it in half into two letter regions!
half_width = int(w / 2)
def process_image(self, frame):
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.medianBlur(gray, 5)
# Get Parameters from ROS interface
MinThreshold = rospy.get_param('~MinThreshold')
MaxThreshold = rospy.get_param('~MaxThreshold')
MinAreaThreshold = rospy.get_param('~MinAreaThreshold')
ReferenceMeasure = rospy.get_param('~ReferenceMeasure')
# perform edge detection, then perform a dilation + erosion to
# close gaps in between object edges
edged = cv2.Canny(gray, MinThreshold, MaxThreshold)
kernel = np.ones((3, 3), np.uint8)
edged = cv2.dilate(edged, kernel, iterations=1)
# find contours in the edge map
cnts = cv2.findContours(edged.copy(), cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
# sort the contours from left-to-right and initialize the
# 'pixels per metric' calibration variable
(cnts, _) = contours.sort_contours(cnts)
# compute the rotated bounding box of the contour
processed_frame = frame.copy()
# loop over the contours individually
for c in cnts:
# if the contour is not sufficiently large, ignore it
if cv2.contourArea(c) < MinAreaThreshold:
continue
hull = cv2.convexHull(c, returnPoints=True)
# compute the rotated bounding box of the contour
box = cv2.minAreaRect(hull)
box = cv2.cv.BoxPoints(box)
box = np.array(box, dtype="int")
# order the points in the contour such that they appear
# in top-left, top-right, bottom-right, and bottom-left
# order, then draw the outline of the rotated bounding
# box
box = perspective.order_points(box)
cv2.drawContours(processed_frame, [
box.astype("int")], -1, (0, 0, 255), 2)
# loop over the processed_frame points and draw them
for (x, y) in box:
cv2.circle(processed_frame, (int(x), int(y)),
5, (0, 0, 255), -1)
# unpack the ordered bounding box, then compute the midpoint
# between the top-left and top-right coordinates, followed by
# the midpoint between bottom-left and bottom-right coordinates
(tl, tr, br, bl) = box
(tltrX, tltrY) = self.midpoint(tl, tr)
(blbrX, blbrY) = self.midpoint(bl, br)
# compute the midpoint between the top-left and top-right points,
# followed by the midpoint between the top-righ and bottom-right
(tlblX, tlblY) = self.midpoint(tl, bl)
(trbrX, trbrY) = self.midpoint(tr, br)
# if the contour is not sufficiently large, ignore it
if cv2.contourArea(c) < 10000:
# draw the midpoints on the frame
cv2.circle(processed_frame, (int(tltrX),
int(tltrY)), 5, (255, 0, 0), -1)
cv2.circle(processed_frame, (int(blbrX),
int(blbrY)), 5, (255, 0, 0), -1)
cv2.circle(processed_frame, (int(tlblX),
int(tlblY)), 5, (255, 0, 0), -1)
cv2.circle(processed_frame, (int(trbrX),
int(trbrY)), 5, (255, 0, 0), -1)
# draw lines between the midpoints
cv2.line(processed_frame, (int(tltrX), int(tltrY)), (int(blbrX), int(blbrY)),
(255, 0, 255), 2)
cv2.line(processed_frame, (int(tlblX), int(tlblY)), (int(trbrX), int(trbrY)),
(255, 0, 255), 2)
# compute the Euclidean distance between the midpoints
dA = dist.euclidean((tltrX, tltrY), (blbrX, blbrY))
dB = dist.euclidean((tlblX, tlblY), (trbrX, trbrY))
# if the pixels per metric has not been initialized, then
# compute it as the ratio of pixels to supplied metric
# (in this case, inches)
if self.pixelsPerMetric is None:
self.pixelsPerMetric = dB / ReferenceMeasure
# compute the size of the object
dimA = dA / self.pixelsPerMetric
dimB = dB / self.pixelsPerMetric
if cv2.contourArea(c) < 10000:
# draw the object sizes on the frame
cv2.putText(processed_frame, "{:.1f}mm".format(dimA),
(int(tltrX - 15), int(tltrY - 10)
), cv2.FONT_HERSHEY_SIMPLEX,
0.70, (0, 0, 255), 2)
cv2.putText(processed_frame, "{:.1f}mm".format(dimB),
(int(trbrX + 10), int(trbrY)
), cv2.FONT_HERSHEY_SIMPLEX,
0.70, (0, 0, 255), 2)
return processed_frame
def select2(): # 정답 and 좌표찾기
global answerSheet, position, answerList
path = filedialog.askopenfilename()
answerSheet = cv2.imread(path, 0)
# 이미지 서로 다른 부분 찾는 코드 정확히 모름
(score, diff) = compare_ssim(testSheet, answerSheet, full=True)
diff = (diff * 255).astype("uint8")
thresh = cv2.threshold(diff, 0, 255,
cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
# 정답지 세로로 분리
x1, y1, h1 = 0, 0, 0
num = 0
count = 0
startX, startY = 0, 0
img = []
row = []
row.append([])
box = []
for c in cnts:
(x, y, w, h) = cv2.boundingRect(c)
box.append(c)
if count == 0:
row[num].append(count)
pass
else:
if abs(y1 - y) < 20:
row[num].append(count)
else:
num = num + 1
row.append([])
row[num].append(count)
y1 = y
count += 1
#정답지 세로로 분리된 것 중에 가로로 분리할 수 있는지 확인
a, b, c, d = 0, 0, 0, 0
for i in range(0, len(row)):
minX = 10000
minY = 10000
maxX = 0
maxY = 0
for j in range(0, len(row[i])):
a, b, c, d = cv2.boundingRect(box[row[i][j]])
if minX > a:
minX = a
if maxY < b + d:
maxY = b + d
if maxX < a + c:
maxX = a + c
if minY > b:
minY = b
if abs(maxY - minY) < 5:
pass
else:
img.append(
answerSheet[minY:maxY,
minX:maxX]) # img == 최종 답안 단어들의 이미지를 저장한 리스트
print(len(position))
answerList = []
# 이 코드가 실행하고 있는 위치에 answer이라는 폴더만들면 정답지가 answer폴더안에 저장 dir있는지 확인하고 없으면 만드는 코드로 수정
for i in range(0, len(img)):
#cv2.imshow(str(i), img[i])
# resize = cv2.resize(img[i], None, fx=2.0, fy=2.0, interpolation=cv2.INTER_CUBIC + cv2.INTER_LINEAR)
'''gau = cv2.GaussianBlur(resize, (5, 5), 0)
temp = cv2.addWeighted(resize, 1.5, gau, -0.5, 0)
kernel = np.ones((2,2), np.uint8)
er = cv2.erode(temp, kernel, iterations=1)'''
#cv2.imshow("zzz", er)
#tessdata_dir_config = r'--tessdata-dir "<C:\Program Files (x86)\Tesseract-OCR\tessdata>"'
#cv2.imwrite("answer\\"+"ss"+str(i) + ".jpg", er)
result = pytesseract.image_to_string(img[i], lang='eng')
result = result.replace(" ", "")
result = str(result)
answerList.append(result)
print(result)
if not (os.path.isdir("answerSheet")):
os.makedirs(os.path.join("answerSheet"))
f = open("answerSheet/answerList.txt", "w", -1, "utf-8")
for i in range(0, len(answerList)):
f.write(answerList[i] + "\n")
f.close()
def findContour(self, image):
contour = cv2.findContours(image, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE)
contour = contour[0] if imutils.is_cv2() else contour[1]
return contour
#Hence p is a keypoint of the image (and it represents a corner in the image)
# import the necessary packages
from __future__ import print_function
import numpy as np
import cv2
import imutils
# load the image and convert it to grayscale
image = cv2.imread("./images/trex.png")
#image = cv2.imread("./images/grand_central_terminal.png")
orig = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# handle if we are detecting FAST keypoints in the image for OpenCV 2.4
if imutils.is_cv2():
detector = cv2.FeatureDetector_create("FAST")
kps = detector.detect(gray)
# otherwise, we are detecting FAST keypoints for OpenCV 3+
else:
detector = cv2.FastFeatureDetector_create()
kps = detector.detect(gray, None)
print("# of keypoints: {}".format(len(kps)))
# loop over the keypoints and draw them
for kp in kps:
r = int(0.5 * kp.size)
(x, y) = np.int0(kp.pt)
cv2.circle(image, (x, y), r, (0, 255, 255), 2)
# show the image
cv2.imshow("Images", np.hstack([orig, image]))
for image_file in captcha_image_files:
# Load the image and convert it to grayscale
image = cv2.imread(image_file)
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Add some extra padding around the image
image = cv2.copyMakeBorder(image, 20, 20, 20, 20, cv2.BORDER_REPLICATE)
# threshold the image (convert it to pure black and white)
thresh = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
# find the contours (continuous blobs of pixels) the image
contours = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# Hack for compatibility with different OpenCV versions
contours = contours[0] if imutils.is_cv2() else contours[1]
letter_image_regions = []
# Now we can loop through each of the four contours and extract the letter
# inside of each one
for contour in contours:
# Get the rectangle that contains the contour
(x, y, w, h) = cv2.boundingRect(contour)
# Compare the width and height of the contour to detect letters that
# are conjoined into one chunk
if w / h > 1.25:
# This contour is too wide to be a single letter!
# Split it in half into two letter regions!
half_width = int(w / 2)
charNames = ["1", "2", "3", "4", "5", "6", "7", "8", "9", "0",
"T", "U", "A", "D"]
# load the reference MICR image from disk, convert it to grayscale,
# and threshold it, such that the digits appear as *white* on a
# *black* background
ref = cv2.imread(args["reference"])
ref = cv2.cvtColor(ref, cv2.COLOR_BGR2GRAY)
ref = imutils.resize(ref, width=400)
ref = cv2.threshold(ref, 0, 255, cv2.THRESH_BINARY_INV |
cv2.THRESH_OTSU)[1]
# find contours in the MICR image (i.e,. the outlines of the
# characters) and sort them from left to right
refCnts = cv2.findContours(ref.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
refCnts = refCnts[0] if imutils.is_cv2() else refCnts[1]
refCnts = contours.sort_contours(refCnts, method="left-to-right")[0]
# extract the digits and symbols from the list of contours, then
# initialize a dictionary to map the character name to the ROI
refROIs = extract_digits_and_symbols(ref, refCnts,
minW=10, minH=20)[0]
chars = {}
# loop over the reference ROIs
for (name, roi) in zip(charNames, refROIs):
# resize the ROI to a fixed size, then update the characters
# dictionary, mapping the character name to the ROI
roi = cv2.resize(roi, (36, 36))
chars[name] = roi
# initialize a rectangular kernel (wider than it is tall) along with
# an empty list to store the output of the check OCR
def main(_argv):
# try access to GPU
physical_devices = tf.config.experimental.list_physical_devices('GPU')
if len(physical_devices) > 0:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
# Create Yolo model (Tiny or complete)
if FLAGS.tiny:
# light version (faster processing)
yolo = YoloV3Tiny(classes=FLAGS.num_classes)
else:
# Deeper more robust model
yolo = YoloV3(classes=FLAGS.num_classes)
# Load weights from a pretrained net
yolo.load_weights(FLAGS.weights).expect_partial()
logging.info('weights loaded')
# Get classes names
class_names = [c.strip() for c in open(FLAGS.classes).readlines()]
logging.info('classes loaded')
#Initialize deep sort.
deepsort = deepsort_rbc()
times = []
try:
vid = cv2.VideoCapture(int(FLAGS.video))
except:
vid = cv2.VideoCapture(FLAGS.video)
out = None
if FLAGS.output:
# by default VideoCapture returns float instead of int
width = int(vid.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(vid.get(cv2.CAP_PROP_FPS))
codec = cv2.VideoWriter_fourcc(*"MJPG")
out = cv2.VideoWriter(FLAGS.output, codec, fps, (width, height))
# try to determine the total number of frames in the video file
try:
prop = cv2.cv.CV_CAP_PROP_FRAME_COUNT if imutils.is_cv2() \
else cv2.CAP_PROP_FRAME_COUNT
total = int(vid.get(prop))
logging.info("{} total frames in video".format(total))
# print("[INFO] {} total frames in video".format(total))
# an error occurred while trying to determine the total
# number of frames in the video file
except:
logging.info("could not determine # of frames in video")
logging.info("No approx. completion time can be provided")
total = -1
# number of frames counter
cont = 0
# Write Yolo features from each frame to `images.tfrecords`.
#record_file = 'Test.tfrecords'
#with tf.io.TFRecordWriter(record_file) as writer:
while True:
_, img = vid.read()
if img is None:
logging.warning("Empty Frame")
time.sleep(0.1)
break
# print(cont)
img_in = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img_in = tf.expand_dims(img_in, 0)
img_in = transform_images(img_in, FLAGS.size)
t1 = time.time()
# use model to predict object bboxes
t1 = time.time()
ind, boxes, scores, classes, nums, feat = yolo(img_in)
t2 = time.time()
times.append(t2 - t1)
times = times[-20:]
# initialize our lists of detected bounding boxes, confidences,
# and class IDs, respectively
bboxes_p = []
confidences = []
classIDs = []
feat_track = []
scales = []
ids = []
# feature pre-location
feat_all = []
conti = 0
for j, indi in enumerate(ind):
if indi is not None:
for i in indi:
classID = int(classes[0][int(i[4])])
if class_names[classID] == "car" or class_names[
classID] == "truck":
sco = np.array(scores[0][int(i[4])])
box_p = np.array(boxes[0][int(i[4])])
# logging.info('\t{}, {}, {}'.format(class_names[classID],
# sco,box_p))
# Feature extraction
x, y = np.array(i[1:3])
feat_1 = feat[j][:, x, y, :][0]
feat_track.append(feat_1)
feat_all.append(
np.concatenate(
[
feat_1,
tf.expand_dims(classes[0][int(i[4])],
0), # object class
tf.expand_dims(i[4], 0)
],
axis=0)) # id object in frame
# objects allocation
ids.append(conti)
conti += 1
scales.append(j)
confidences.append(float(sco))
bboxes_p.append(box_p)
classIDs.append(classID)
# save output image
# img2 = draw_output(img, (bboxes_p,confidences,classIDs,ids,
# scales), class_names)
# cv2.imshow('output', img2)
# key = cv2.waitKey(0) & 0xFF
# if key == ord('q'):
# break
if FLAGS.save:
# Save features to TFRecord
if feat_all:
t_feat_all = tf.convert_to_tensor(feat_all)
# Process the frames into `tf.Example` messages.
tf_example = frame_example(t_feat_all, cont)
# Write to a `.tfrecords` file.
writer.write(tf_example.SerializeToString())
# ensure at least one detection exists
if bboxes_p:
# if cont == 46:
# print(cont)
# feed deepsort with detections and features
tracker, detections_class = deepsort.run_deep_sort(
img, np.asarray(bboxes_p), confidences, classIDs, scales, ids,
feat_track)
classIDs_nms = detections_class[1]
scales_nms = detections_class[2]
ids_nms = detections_class[3]
# prelocation employed detections
boxes_nms = []
sco_nms = []
for det in detections_class[0]:
# Append NMS detection boxes
boxes_nms.append(det.to_tlbr())
sco_nms.append(det.confidence)
# prelocation of tracked boxes
boxes_ds = []
id_ds = []
for track in tracker.tracks:
if not track.is_confirmed() or track.time_since_update > 1:
continue
#Append boxes and id.
boxes_ds.append(
track.to_tlbr()) #Get the corrected/predicted bounding box
id_ds.append(str(
track.track_id)) #Get the ID for the particular track.
# save output image
img = draw_YOLO(
img, (boxes_nms, sco_nms, classIDs_nms, ids_nms, scales_nms),
class_names)
if boxes_ds:
img = draw_DS(img, boxes_ds, id_ds)
img = cv2.putText(
img, "Time: {:.2f}ms, frame:{:d}".format(
sum(times) / len(times) * 1000, cont), (0, 30),
cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 2)
if FLAGS.output:
out.write(img)
cv2.imshow('output', img)
key = cv2.waitKey(100) & 0xFF
if key == ord('q'):
break
cont += 1
cv2.destroyAllWindows()
def select3(): # 학생들 정답 찾기 & 정답과 비교, 채점해서 출력
global studentSheet
studentAnswer = []
path = filedialog.askopenfilename()
studentSheet = cv2.imread(path, 0)
if not (os.path.isdir("answer")):
os.makedirs(os.path.join("answer"))
'''for i in range(0,len(position)):
studentAnswer.append(studentSheet[position[i][0]:position[i][1],position[i][2]:position[i][3]])
cv2.imwrite("answer\\"+str(i)+".jpg",studentAnswer[i])'''
#cv2.imshow("test",testSheet)
#cv2.imshow("answer",answerSheet)
#cv2.imshow("student",studentSheet)
# 이미지 서로 다른 부분 찾는 코드 정확히 모름
(score, diff) = compare_ssim(testSheet, studentSheet, full=True)
diff = (diff * 255).astype("uint8")
thresh = cv2.threshold(diff, 0, 255,
cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
# 정답지 세로로 분리
x1, y1, h1 = 0, 0, 0
num = 0
count = 0
startX, startY = 0, 0
img2 = []
row2 = []
row2.append([])
box2 = []
for c in cnts:
(x, y, w, h) = cv2.boundingRect(c)
box2.append(c)
if count == 0:
row2[num].append(count)
pass
else:
if abs(y1 - y) < 20:
row2[num].append(count)
else:
num = num + 1
row2.append([])
row2[num].append(count)
y1 = y
count += 1
zz = cv2.imread(path, 1)
# 정답지 세로로 분리된 것 중에 가로로 분리할 수 있는지 확인
a, b, c, d = 0, 0, 0, 0
for i in range(0, len(row2)):
minX = 10000
minY = 10000
maxX = 0
maxY = 0
for j in range(0, len(row2[i])):
a, b, c, d = cv2.boundingRect(box2[row2[i][j]])
if minX > a:
minX = a
if maxY < b + d:
maxY = b + d
if maxX < a + c:
maxX = a + c
if minY > b:
minY = b
if abs(maxY - minY) < 5:
pass
else:
img2.append(
studentSheet[minY:maxY,
minX:maxX]) # img == 최종 답안 단어들의 이미지를 저장한 리스트
cv2.rectangle(zz, (minX, minY), (maxX, maxY), (0, 0, 255), 3)
pos = []
pos.append(minY)
pos.append(maxY)
pos.append(minX)
pos.append(maxX)
position.append(pos)
cv2.imwrite("myanswe.jpg", zz)
for i in range(0, len(img2)):
cv2.imwrite("/Users/hcy/Desktop/GP/answer/" + "" + str(i) + ".jpg",
img2[i])
os.chdir("/Users/hcy/Desktop/GP/src/")
os.system('python main.py'
) # main.py 실행하면 answerImage에 있는 폴더 모두 실행, txt파일에 정답 저장
# blank 처리 - len(answerList) - studentAnswerList
#r = open('C:\\Users\yea\.spyder-py3\\answerSheet\\answerwordLists.txt','rt')
with codecs.open('/Users/hcy/Desktop/GP/src/answerwordLists.txt',
'r') as r:
while (1):
line = r.readline()
try:
escape = line.index('\n')
except:
escape = len(line)
if line:
studentAnswer.append(line[0:escape].replace(" ", ""))
else:
break
r.close()
answerList1 = []
with codecs.open('/Users/hcy/Desktop/GP/src/answerList.txt',
'r',
encoding='utf-8') as g:
while (1):
line = g.readline()
try:
escape = line.index('\r\n')
except:
escape = len(line)
if line:
answerList1.append(line[0:escape].replace(" ", ""))
else:
break
g.close()
print(answerList1)
print(studentAnswer)
score = len(studentAnswer)
correctNum = np.zeros(len(answerList1))
'''for i in range(0,len(studentAnswer)):
correct = 0
for j in range(0,len(answerList)): # answerList는 순서대로 저장되 있으므로 j에 따라 채점
if(studentAnswer[i] == answerList[j]):
correct = 1
correctNum[j]=1
break
if correct==0:ZZ
print(studentAnswer[i])
score-=1'''
for item in studentAnswer:
correct = 0
for j in range(0, len(answerList1)):
if eq(item, answerList1[j]):
correct = 1
correctNum[j] = 1
if correct == 0:
print(item)
score -= 1
print(score)
print(correctNum)
color = cv2.imread(path)
for i in range(0, len(correctNum)):
if (correctNum[i] == 1):
print("correct!")
#print(img2[i][0],img2[i][1])
cv2.circle(color, (int((position[i][3] + position[i][2]) / 2),
int((position[i][0] + position[i][1]) / 2)), 30,
(0, 0, 255), 5)
#cv2.circle(studentSheet,(int(x+w/2),int(y-h/2)),30,(0,0,255),-1)
else:
cv2.putText(color, " / ",
(int((position[i][3] + position[i][2]) / 2) - 70,
int((position[i][0] + position[i][1]) / 2) + 35),
cv2.FONT_HERSHEY_PLAIN, 5, (0, 0, 255), 5, cv2.LINE_AA)
cv2.putText(color,
str(score) + " / " + str(len(correctNum)), (1070, 1950),
cv2.FONT_HERSHEY_SIMPLEX, 3, (0, 0, 255), 5, cv2.LINE_AA)
color = cv2.resize(color, (850, 850))
cv2.imshow("Result", color)
cv2.imwrite("Result.jpg", color)
# cv2.circle(img,(447,63), 63, (0,0,255), -1)
cv2.waitKey(0)
cv2.destroyAllWindows()
def main():
cap = cv2.VideoCapture(vid_path)
status1, previous_frame = cap.read()
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
copy_frame = cv2.cvtColor(previous_frame, cv2.COLOR_BGR2GRAY)
fgbg = cv2.createBackgroundSubtractorMOG2()
hsv = np.zeros_like(previous_frame)
hsv[..., 1] = 255
t = 20
red = 30
check_red = 1
start = 0
radiuce_up_limit = 60
radiuce_low_limit = 30
i = 0
while (i < total_frames - 1):
ret, frame = cap.read()
i = i + 1
frame1 = frame.copy()
current_frame = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)
current_frame = cv2.GaussianBlur(current_frame, (var_blur, var_blur),
0)
# frame differening
frame_diff = cv2.absdiff(current_frame, copy_frame)
ret, binary_image1 = cv2.threshold(frame_diff, 3, 255,
cv2.THRESH_BINARY)
# Background Subtraction
binary_image3 = fgbg.apply(current_frame)
# combination of two methods
final_binary = cv2.bitwise_and(binary_image3, binary_image1)
lab_val = 255
n_labels, img_labeled, lab_stats, _ = \
cv2.connectedComponentsWithStats(final_binary, connectivity=8,
ltype=cv2.CV_32S)
if check_red == 1:
red = red + 10
if red > radiuce_up_limit:
check_red = 0
else:
red = red - 10
if red == radiuce_low_limit:
check_red = 1
if lab_stats[1:, 4].size > 2:
# print(lab_stats[1:,4].size)
re = lab_stats[1:, 4].argsort()[-2:][::-1] + 1
largest_mask = np.zeros(final_binary.shape, dtype=np.uint8)
largest_mask[img_labeled == re[0]] = lab_val
cnts1 = cv2.findContours(largest_mask.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts1 = cnts1[0] if imutils.is_cv2() else cnts1[1]
largest_mask[img_labeled == re[1]] = lab_val
cnts2 = cv2.findContours(largest_mask.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts2 = cnts2[0] if imutils.is_cv2() else cnts2[1]
if len(cnts2) > 1:
X1 = cnts2[0][0]
X2 = cnts2[1][0]
cX1 = X1[0][0]
cY1 = X1[0][1]
cX2 = X2[0][0]
cY2 = X2[0][1]
# distance between obj1 and obj2
dist1 = math.sqrt((cX1 - cX2)**2 + (cY1 - cY2)**2)
if dist1 < 90:
cX2 = cX1
cY2 = cY1
radiuce_up_limit = 100
else:
radiuce_up_limit = 60
cv2.circle(frame, (cX1, cY1), red, (0, 255, 255), 3)
cv2.circle(frame, (cX2, cY2), red, (0, 255, 255), 3)
cv2.putText(frame, 'Breathing', (10, 40), cv2.FONT_HERSHEY_SIMPLEX,
1, (0, 255, 255), 1, cv2.LINE_AA)
cv2.imshow('Frame', frame)
else:
t = t + 1
if t > 40:
if lab_stats[1:, 4].size > 0 and start == 1:
t = 0
cv2.putText(frame, 'Not Breathing', (10, 40),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 1,
cv2.LINE_AA)
cv2.imshow('Frame', frame)
else:
cv2.circle(frame, (cX1, cY1), red, (0, 255, 255), 3)
cv2.circle(frame, (cX2, cY2), red, (0, 255, 255), 3)
cv2.putText(frame, 'Breathing', (10, 40),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 1,
cv2.LINE_AA)
cv2.imshow('Frame', frame)
previous_frame = current_frame
k = cv2.waitKey(1) & 0xff
if k == 27:
break
cap.release()
cv2.destroyAllWindows()
def segment_chars(plate_img, fixed_width):
"""
extract Value channel from the HSV format
of image and apply adaptive thresholding
to reveal the characters on the license plate
"""
V = cv2.split(cv2.cvtColor(plate_img, cv2.COLOR_BGR2HSV))[2]
thresh = cv2.adaptiveThreshold(value, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY, 11, 2)
thresh = cv2.bitwise_not(thresh)
# resize the license plate region to
# a canoncial size
plate_img = imutils.resize(plate_img, width=fixed_width)
thresh = imutils.resize(thresh, width=fixed_width)
bgr_thresh = cv2.cvtColor(thresh, cv2.COLOR_GRAY2BGR)
# perform a connected components analysis
# and initialize the mask to store the locations
# of the character candidates
labels = measure.label(thresh, neighbors=8, background=0)
charCandidates = np.zeros(thresh.shape, dtype='uint8')
# loop over the unique components
characters = []
for label in np.unique(labels):
# if this is the background label, ignore it
if label == 0:
continue
# otherwise, construct the label mask to display
# only connected components for the current label,
# then find contours in the label mask
labelMask = np.zeros(thresh.shape, dtype='uint8')
labelMask[labels == label] = 255
cnts = cv2.findContours(labelMask, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
# ensure at least one contour was found in the mask
if len(cnts) > 0:
# grab the largest contour which corresponds
# to the component in the mask, then grab the
# bounding box for the contour
c = max(cnts, key=cv2.contourArea)
(boxX, boxY, boxW, boxH) = cv2.boundingRect(c)
# compute the aspect ratio, solodity, and
# height ration for the component
aspectRatio = boxW / float(boxH)
solidity = cv2.contourArea(c) / float(boxW * boxH)
heightRatio = boxH / float(plate_img.shape[0])
# determine if the aspect ratio, solidity,
# and height of the contour pass the rules
# tests
keepAspectRatio = aspectRatio < 1.0
keepSolidity = solidity > 0.15
keepHeight = heightRatio > 0.5 and heightRatio < 0.95
# check to see if the component passes
# all the tests
if keepAspectRatio and keepSolidity and keepHeight and boxW > 14:
# compute the convex hull of the contour
# and draw it on the character candidates
# mask
hull = cv2.convexHull(c)
cv2.drawContours(charCandidates, [hull], -1, 255, -1)
_, contours, hier = cv2.findContours(charCandidates, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
if contours:
contours = sort_cont(contours)
# value to be added to each dimension
# of the character
addPixel = 4
for c in contours:
(x, y, w, h) = cv2.boundingRect(c)
if y > addPixel:
y = y - addPixel
else:
y = 0
if x > addPixel:
x = x - addPixel
else:
x = 0
temp = bgr_thresh[y:y + h + (addPixel * 2),
x:x + w + (addPixel * 2)]
characters.append(temp)
return characters
else:
return None
def get_images():
if request.method == 'GET':
return render_template('imagediff.html')
if request.method == 'POST':
try:
#Receive the incoming data and split it into two image files
imageData = json.loads(request.json)
image1 = imageData['image1']
image2 = imageData['image2']
except Exception as ex:
imageData = json.loads(request.data)
image1 = imageData['image1']
image2 = imageData['image2']
try:
#Read the image string and convert it into jpeg for the first image
ImageALocation = os.path.join('ImageA.jpg')
with open(ImageALocation, "wb") as newFileA:
newFileA.write(base64.b64decode(image1))
newFileA.close()
#Read the image string and convert it into jpeg for the second image
ImageBLocation = os.path.join('ImageB.jpg')
with open(ImageBLocation, "wb") as newFileB:
newFileB.write(base64.b64decode(image2))
newFileB.close()
#Call the Image Compare function
#result = compare_images(ImageALocation,ImageBLocation)
###########################################################################################################################
imageA = cv2.imread(ImageALocation, -1)
imageB = cv2.imread(ImageBLocation, -1)
'''imageA = cv2.imread(ImageALocation,0)
imageB = cv2.imread(ImageBLocation,0)'''
grayA = cv2.cvtColor(imageA, cv2.COLOR_BGR2GRAY)
grayB = cv2.cvtColor(imageB, cv2.COLOR_BGR2GRAY)
# compute the Structural Similarity Index (SSIM) between the two
# images, ensuring that the difference image is returned
(score, diff) = compare_ssim(grayA, grayB, full=True)
diff = (diff * 255).astype("uint8")
#print("SSIM: {}".format(score))
strvar = format(score)
if strvar == "1.0":
result = strvar
else:
# threshold the difference image, followed by finding contours to
# obtain the regions of the two input images that differ
thresh = cv2.threshold(diff, 0, 255, cv2.THRESH_BINARY_INV
| cv2.THRESH_OTSU)[1]
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
# loop over the contours
# compute the bounding box of the contour and then draw the
# bounding box on both input images to represent where the two
# images differ
for c in cnts:
(x, y, w, h) = cv2.boundingRect(c)
cv2.rectangle(imageA, (x, y), (x + w, y + h), (0, 0, 255),
2)
cv2.rectangle(imageB, (x, y), (x + w, y + h), (0, 0, 255),
2)
# show the output images
ImageBLocation = os.path.join("Modified.jpg")
cv2.imwrite(ImageBLocation, imageB)
result = ImageBLocation
###############################################################################################################################
if result == "1.0":
return result
else:
#Open the result image and convert to base64 string for transmission
with open(result, "rb") as resultFile:
data = base64.b64encode(resultFile.read())
resultFile.close()
#Package the image string in Json format and send back to caller
jsonOut = str(data.decode())
dump = json.dumps(jsonOut)
load = json.loads(dump)
return load
except Exception as ex:
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
print(exc_type, fname, exc_tb.tb_lineno)
jsonOut = {
'ErrorMessage':
str(ex) + str(exc_type) + str(fname) + str(exc_tb.tb_lineno)
}
dump = json.dumps(jsonOut)
load = json.loads(dump)
return jsonify(load)
dim = (width, height)
image = cv2.resize(img, dim, interpolation=cv2.INTER_AREA)
marker = find_marker(image)
focalLength = (marker[1][0] * KNOWN_DISTANCE) / KNOWN_WIDTH
# print(focalLength)
# loop over the images
for imagePath in sorted(paths.list_images("images")):
# load the image, find the marker in the image, then compute the
# distance to the marker from the camera
img = cv2.imread(imagePath)
scale_percent = 30
width = int(img.shape[1] * scale_percent / 100)
height = int(img.shape[0] * scale_percent / 100)
dim = (width, height)
image = cv2.resize(img, dim, interpolation=cv2.INTER_AREA)
marker = find_marker(image)
inches = distance_to_camera(KNOWN_WIDTH, focalLength, marker[1][0])
print(inches)
# draw a bounding box around the image and display it
box = cv2.cv.BoxPoints(marker) if imutils.is_cv2() else cv2.boxPoints(
marker)
box = np.int0(box)
cv2.drawContours(image, [box], -1, (0, 255, 0), 2)
cv2.putText(image, "%.2fft" % (inches / 12),
(image.shape[1] - 200, image.shape[0] - 20),
cv2.FONT_HERSHEY_SIMPLEX, 2.0, (0, 255, 0), 3)
cv2.imshow("image", image)
cv2.waitKey(0)