def classify(imgName):
global img
global FP
global TP
loadImage(imgName+'.jpg')
loadMask(imgName+'.png')
img=cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
imgHeight=img.shape[0]
imgWidth=img.shape[1]
blank_image =np.zeros((imgHeight, imgWidth, 1), np.uint8)
for x in range(0, imgHeight):
for y in range(0,imgWidth):
A=img[x,y][0]
A=translate(A,0,255,0,31)
B=img[x,y][1]
B=translate(B,0,255,0,31)
histopeau=hist_fullHSVF[A,B]
histononpeau=hist_fullHSVR[A,B]
print (histopeau*percentPeau)/(histopeau*percentPeau+histononpeau*(100-percentPeau))
if((histopeau*percentPeau)/(histopeau*percentPeau+histononpeau*(100-percentPeau))>0.001):
blank_image[x,y]=255
if(mask[x,y]!=0):
TP+=1
else:
FP+=1
else:
blank_image[x,y]=0
cv2.imwrite('output2.jpg',blank_image)
print TP
print FP
sys.exit()
def main():
for fname in glob("left/*/*/ein/sceneModel/model.yml"):
print fname
f = open(fname)
lines = []
# ignore the %YAML:1.0, because the python parser doesn't handle 1.0.
f.readline()
for line in f:
# for some reason the python parser doesn't like this line either.
if "background_pose" in line:
continue
lines.append(line)
data = "\n".join(lines)
ymlobject = yaml.load(data)
#print ymlobject
scene = ymlobject["Scene"]
observed_map = GaussianMap.fromYaml(scene["observed_map"])
image = observed_map.toImage()
cv2.imwrite("observed.png", image)
cv2.imshow("observed map", image)
print "observed map: ", observed_map.width, "x", observed_map.height
dimage = readMatFromYaml(scene["discrepancy_magnitude"])
cv2.imshow("discrepancy magnitude", dimage)
cv2.waitKey(0)
cv2.destroyAllWindows()
def main():
''' This function applies your split script to images.
It will search through the images/part0 subfolder, and apply your splitting
function to each one. It will then save the resulting images.
'''
imagesfolder0 = os.path.abspath(os.path.join(os.curdir, 'images', 'part0'))
print '''Searching for images in {} folder
(will ignore red, green, or blue in the name)'''.format(imagesfolder0)
exts = ['.bmp', '.pbm', '.pgm', '.ppm', '.sr', '.ras', '.jpeg', '.jpg',
'.jpe', '.jp2', '.tiff', '.tif', '.png']
for dirname, dirnames, filenames in os.walk(imagesfolder0):
for filename in filenames:
name, ext = os.path.splitext(filename)
if ext in exts and 'red' not in name and 'green' not in name and \
'blue' not in name:
print "Splitting image {}.".format(filename)
img = cv2.imread(os.path.join(dirname, filename))
red, green, blue = split_rgb(img)
for values, color, channel in zip((red, green, blue),
('red', 'green', 'blue'), (2,1,0)):
img = np.zeros((values.shape[0], values.shape[1], 3),
dtype = values.dtype)
img[:,:,channel] = values
print "Writing image {}.".format(name+color+ext)
cv2.imwrite(os.path.join(dirname, name+color+ext), img)
def tryconcat():
flist=[]
for j in range(8):
for i in range(16):
flist.append(cv2.imread('tmp/%d_%d_%d.jpg'%(i,j,1)))
img=concat_images(flist, 8, 16)
cv2.imwrite("temp_hd.jpg", img)
def skeletonization(img):
'''
http://opencvpython.blogspot.ru/2012/05/skeletonization-using-opencv-python.html
'''
img = img.copy()
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
size = np.size(img)
skel = np.zeros(img.shape, np.uint8)
# ret, img = cv2.threshold(img, 127, 255, 0)
img = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 7, 2)
element = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3))
while True:
eroded = cv2.erode(img, element)
temp = cv2.dilate(eroded, element)
temp = cv2.subtract(img, temp)
skel = cv2.bitwise_or(skel, temp)
img = eroded.copy()
zeros = size - cv2.countNonZero(img)
if zeros == size:
break
cv2.imwrite("skel.png", skel)
return skel
def show_img(data, suffix, show):
global wnd_x
global wnd_y
if data == None:
return
image = data.astype(np.uint8)
if image.shape[0] == 6:
img = image.transpose(1,2,0)
img1 = img[:,:,:3]
img2 = img[:,:,3:]
else:
img1 = image[0]
img2 = image[1]
filename = 'img-1-'+suffix+'.jpg'
cv2.imwrite(filename, img1)
if show:
cv2.imshow("IMG 1", img1)
cv2.moveWindow("IMG 1", wnd_x, wnd_y)
filename = 'img-2-'+suffix+'.jpg'
cv2.imwrite(filename, img2)
if show:
cv2.imshow("IMG 2", img2)
wnd_x+=300
cv2.moveWindow("IMG 2", wnd_x, wnd_y)
wnd_x=10
wnd_y+=300
def detect(filename, cascade_file = "lbpcascade_animeface.xml"):
if not os.path.isfile(cascade_file):
raise RuntimeError("%s: not found" % cascade_file)
cascade = cv2.CascadeClassifier(cascade_file)
image = cv2.imread(filename)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.equalizeHist(gray)
faces = cascade.detectMultiScale(gray,
# detector options
scaleFactor = 1.1,
minNeighbors = 5,
minSize = (256, 256))
faceCount = 0;
for (x, y, w, h) in faces:
print (x, y, w, h)
# cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 255), 2)
if len(sys.argv) >= 3:
filanameWithExtension = os.path.basename(sys.argv[1])
filename = os.path.splitext(filanameWithExtension)[0]
savepath = os.path.join(sys.argv[2], filename + "-" + str(faceCount) + ".png");
cv2.imwrite(savepath, image[y:y+h,x:x+w])
faceCount = faceCount + 1
def run(self):
self.ratio = 100
self.resize_im = array([])
while True:
ch = 0xFF & cv2.waitKey(50)
if ch == 27:
break
if ch in [ord('s'), ord('S')]:
if self.resize_im.any():
cv2.imwrite(str(self.filename), self.resize_im)
print 'Image Saved.'
break
if ch in [ord('a'), ord('A')]:
newFilename = QFileDialog.getSaveFileName(self, 'Save File As...', os.getenv('HOME'),
"Images (*.png *.xpm *.jpg)")
if newFilename:
if self.resize_im.any():
cv2.imwrite(str(newFilename), self.resize_im)
print 'Image Saved as ' + newFilename
break
if ch in [ord('r'), ord('R')]:
ratio, ok = QInputDialog.getText(self, 'Set Image Ratio', 'Type ratio value in % (ex: 80): ')
if ok:
if int(ratio) > 0 and int(ratio) <= 100:
self.ratio = int(ratio)
self.resizeImage()
else:
print 'Image scale must be between 1 and 100'
cv2.destroyAllWindows()
def verb_showfilters(argv):
"""Dump source code of session"""
f = KITNNFile(argv[2])
s = f.getSession(argv[3]).d["snap/1"]
fine = s["data/2" ][...]
medium = s["data/8" ][...]
coarse = s["data/14"][...]
w = 3+(16*7+15*3)+3
h = 3+( 9*7+ 8*3)+3
img= np.zeros((h,w,3), dtype="uint8")
for i in xrange(9):
for j in xrange(16):
n = i*16+j
if i in [0,1,2]:
card = fine [n- 0]
elif i in [3,4,5]:
card = medium[n-48]
elif i in [6,7,8]:
card = coarse[n-96]
card -= np.min(card)
card /= np.max(card)
card = card.transpose(1,2,0)
img[3+i*10:3+i*10+7, 3+j*10:3+j*10+7] = 255*card
img = cv2.resize(img, (0,0), None, 8, 8, cv2.INTER_NEAREST)
cv2.imshow("Filters", img)
cv2.imwrite("Filters.png", img)
cv2.waitKey()
def featureMatching(input_keypoint_list, input_xy_list, input_img, template_img):
#inputとtemplateのkeypoint_binary,xy_listを読み込む
template_keypoint_list = []
template_keypoint = csv.reader(open('template_keypoint_binary.csv', 'r'))
for temp_key in template_keypoint:
template_keypoint_list.append(map(int,temp_key))
template_xy_list = []
template_xy = csv.reader(open('template_keypoint_xy.csv', 'r'))
for temp_xy in template_xy:
template_xy_list.append(map(int,temp_xy))
x_sum_img = len(input_img[0]) + len(template_img[0])
sum_img = np.zeros((len(input_img), x_sum_img), np.uint8)
sum_img = np.hstack((input_img, template_img))
template_add_xsize = []
#ハミング距離をとって0になったらマッチングとする
for temp_y in xrange(0,len(template_keypoint_list)):
for inp_y in xrange(0,len(input_keypoint_list)):
calc_list = template_keypoint_list[temp_y] - input_keypoint_list[inp_y]
#print 'template_xy' + str(template_xy_list[temp_y])
#print 'input_xy' + str(input_xy_list[inp_y])
if all((x == 0 for x in calc_list)) == True:
#マッチングした時のkeypoint_bineryと同じ場所のx,yの値を画像平面上にマッピングし、結果を出力
print 'get match'
temp_xy = template_xy_list[temp_y]
inp_xy = input_xy_list[inp_y]
template_add_xsize = [temp_xy[1] + len(input_img[0]),temp_xy[0]]
input_change_xy = [inp_xy[1],inp_xy[0]]
add_input = tuple(input_change_xy)
add_template = tuple(template_add_xsize)
cv2.circle(sum_img,add_input,3,(0,0,0),-1)
cv2.circle(sum_img,add_template,3,(0,0,255),-1)
#cv2.line(sum_img,add_input,add_template,(255,255,0),1)
#print 'add_input = ' +str(add_input) + ' add_template = ' +str(add_template)
print 'quit calc'
cv2.imwrite('sum_img.tif', sum_img)
def coolBlack():
IMAGE_WEIGHT = 0.5
image = cv2.imread("G:/Filters/wasim.jpg",0)
black = cv2.imread("G:/Filters/black5.jpg",0)
black = cv2.resize(black, image.shape[::-1])
res1 = cv2.addWeighted(image, IMAGE_WEIGHT, black, 1 - IMAGE_WEIGHT, 1)
#NORMALIZE IMAGES
image = np.float32(image)
black = np.float32(black)
image /= 255
black /= 200
res = image*black
cv2.imshow("RES", res)
cv2.waitKey(0)
fname = "G:/Filtes/temp.jpg"
cv2.imwrite(fname, res)
res = cv2.imread(fname, 0)
cv2.imshow("BLACK", res)
cv2.waitKey(0)
def exitFrame(self):
"""Draw to the window. Write to files. Release the frame."""
if self.frame is None:
self._enteredFrame = False
return
# Update the FPS estimate and related variables.
if self._framesElapsed == 0:
self._startTime = time.time()
else:
timeElapsed = time.time() - self._startTime
self._fpsEstimate = self._framesElapsed / timeElapsed
self._framesElapsed += 1
# Draw to the window, if any.
if self.previewWindowManager is not None:
if self.shouldMirrorPreview:
mirroredFrame = numpy.fliplr(self._frame).copy()
self.previewWindowManager.show(mirroredFrame)
else:
self.previewWindowManager.show(self._frame)
# Write to the image file, if any.
if self.isWritingImage:
cv2.imwrite(self._imageFilename, self._frame)
self._imageFilename = None
# Write to the video file, if any.
self._writeVideoFrame()
# Release the frame.
self._frame = None
self._enteredFrame = False
def describeNegativeHelper(imagePath, output):
outputImagePath = '%s%s' % (imagePath, outputImageExtension)
image = cv2.imread(imagePath)
# Save an equalized version of the image.
cv2.imwrite(outputImagePath, equalizedGray(image))
# Append the equalized image to the negative description.
print >> output, outputImagePath
def calibrate(filenames):
# termination criteria
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((6*7,3), np.float32)
objp[:,:2] = np.mgrid[0:7,0:6].T.reshape(-1,2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d point in real world space
imgpoints = [] # 2d points in image plane.
images = []
for filename in filenames:
# Find the chess board corners. If found, add object points, image points (after refining them)
img = cv2.imread(filename)
if img != None:
print "Loaded " + repr(filename)
else:
print "Unable to load image " + repr(filename)
continue
images.append(img)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, corners = cv2.findChessboardCorners(gray, (7,6), None)
if ret == True:
objpoints.append(objp)
corners2 = cv2.cornerSubPix(gray, corners, (11,11), (-1,-1), criteria)
imgpoints.append(corners2)
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1],None,None)
print "Loaded all images and calbulated calibration"
for i, img in enumerate(images):
img = images[i]
h, w = img.shape[:2]
newcameramtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (w,h), 1, (w,h))
dst = cv2.undistort(img, mtx, dist, None, newcameramtx)
x, y, w, h = roi
cv2.imwrite( 'calibrated/out_' + str(i) + '.png', dst[ y : y+h, x : x+w ])
print "Outputted calibrated image: 'calibrated/out_" + str(i) + ".png'"
def Get_wall_coordinates(source, dest):
x = source;
y = dest;
# *******************************Image Processing for identifying corners in the image**********************************
image = cv2.imread(x)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = np.float32(gray)
dst = cv2.cornerHarris(gray, 2, 3, 0.04)
image[dst > 0.01 * dst.max()] = [0, 0, 255]
cv2.imwrite('coordinateImage.jpg', image)
# *******************************Getting the pixel values of the detected corners***************************************
getCoord = np.where(np.all(image == (0, 0, 255), axis=-1))
coordinates = zip(getCoord[0], getCoord[1])
x = np.array(coordinates, dtype="int")
print ("Coordinates : ")
print (x)
# ******************************* Generating the text file *************************************************************
filename1 = open(y, "w")
filename1.write(str(coordinates))
filename1.close()
#plt.scatter(getCoord[0], getCoord[1])
#plt.show()
def extractDescriptor(fileName,descriptor,space,channel):
descriptorName = "../temp/faces/" + fileName[19:-4] + "-" + descriptor + "-descriptor.txt"
nameSpace = ""
nname = fileName
newName = nname[:-3] + "ppm"
sourceImg = cv2.imread(fileName)
if space == 0:
destImg = cv2.cvtColor(sourceImg, cv2.COLOR_BGR2HSV)
elif space == 1:
destImg = cv2.cvtColor(sourceImg, cv2.COLOR_BGR2RGB)
elif space == 2:
destImg = cv2.cvtColor(sourceImg, cv2.COLOR_BGR2YCR_CB)
elif space == 4:
destImg = cv2.cvtColor(sourceImg, cv2.COLOR_BGR2LAB)
else:
destImg = sourceImg
cv2.imwrite(nname,destImg)
command = "convert " + nname + " " + newName
os.system(command)
command = "rm " + fileName
os.system(command)
upperDesc = descriptor.upper()
if (upperDesc == "ACC") or (upperDesc == "BIC") or (upperDesc == "LCH") or (upperDesc == "CCV"):
command = "../descriptors/" + descriptor + "/source/bin/./" + descriptor + "_extraction " + newName + " " + descriptorName
else:
command = "../descriptors/" + descriptor + "/source/bin/./" + descriptor + "_extraction " + newName + " " + descriptorName + " " + str(channel)
os.system(command)
def run(self):
runFlag = True
cv2.namedWindow("TurtleCam 9000", 1)
while(runFlag):
image, timesImageServed = self.robot.getImage()
with self.lock:
if timesImageServed > 30:
if self.stalled == False:
print "Camera Stalled!"
self.stalled = True
else:
self.stalled = False
frame = self.mcs.update(image.copy())
cv2.imshow("TurtleCam 9000", frame)
code = chr(cv2.waitKey(10) & 255)
if code == 't':
cv2.imwrite("/home/macalester/catkin_ws/src/speedy_nav/res/captures/cap-" + str(datetime.now()) + ".jpg", image)
print "Image saved!"
if code == 'q':
break
with self.lock:
runFlag = self.runFlag
def locate_thumbnail(thumbnail_filename, source_filename, display=False, save_visualization=False,
save_reconstruction=False, reconstruction_format="jpg"):
thumbnail_basename, thumbnail_image = open_image(thumbnail_filename)
source_basename, source_image = open_image(source_filename)
logging.info("Attempting to locate %s within %s", thumbnail_filename, source_filename)
kp_pairs = match_images(thumbnail_image, source_image)
if len(kp_pairs) >= 4:
title = "Found %d matches" % len(kp_pairs)
logging.info(title)
H, mask = find_homography(kp_pairs)
new_thumbnail, corners, rotation = reconstruct_thumbnail(thumbnail_image, source_image, kp_pairs, H)
print(json.dumps({
"master": {
"source": source_filename,
"dimensions": {
"height": source_image.shape[0],
"width": source_image.shape[1],
}
},
"thumbnail": {
"source": thumbnail_filename,
"dimensions": {
"height": thumbnail_image.shape[0],
"width": thumbnail_image.shape[1],
}
},
"bounding_box": {
"height": corners[0][1] - corners[0][0],
"width": corners[1][1] - corners[1][0],
"x": corners[1][0],
"y": corners[0][0],
},
"rotation_degrees": rotation
}))
if save_reconstruction:
new_filename = "%s.reconstructed.%s" % (thumbnail_basename, reconstruction_format)
cv2.imwrite(new_filename, new_thumbnail)
logging.info("Saved reconstructed thumbnail %s", new_filename)
else:
logging.warning("Found only %d matches; skipping reconstruction", len(kp_pairs))
new_thumbnail = corners = H = mask = None
if display or save_visualization:
vis_image = visualize_matches(source_image, thumbnail_image, new_thumbnail, corners, kp_pairs, mask)
if save_visualization:
vis_filename = "%s.visualized%s" % os.path.splitext(thumbnail_filename)
cv2.imwrite(vis_filename, vis_image)
logging.info("Saved match visualization %s", vis_filename)
if display:
cv2.imshow(title, vis_image)
cv2.waitKey()
cv2.destroyAllWindows()
def showImageandPlot(N):
#A simple attenmpt to get mouse inputs and display images using matplotlib
I = cv2.imread('groundfloor.bmp')
drawI = I.copy()
#make figure and two subplots
fig = figure(1)
ax1 = subplot(1,2,1)
ax2 = subplot(1,2,2)
ax1.imshow(I)
ax2.imshow(drawI)
ax1.axis('image')
ax1.axis('off')
points = fig.ginput(5)
fig.hold('on')
for p in points:
#Draw on figure
subplot(1,2,1)
plot(p[0],p[1],'rx')
#Draw in image
cv2.circle(drawI,(int(p[0]),int(p[1])),2,(0,255,0),10)
ax2.cla
ax2.imshow(drawI)
draw() #update display: updates are usually defered
show()
savefig('somefig.jpg')
cv2.imwrite("drawImage.jpg", drawI)
def img_test(forest, feature_extractor, points, colors, filename, size=512,
radius=3, proba=True):
img = np.zeros((size, size, 3))
v_min = points.min()
v_max = points.max()
step = float(v_max - v_min) / img.shape[0]
grid = np.arange(v_min, v_max, step)
xy = np.array(list(itertools.product(grid, grid)))
features = feature_extractor.apply_all(xy)
if proba:
r = forest.predict_proba(features)
col = np.dot(r, colors)
else:
r = forest.predict(features).astype('int32')
col = colors[r]
img[((xy[:, 1] - v_min) / step).astype('int32'),
((xy[:, 0] - v_min) / step).astype('int32')] = col
points = ((points - v_min) / step).astype('int')
for p, r in zip(points, responses):
col = tuple(colors[int(r)])
cv2.circle(img, tuple(p), radius + 1, (0, 0, 0), thickness=-1)
cv2.circle(img, tuple(p), radius, col, thickness=-1)
cv2.imwrite(filename, img)
def show_circle(img, img_file_name):
temp_img = cv2.medianBlur(img, 5)
c_img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
circles = find_circle(temp_img, 10, 100, 30)
ix, iy = 0, 0
if circles is None:
circle_out_str = "_circle_%d-%d.png" % (ix, iy)
circle_out_file = re.sub(r'\.jpg', circle_out_str, img_file_name)
cv2.imwrite(circle_out_file, c_img)
return False
for i in circles[0, :]:
center = (i[0], i[1])
ix, iy = i[0], i[1]
radius = i[2]
circle_color = (0, 255, 0)
cv2.circle(c_img, center, radius, circle_color, 1)
center_color = (0, 0, 255)
cv2.circle(c_img, center, 2, center_color, 1)
circle_out_str = "_circle_%d-%d.png" % (ix, iy)
circle_out_file = re.sub(r'\.jpg', circle_out_str, img_file_name)
cv2.imwrite(circle_out_file, c_img)
return True
def show_results(self, image, results, imshow=True, deteted_boxes_file=None,
detected_image_file=None):
"""Show the detection boxes"""
img_cp = image.copy()
if deteted_boxes_file:
f = open(deteted_boxes_file, "w")
# draw boxes
for i in range(len(results)):
x = int(results[i][1])
y = int(results[i][2])
w = int(results[i][3]) // 2
h = int(results[i][4]) // 2
if self.verbose:
print(" class: %s, [x, y, w, h]=[%d, %d, %d, %d], confidence=%f" % (results[i][0],
x, y, w, h, results[i][-1]))
cv2.rectangle(img_cp, (x - w, y - h), (x + w, y + h), (0, 255, 0), 2)
cv2.rectangle(img_cp, (x - w, y - h - 20), (x + w, y - h), (125, 125, 125), -1)
cv2.putText(img_cp, results[i][0] + ' : %.2f' % results[i][5], (x - w + 5, y - h - 7),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1)
if deteted_boxes_file:
f.write(results[i][0] + ',' + str(x) + ',' + str(y) + ',' +
str(w) + ',' + str(h)+',' + str(results[i][5]) + '\n')
if imshow:
cv2.imshow('YOLO_small detection', img_cp)
cv2.waitKey(1)
if detected_image_file:
cv2.imwrite(detected_image_file, img_cp)
if deteted_boxes_file:
f.close()
def test():
# 3 cards on flat table
cards_3 = cv2.imread('images/set-3-texture.jpg')
# 5 cards at an angle
cards_5 = cv2.imread('images/set-5-random.jpg')
thresh_3 = s.get_binary(cards_3)
contours = s.find_contours(thresh_3, 3)
assert len(s.transform_cards(cards_3, contours, 3)) == 3
res5 = s.detect_cards(cards_5)
assert res5 is not None and len(res5) == 5
res3 = s.detect_cards(cards_3)
assert res3 is not None and len(res3) == 3
for i in range(len(res5)):
c = res5[i]
# util.show(c, 'card')
cv2.imwrite('images/cards/card-5-%d.jpg' % i, c)
for i in range(len(res3)):
c = res3[i]
# util.show(c, 'card')
cv2.imwrite('images/cards/card-3-%d.jpg' % i, c)
# for cards detected, get properties
for link in os.listdir('images/cards'):
img = cv2.imread('images/cards/%s' % link)
test_props(img)
print 'tests pass'
def det2negative(xmldoc, opath):
samples = xmldoc.getElementsByTagName('sample')
for sample in samples:
detections = sample.getElementsByTagName('detections')
detections = minidom.parseString(detections[0].toxml())
detections = detections.getElementsByTagName("_")
if len(detections) is not 0:
path = sample.getElementsByTagName("path")
path = path[0].firstChild.nodeValue
mat = cv2.imread(path)
mat_h, mat_w, _ = mat.shape
for detection in detections:
detection = detection.childNodes
for each in detection:
rect = eval(re.sub( r"\b\s\b", ",", re.sub(r"\n", "[", each.nodeValue )) + "]")
print rect
ratio = 64.0 / rect[3]
print rect, ratio
mat = resize(mat, int(round(mat_w * ratio)), int(round(mat_h * ratio)))
rect[0] = int(round(ratio * rect[0])) - 10
rect[1] = int(round(ratio * rect[1])) - 10
rect[2] = rect[0] + 32 + 20
rect[3] = rect[1] + 64 + 20
try:
cropped = mat[rect[1]:(rect[3]), rect[0]:(rect[2]), :]
img = os.path.join(opath, ''.join(random.choice(string.lowercase) for i in range(8)) + ".png")
cr_h, cr_w, _ = cropped.shape
if cr_h is 84 and cr_w is 52:
cv2.imwrite(img, cropped)
except:
pass
def listener():
global fnobj
rospy.init_node('reconocimiento', anonymous=True)
rospy.Subscriber("chatter", coordenadas, callback)
rate = rospy.Rate(50) #hz
cap = cv2.VideoCapture(0)
fnobj = 'logo.png'
opts, args = getopt.getopt(sys.argv[1:], '', ['feature='])
opts = dict(opts)
feature_name = opts.get('--feature', 'sift')
while not rospy.is_shutdown():
ret, frame = cap.read()
crop_img = frame[y:h,x:w]
cv2.imwrite("full.png",crop_img)
img1 = cv2.imread('full.png',0)
img2 = cv2.imread(fnobj, 0)
detector, matcher = init_feature(feature_name)
if detector != None:
print 'usando', feature_name
else:
print 'unknown feature:', feature_name
sys.exit(1)
kp1, desc1 = detector.detectAndCompute(img1, None)
kp2, desc2 = detector.detectAndCompute(img2, None)
print 'img1 - %d features, img2 - %d features' % (len(kp1), len(kp2))
match_and_draw('analisis', matcher,desc1,desc2,kp1,kp2,img1,img2)
cv2.waitKey(1)
def _save(self, *argv):
if not self._preview(): return
self.consoleNor("过程控制：图片正在处理中...")
filepath = tkFileDialog.asksaveasfilename()
filename = os.path.basename(filepath)
dotNum = filename.count(".")
if dotNum == 0:
if filename == "" : return
postfix = ".jpg"
elif dotNum >= 2 or not (filename.split(".")[1] in IMAGE_TYPES):
self.consoleErr("格式错误：文件名格式错误！")
return
else:
postfix = ""
if os.path.exists(self.filename) and fileCheck.isImage(self.filename):
try:
if self.algNum == 0:
im = tool_cv2.resize_linear(self.filename, self.wScale, self.hScale)
else:
im = tool_cv2.resize_cubic(self.filename, self.wScale, self.hScale)
self.consoleNor("过程控制：图片处理完毕，开始保存...")
cv2.imwrite(filepath+postfix, im)
self.consoleNor("过程控制：图片保存完毕...")
except:
self.consoleErr("过程控制：图片保存过程出现问题...")
else:
self.consoleErr("类型错误：打开的不是图片或者是文件不存在！")
def evaluate(im, algo, gt_illuminant, i, range_thresh, bin_num, dst_folder):
new_im = None
start_time = timeit.default_timer()
if algo=="grayworld":
new_im = cv2.xphoto.autowbGrayworld(im, 0.95)
elif algo=="nothing":
new_im = im
elif algo=="learning_based":
new_im = cv2.xphoto.autowbLearningBased(im, None, range_thresh, 0.98, bin_num)
elif algo=="GT":
gains = gt_illuminant / min(gt_illuminant)
g1 = float(1.0 / gains[2])
g2 = float(1.0 / gains[1])
g3 = float(1.0 / gains[0])
new_im = cv2.xphoto.applyChannelGains(im, g1, g2, g3)
time = 1000*(timeit.default_timer() - start_time) #time in ms
if len(dst_folder)>0:
if not os.path.exists(dst_folder):
os.makedirs(dst_folder)
im_name = ("%04d_" % i) + algo + ".jpg"
cv2.imwrite(os.path.join(dst_folder, im_name), stretch_to_8bit(new_im))
#recover the illuminant from the color balancing result, assuming the standard model:
estimated_illuminant = [0, 0, 0]
eps = 0.01
estimated_illuminant[2] = np.percentile((im[:,:,0] + eps) / (new_im[:,:,0] + eps), 50)
estimated_illuminant[1] = np.percentile((im[:,:,1] + eps) / (new_im[:,:,1] + eps), 50)
estimated_illuminant[0] = np.percentile((im[:,:,2] + eps) / (new_im[:,:,2] + eps), 50)
res = np.arccos(np.dot(gt_illuminant,estimated_illuminant)/
(np.linalg.norm(gt_illuminant) * np.linalg.norm(estimated_illuminant)))
return (time, (res / np.pi) * 180)
def camactivate ():
with picamera.PiCamera() as camera:
camera.resolution = (512,512)
time.sleep(2)
camera.capture('im1.jpg')
time.sleep(2)
camera.capture('im2.jpg')
time.sleep(2)
camera.capture('im3.jpg')
time.sleep(2)
camera.capture('im4.jpg')
im1=cv2.imread('im1.jpg',1)
im2=cv2.imread('im2.jpg',1)
im3=cv2.imread('im3.jpg',1)
im4=cv2.imread('im4.jpg',1)
cv2.putText(im1,'Cam1',(10,20),cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,255),2)
cv2.putText(im2,'Cam2',(10,20),cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,255),2)
cv2.putText(im3,'Cam3',(10,20),cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,255),2)
cv2.putText(im4,'Cam4',(10,20),cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,255),2)
cv2.namedWindow('Catenated Images',cv2.WINDOW_NORMAL)
concat=np.zeros((1024,1024,3),np.uint8)
concat[0:512,0:512,:]=im1
concat[0:512,512:1024,:]=im2
concat[512:1024,0:512,:]=im3
concat[512:1024,512:1024,:]=im4
cv2.imshow('Catenated Images',concat)
cv2.imwrite('concat.jpg',concat)
cv2.waitKey(0)
def mask(img):
biggest =None
max_area = 0
grey = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)
#blk = cv2.bitwise_not(grey)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))
res = cv2.morphologyEx(grey,cv2.MORPH_OPEN,kernel)
ret,thresh = cv2.threshold(grey,127,255,0)
contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
dest = np.zeros(thresh.shape, np.uint8)
print contours[::1]
print len(contours)
print hierarchy
for cnt in contours[::1]:
rect = cv2.minAreaRect(cnt)
points = cv2.cv.BoxPoints(rect)
points = np.int0(np.around(points))
#cv2.drawContours(dest, [cnt],0,(0,255,0),2)
#cv2.polylines(dest, [points], True,( 255,255,255), 2 )
cv2.fillPoly(orig, [cnt], (100,20,90), 4)
cv2.fillPoly(dest, [cnt], (255,255,255), 4)
x = cv2.cvtColor(dest,cv2.COLOR_GRAY2RGB)
cv2.imshow('contour-highlighted image.jpg', x)
cv2.imwrite("../../images/bound.jpg", x)
cv2.imshow('masked image', orig)
def _generate_training_set(img, image_file):
save_location = "images/training/"
_, img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY_INV)
_, regions = cv2.connectedComponents(img, img)
if not os.path.exists("../images/cc"):
os.makedirs("../images/cc")
cv2.imwrite("../images/cc/cc.png", regions)
cc = cv2.imread("../images/cc/cc.png", 0)
_, cc_vis = cv2.threshold(cc, 1, 255, cv2.THRESH_BINARY)
_, contours, hierarchy = cv2.findContours(cc_vis, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
idx = 0
for cnt in contours:
area = cv2.contourArea(cnt)
if area < 50 or area > 1000:
continue
if len(cnt) < 5:
continue
idx += 1
x, y, w, h = cv2.boundingRect(cnt)
roi = img[y: y + h, x: x + w]
name = image_file.split('.')[0]
inverted = (255 - roi)
cv2.imwrite(save_location + name + str(idx) + '.jpg', inverted)
cv2.waitKey(0)
# Label as found be YOLO CNN
label = str(category.decode("utf-8"))
# Draw Bounding Boxes around found RoIs, with different colours depending on found label/class
if label == "horse":
cv2.rectangle(img, (int(x - w / 2), int(y - h / 2)), (int(x + w / 2), int(y + h / 2)), (255, 0, 0),
thickness=2)
if label == "horse":
cv2.rectangle(img, (int(x - w / 2), int(y - h / 2)), (int(x + w / 2), int(y + h / 2)), (0, 0, 255),
thickness=2)
# Write label and confidence score above each BoundingBox
cv2.putText(img, f"{label} ({score})", (int(x), int(y)), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 0))
# Save image
cv2.imwrite("out.jpg", img)
# flag to label an entire video set (-v)
elif sys.argv[1] == "-v":
# video passed as path in arguments (argv[2])
filepath = sys.argv[2]
# read video
vid = cv2.VideoCapture(filepath)
try:
# Check if video could be opened; if yes, process it
if vid.isOpened():
# Find OpenCV version, to determine parameters that have been changed between versions
(major_ver, minor_ver, subminor_ver) = (cv2.__version__).split('.')
if int(major_ver) < 3:
import cv2
import numpy as np
img = cv2.imread(r'C:\Users\tchat\.spyder-py3\bumblebee.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
sift = cv2.xfeatures2d.SIFT_create()
kp = sift.detect(gray, None)
img = cv2.drawKeypoints(img,
kp,
None,
flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
cv2.imwrite('D:\Image Processing\HW3\sift_bum_keypoints.jpg', img)
surf = cv2.xfeatures2d.SURF_create(4000)
(kps, descs) = surf.detectAndCompute(gray, None)
img1 = cv2.drawKeypoints(gray,
kps,
None,
flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
cv2.imwrite('D:\Image Processing\HW3\surf_bum_keypoints.jpg', img1)
def __init__(self, image_directory):
print(image_directory)
img = cv2.imread(os.path.join(image_directory, "raw.png"))
self.height, self.width = img.shape[0:2]
self.horizontal_lines = []
self.vertical_lines = []
low_threshold = 100
high_threshold = 150
#edges = cv2.Canny(img, low_threshold, high_threshold)
edges = auto_canny(img)
cv2.imwrite("/tmp/edges.png", edges)
rho = 1 # distance resolution in pixels of the Hough grid
theta = np.pi / 180 # angular resolution in radians of the Hough grid
threshold = 15 # minimum number of votes (intersections in Hough grid cell)
min_line_length = 150 # minimum number of pixels making up a line
max_line_gap = 30 # maximum gap in pixels between connectable line segments
line_image = np.copy(img) * 0 # creating a blank to draw lines on
# Run Hough on edge detected image
# Output "lines" is an array containing endpoints of detected line segments
lines = cv2.HoughLinesP(edges, rho, theta, threshold, np.array([]),
min_line_length, max_line_gap)
try:
vertical_lines = [x for x in lines if x[0][0] == x[0][2]]
except TypeError:
return
# debounce vertical_lines lines
vertical_lines.sort(key=lambda a: a[0][0])
clean = vertical_lines[:1]
for i in range(len(vertical_lines) - 1):
line = vertical_lines[i + 1]
last = clean[-1]
if abs(last[0][0] - line[0][0]) < 20:
# two very close line, just keep the longest
last_len = last[0][1] - last[0][3]
line_len = line[0][1] - line[0][3]
if line_len > last_len:
clean = clean[:-1]
clean.append(line)
else:
clean.append(line)
vertical_lines = clean
# sort by size, biggest to smallest
vertical_lines.sort(key=lambda a: a[0][1] - a[0][3], reverse=True)
try:
main_line = vertical_lines[0]
except IndexError:
return
self.vertical_lines = [
VerticalLine(a[0][0], a[0][3], a[0][1]) for a in vertical_lines[:2]
]
# filter: y1 == y2 and x1 < main_line.x1 and y2 > main_line.y1 - and y1 > main_line.y1 and y1 < main_line.y2
horizontal_lines = [
a for a in lines
if a[0][1] == a[0][3] and (a[0][0] < main_line[0][0] < a[0][2]) and
(main_line[0][3] <= a[0][3] <= main_line[0][1])
]
# debounce horizontal_lines
horizontal_lines.sort(key=lambda a: a[0][1])
clean = horizontal_lines[:1]
for i in range(len(horizontal_lines) - 1):
line = horizontal_lines[i + 1]
last = clean[-1]
if abs(last[0][1] - line[0][1]) < 20:
# two very close line, just keep the longest
last_len = last[0][2] - last[0][0]
line_len = line[0][2] - line[0][0]
if line_len > last_len:
clean = clean[:-1]
clean.append(line)
else:
clean.append(line)
horizontal_lines = clean
# now filter the horizontal lines, keeping the ones at the clostest to top and bottom
try:
top_line = horizontal_lines[0]
bottom_line = horizontal_lines[-1]
self.horizontal_lines = [
HorizontalLine(x[0][1], x[0][0], x[0][2])
for x in [top_line, bottom_line]
]
except IndexError:
self.horizontal_lines = []
#
# horizontal_lines.sort(key=lambda a: a[0][2] - a[0][0], reverse=True)
# horizontal_lines = horizontal_lines[:2]
# horizontal_lines.sort(key=lambda a: a[0][1])
# Find the longest vertical line_image - ideally just one
for vline in self.vertical_lines:
_ = cv2.line(line_image, (vline.x, vline.y1), (vline.x, vline.y2),
(128 + int(random.random() * 128), 128, 128), 1)
for hline in self.horizontal_lines:
_ = cv2.line(line_image, (hline.x1, hline.y), (hline.x2, hline.y),
(128, 128 + int(random.random() * 128), 128), 1)
#
#
# lines_edges = cv2.addWeighted(img, 0.8, line_image, 1, 0)
cv2.imwrite(os.path.join(image_directory, "lines.png"), line_image)
def compute_disparity_from_z_info(
filepath_in: str,
filepath_out: str,
baseline_mm: float,
calibration_matrix: np.array,
res_x: int = bpy.context.scene.render.resolution_x,
res_y: int = bpy.context.scene.render.resolution_y,
scale: float = 1e4):
"""Compute disparity map from given z info (depth or range). Values are in .1 mm
By convention, disparity is computed from left camera to right camera (even for the right camera).
if z = depth, this is assumed to be stored as a PNG image of uint16 (compressed) values in .1 mm
If z = range, this is assumed to be stored as a EXR image of float32 (true range) values in meters
Args:
fpath_in(str): path to input file to read in
fpath_out(str): path to output file to write out. If None (explicitly given), only return (no save to file).
NOTE: make sure the filesystem tree exists
baseline_mm(float): baseline value (in mm) between parallel cameras setup
calibration_matrix(np.array): 3x3 camera calibration matrix. Used also to extract the focal lenght in pixel
NOTE: if z = range, depth must be computed. This requires additional arguments.
See amira_blender_rendering.utils.camera.project_pinhole_range_to_rectified_depth
Opt Args:
res_x(int): render/image x resolution. Default: bpy.context.scene.resolution_x
res_y(int): render/image y resolution. Default: bpy.context.scene.resolution_y
scale(float): value used to convert range (in m) to depth. Default: 1e4 (.1mm)
Returns:
np.array<np.uint16>: disparity map in pixels
"""
# check filepath_in to read file from
if '.png' in filepath_in:
logger.info(f'Loading depth from .PNG file {filepath_in}')
depth = (cv2.imread(filepath_in,
cv2.IMREAD_UNCHANGED)).astype(np.uint16)
elif '.exr' in filepath_in:
# in case of exr file we convert range to depth first
logger.info(f'Computing depth from EXR range file {filepath_in}')
depth = project_pinhole_range_to_rectified_depth(
filepath_in, None, calibration_matrix, res_x, res_y, scale)
else:
logger.error(
f'Given file {filepath_in} is neither of type PNG nor EXR. Skipping!'
)
return
# depth is always converted to mm and to float for precision computations
depth = depth.astype(np.float32) / (scale / 1e3)
logger.info('Computing disparity map')
# get focal lenght
focal_length_px = calibration_matrix[0, 0]
# disparity in pixels
disparity = baseline_mm * focal_length_px * np.reciprocal(depth)
# cast to uint16 for PNG format
disparity = (disparity).astype(np.uint16)
# write out if requested
if filepath_out is not None:
cv2.imwrite(
filepath_out, disparity,
[cv2.IMWRITE_PNG_STRATEGY, cv2.IMWRITE_PNG_STRATEGY_DEFAULT])
logger.info(f'Saved disparity map at {filepath_out}')
return disparity
def project_pinhole_range_to_rectified_depth(
filepath_in: str,
filepath_out: str,
calibration_matrix: np.array,
res_x: int = bpy.context.scene.render.resolution_x,
res_y: int = bpy.context.scene.render.resolution_y,
scale: float = 1e4):
"""
Given a depth map computed (as standard in ABR setup) using a perfect pinhole model,
compute the projected rectified depth
The function assumes to read-in from an Exr image format (32 bit true range values)
and to write-out in PNG image format (16 bit truncated depth values).
NOTE: depth values that might cause overflow in 16bit (i.e. >65k) are set to 0.
Args:
filepath_in(str): path to (.exr) file with range values (assumed in m) to load
filepath_out(str): path to (.png) file where (rectified) depth map is write to.
If None (explicitly given) only return converted image (no save).
NOTE: make sure the filesystem tree exists
calibration_matrix(np.array): 3x3 camera calibration matrix
Optional Args:
res_x(int): render/image x resolution (pixel). Default is initial scene resolution_x
res_y(int): render/image y resolution (pixel). Default is initial scene resolution_y
scale(float): scaling factor to convert range (in m) to depth. Default 1e4 (m to .1 mm)
Return:
np.array<np.uint16>: converted depth
"""
# quick check file type
if '.exr' not in filepath_in:
raise ValueError(f'Given input file {filepath_in} not of type EXR')
if '.png' not in filepath_out and filepath_out is not None:
raise ValueError(f'Given output file {filepath_out} not of tyep PNG')
# read range image (float32 values) in meters
# NOTE: the ANYDEPTH flag lead to a offset in the read value
# range_exr = (cv2.imread(filepath_in, cv2.IMREAD_ANYDEPTH)).astype(np.float32)
range_exr = (cv2.imread(filepath_in,
cv2.IMREAD_UNCHANGED))[:, :, 0].astype(np.float32)
logger.info('Rectifying pinhole range map into depth')
grid = np.indices((res_y, res_x))
u = grid[1].flatten()
v = grid[0].flatten()
uv1 = np.array([u, v, np.ones(res_x * res_y)])
K_inv = np.linalg.inv(calibration_matrix)
v_dirs_mtx = np.dot(K_inv, uv1).T.reshape(res_y, res_x, 3)
v_dirs_mtx_unit_inv = np.reciprocal(np.linalg.norm(v_dirs_mtx, axis=2))
depth_img = (range_exr * v_dirs_mtx_unit_inv * scale)
# remove overflow values
depth_img[depth_img > 65000] = 0
# cast to 16 bit
depth_img = depth_img.astype(np.uint16)
# write out if requested
if filepath_out is not None:
cv2.imwrite(
filepath_out, depth_img,
[cv2.IMWRITE_PNG_STRATEGY, cv2.IMWRITE_PNG_STRATEGY_DEFAULT])
logger.info(f'Saved (rectified) depth map at {filepath_out}')
return depth_img
def face_detection(DONE, saved_frames_counter, max_saved_frames):
cap = cv2.VideoCapture(2)
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
x_ratio = 0.3
y_ratio = 0.5
frame_id = 0
save_frame_path = "./face_frames/" # overwrite previus images
frame_container = [None] * max_saved_frames
frame_container_coords = [None] * max_saved_frames
while True:
_, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = detector(gray)
center = np.zeros(2, dtype=int)
x_Crop = 0
y_Crop = 0
save = False
for face in faces:
x1 = face.left()
y1 = face.top()
x2 = face.right()
y2 = face.bottom()
# cv2.rectangle(frame, (x1,y1),(x2,y2),(0,255,0),3)
y_Crop = int((x2 - x1) * (1.0 + x_ratio)) // 2
x_Crop = int((y2 - y1) * (1.0 + y_ratio)) // 2
landmarks = predictor(gray, face)
center = np.zeros(2, dtype=int)
for n in range(0, 68):
x = landmarks.part(n).x
y = landmarks.part(n).y
# cv2.circle(frame,(x,y),4,(255,0,0),-1)
center += np.array([x, y])
center = center // 68
save = random.random() > 0.5
# print(center)
# cv2.circle(frame, (center[0],center[1]), 4, (0,0,255), -1)
frame_coords = [
center[1] - x_Crop, center[1] + x_Crop, center[0] - y_Crop,
center[0] + y_Crop
]
cv2.imshow("LandMark Detection", frame)
if save and saved_frames_counter < max_saved_frames:
frame_container[saved_frames_counter] = copy.copy(frame)
frame_container_coords[saved_frames_counter] = copy.copy(
frame_coords)
saved_frames_counter += 1
if saved_frames_counter == max_saved_frames and not DONE:
print("The Coords")
shape = np.matrix(frame_container_coords).sum(
axis=0) // max_saved_frames
shape = shape.tolist()[0]
print(shape)
for ii in range(max_saved_frames):
alt_frame = frame_container[ii][shape[0]:shape[1],
shape[2]:shape[3], :]
cv2.imwrite(save_frame_path + "frame{:d}.jpg".format(ii),
alt_frame)
print("Working" + "".join(["."] * (ii % 4)))
print("DONE")
DONE = True
key = cv2.waitKey(1)
if key == 27:
break
frame_id += 1
cv2.destroyAllWindows()
cap.release()
im = img.crop((x, y, x + nw, y + nh))
return im, x, y, x + nw, y + nh
#Width and height of random crops
widthc = 1000
heightc = 700
i = 0
listFiles = os.listdir('.')
pattern = "*.png"
print(listFiles)
for entry in listFiles:
if fnmatch.fnmatch(entry, pattern):
#Resize image before performing random crops
im = Image.open(entry.format(i + 1))
im = im.resize((2707, 3828), Image.BILINEAR)
for j in range(0, numcrops):
image, xminc, yminc, xmaxc, ymaxc = randCrop(im, widthc, heightc)
imcv2 = np.array(image)
togray = cv2.cvtColor(imcv2, cv2.COLOR_RGB2GRAY)
image2 = np.zeros_like(image)
image2[:, :, 0] = togray
image2[:, :, 1] = togray
image2[:, :, 2] = togray
cv2.imwrite(
'newcrops/randscore{0}'.format(i + 1) +
'crop{0}.jpg'.format(j + 1), image2)
i += 1
# Detectando as faces na imagem
detecteds_face = classificator.detectMultiScale(image_gray,
scaleFactor=1.5,
minSize=(150, 150))
# Percorrendo todas as faces detectadas
for (x, y, l, a) in detecteds_face:
# Desenhando um retângulo sobre a face
cv2.rectangle(image, (x, y), (x + l, y + a), (0, 0, 255), 2)
# Caso aperte 'c', é tirado uma foto para análise
if cv2.waitKey(1) & 0xFF == ord('c'):
# Recortando somente o rosto
image_face = cv2.resize(image_gray[y:y + a, x:x + l], (220, 220))
# Escrevendo a imagem
cv2.imwrite(
"Pictures/pessoa." + str(id) + "." + str(image) + ".jpg",
image_face)
print("[Foto " + str(sample) + " capturada com sucesso]")
sample += 1
cv2.imshow("Face", image)
cv2.waitKey(1)
if sample >= number_samples + 1:
break
print("Faces capturadas com sucesso")
web_camera.release()
cv2.destroyAllWindows()
import time
import subprocess
import cv2
def detect_gender(img_path):
output = subprocess.check_output('th gender_detection.lua '+img_path, shell=True)
gender = output.split('\n')[-2].split('\t')[0]
return gender
if __name__ == "__main__":
img_path = "tmp.jpg"
#Starting the camera
vid = cv2.VideoCapture(0)
while(vid.isOpened()):
start_time = time.time()
ret, frame = vid.read()
if ret==True:
cv2.imwrite("tmp.jpg",frame)
gender = detect_gender(img_path)
print gender
if gender == 'Male' or gender == 'Female':
cv2.putText(frame,gender,(100,100), cv2.FONT_HERSHEY_SIMPLEX, 1,(0,255,0),2)
print("Time taken = ",time.time()-start_time)
cv2.imshow("Gender",frame)
cv2.waitKey(1)
color = green
text = "Result: " + status + " "
img, line_width = draw_text_in_image(img, text, (margin + line_width, v_pos), color, line_width)
if ovmax > 0: # if there is intersections between the bounding-boxes
bbgt = [ float(x) for x in gt_match["bbox"].split() ]
cv2.rectangle(img,(int(bbgt[0]),int(bbgt[1])),(int(bbgt[2]),int(bbgt[3])),light_blue,2)
if status == "MATCH!":
cv2.rectangle(img,(int(bb[0]),int(bb[1])),(int(bb[2]),int(bb[3])),green,2)
else:
cv2.rectangle(img,(int(bb[0]),int(bb[1])),(int(bb[2]),int(bb[3])),light_red,2)
cv2.imshow("Animation", img)
cv2.waitKey(20) # show image for 20 ms
# save image to results
output_img_path = results_files_path + "/images/" + class_name + "_prediction" + str(idx) + ".jpg"
cv2.imwrite(output_img_path, img)
#print(tp)
# compute precision/recall
cumsum = 0
for idx, val in enumerate(fp):
fp[idx] += cumsum
cumsum += val
cumsum = 0
for idx, val in enumerate(tp):
tp[idx] += cumsum
cumsum += val
#print(tp)
rec = tp[:]
for idx, val in enumerate(tp):
rec[idx] = float(tp[idx]) / gt_counter_per_class[class_name]
out1 = conv(dma,xlnk,in_buffer,1,0,precision,img,B1,W1,Configuration,in_buffer_x,in_buffer_bias,in_buffer_weight,in_buffer_param,in_buffer_out)
# test =np.divide(out1,precision).reshape(32,32,32)
# print(test[:,:,0])
out2 = conv(dma,xlnk,in_buffer,1,0,precision,out1,B2,W2,Configuration,in_buffer_x,in_buffer_bias,in_buffer_weight,in_buffer_param,in_buffer_out)
# test =np.divide(out2,precision).reshape(32,32,32)
# print(test[:,:,0])
out3 = conv(dma,xlnk,in_buffer,1,0,precision,out2,B3,W3,Configuration,in_buffer_x,in_buffer_bias,in_buffer_weight,in_buffer_param,in_buffer_out)
# test =np.divide(out3,precision).reshape(32,32,32)
# print(test[:,:,0])
out4 = conv(dma,xlnk,in_buffer,1,0,precision,out3,B4,W4,Configuration,in_buffer_x,in_buffer_bias,in_buffer_weight,in_buffer_param,in_buffer_out)
# test =np.divide(out4,precision).reshape(32,32,32)
# print(test[:,:,0])
out5 = conv(dma,xlnk,in_buffer,1,0,precision,out4,B5,W5,Configuration,in_buffer_x,in_buffer_bias,in_buffer_weight,in_buffer_param,in_buffer_out)
out6 = conv(dma,xlnk,in_buffer,1,0,precision,out5,B6,W6,Configuration,in_buffer_x,in_buffer_bias,in_buffer_weight,in_buffer_param,in_buffer_out)
out7 = conv(dma,xlnk,in_buffer,1,0,precision,out6,B7,W7,Configuration,in_buffer_x,in_buffer_bias,in_buffer_weight,in_buffer_param,in_buffer_out)
out8 = conv(dma,xlnk,in_buffer,1,0,precision,out7,B8,W8,Configuration,in_buffer_x,in_buffer_bias,in_buffer_weight,in_buffer_param,in_buffer_out)
out9 = conv(dma,xlnk,in_buffer,1,0,precision,out8,B9,W9,Configuration,in_buffer_x,in_buffer_bias,in_buffer_weight,in_buffer_param,in_buffer_out)
# test =np.divide(out9,precision).reshape(32,32,32)
# print(test[:,:,0])
out = conv(dma,xlnk,in_buffer,0,1,precision,out9,B10,W10,Configuration,in_buffer_x,in_buffer_bias,in_buffer_weight,in_buffer_param,in_buffer_out)
final = np.divide(out,precision).reshape(32,32,32)
img_final[inputsize*h:inputsize*(h+1),inputsize*w:inputsize*(w+1)] = final[:,:,0]
t3 = time.time()
print(t3-t1)
x = sigmoid_all(img_final,256,256)
final = img1*x+img2*(1-x)
# conv(dma,xlnk,in_buffer,relu,sigmoid,x,Bias,Weight,Configuration,in_buffer_x,in_buffer_bias,in_buffer_weight,in_buffer_param,in_buffer_out):
t2 = time.time()
print(t2-t1)
cv2.imwrite('./dma.jpg',x*255)
def dm_line_cycle_decoder(dm_image, output_dir, zxing_enable=True, **kwargs):
img = cv2.GaussianBlur(dm_image.gray, (3, 3), 0)
rets = dm_image.ret_candidate
use_avaliable = np.zeros(len(rets))
count = 0
messages = []
for ret in rets:
show = False
if use_avaliable[count] != 0:
return
use_avaliable[count] = 1
pts1 = ret.astype(np.float32)
width = int(
np.sqrt((pts1[0][0] - pts1[1][0])**2 +
(pts1[0][1] - pts1[1][1])**2))
height = int(
np.sqrt((pts1[2][0] - pts1[1][0])**2 +
(pts1[2][1] - pts1[1][1])**2))
aspect_ratio = float(width) / height
if aspect_ratio < 1:
width = min(256, max(128, width))
height = int(width / aspect_ratio)
else:
height = min(256, max(128, height))
width = int(height * aspect_ratio)
# aspect_ratio = float(width)/height
if height > width:
aspect_ratio = 1 / aspect_ratio
pts2 = np.float32([[0, 0], [width, 0], [width, height], [0, height]])
M = cv2.getPerspectiveTransform(pts1, pts2)
dst = cv2.warpPerspective(img, M, (width, height))
search_range_w = width // 10
search_width_w = max(4, search_range_w // 4)
search_range_h = height // 10
search_width_h = max(4, search_range_h // 4)
internal_top, num_top = get_signal(dst[:search_range_w, :], width,
search_width_w)
internal_btm, num_btm = get_signal(dst[-search_range_w:, :], width,
search_width_w)
internal_lft, num_lft = get_signal(
np.transpose(dst[:, :search_range_h]), height, search_width_h)
internal_rht, num_rht = get_signal(
np.transpose(dst[:, -search_range_h:]), height, search_width_h)
four_egde = sorted([num_top, num_btm, num_lft, num_rht])
sort_index = np.argsort(np.array([num_top, num_btm, num_lft, num_rht]))
if aspect_ratio <= 1.6:
# 方形重写。
large_ud = max(num_top, num_btm)
small_ud = min(num_top, num_btm)
large_lr = max(num_lft, num_rht)
small_lr = min(num_lft, num_rht)
if (abs(large_ud - small_ud * 2) == 0
or abs(large_ud - small_lr * 2)
== 0) and large_ud >= 10: # 上下最多跳跃点为正解时
num = POSSIBLE_SQAR_SIZE[np.argmin(
abs(POSSIBLE_SQAR_SIZE - large_ud))]
elif (abs(large_lr - small_lr * 2) == 0
or abs(large_lr - small_ud * 2)
== 0) and large_lr >= 10: # 左右最多跳跃点为正解时
num = POSSIBLE_SQAR_SIZE[np.argmin(
abs(POSSIBLE_SQAR_SIZE - large_lr))]
elif (abs(large_ud - large_lr * 2) == 0
or abs(large_lr - large_ud * 2)
== 0) and max(large_lr, large_ud) >= 10: # 存在双倍关系时
num = POSSIBLE_SQAR_SIZE[np.argmin(
abs(POSSIBLE_SQAR_SIZE - max(large_ud, large_lr)))]
else:
if large_ud > large_lr:
index = np.argmin(
abs(np.concatenate((POSSIBLE_SQAR_SIZE - large_ud * 2, POSSIBLE_SQAR_SIZE - large_ud)))) % \
POSSIBLE_SQAR_SIZE.shape[0]
num = POSSIBLE_SQAR_SIZE[index]
else:
index = np.argmin(
abs(np.concatenate((POSSIBLE_SQAR_SIZE - large_lr * 2, POSSIBLE_SQAR_SIZE - large_lr)))) % \
POSSIBLE_SQAR_SIZE.shape[0]
num = POSSIBLE_SQAR_SIZE[index]
grid_num = [num, num]
elif aspect_ratio > 1.6:
# 矩形重写。
# ud: up down
# lr: left right
large_ud = max(num_top, num_btm)
small_ud = min(num_top, num_btm)
large_lr = max(num_lft, num_rht)
small_lr = min(num_lft, num_rht)
if width > height:
# #-------------#
# | |
# #-------------#
# 长边的点数量筛选。
if abs(large_ud - small_ud * 2) == 0:
grid_num = POSSIBLE_RECT_SIZE[np.argmin(
abs(POSSIBLE_RECT_SIZE[:, 1] - large_ud))]
elif abs(large_lr - small_lr * 2) == 0:
grid_num = POSSIBLE_RECT_SIZE[np.argmin(
abs(POSSIBLE_RECT_SIZE[:, 0] - large_lr))]
else: # 最差的结果:直接取长边。
index = np.argmin(abs(np.concatenate(
(POSSIBLE_RECT_SIZE[:, 1] - large_ud * 2, POSSIBLE_RECT_SIZE[:, 1] - large_ud)))) % \
POSSIBLE_RECT_SIZE.shape[0]
grid_num = POSSIBLE_RECT_SIZE[index]
else:
# #----#
# | |
# | |
# | |
# | |
# #----#
if abs(large_lr - small_lr * 2) == 0:
grid_num = POSSIBLE_RECT_SIZE[np.argmin(
abs(POSSIBLE_RECT_SIZE[:, 1] - large_lr))]
elif abs(large_ud - small_ud * 2) == 0:
grid_num = POSSIBLE_RECT_SIZE[np.argmin(
abs(POSSIBLE_RECT_SIZE[:, 0] - large_ud))]
else: # 最差的结果:直接取长边。
index = np.argmin(abs(np.concatenate(
(POSSIBLE_RECT_SIZE[:, 1] - large_lr * 2, POSSIBLE_RECT_SIZE[:, 1] - large_lr)))) % \
POSSIBLE_RECT_SIZE.shape[0]
grid_num = POSSIBLE_RECT_SIZE[index]
grid_num = [grid_num[1], grid_num[0]]
inverse = False
# draw a pseudo binary code.
if min(grid_num) >= 4:
grid_size_y = height / grid_num[0]
grid_size_x = width / grid_num[1]
y_offset = int(grid_size_y // 5)
x_offset = int(grid_size_x // 5)
res_img = np.zeros((8 * grid_num[0], 8 * grid_num[1]))
sample_val = np.zeros((grid_num[0], grid_num[1]))
# 取样开始。
for i in range(grid_num[0]):
for j in range(grid_num[1]):
sample_val[i][j] = np.mean(
dst[int(i * grid_size_y) +
y_offset:int((i + 1) * grid_size_y) - y_offset,
int(j * grid_size_x) +
x_offset:int((j + 1) * grid_size_x) - x_offset])
mean_val = np.mean(sample_val)
mean_border = (
np.mean(sample_val[0, :]) + np.mean(sample_val[:, 0]) +
np.mean(sample_val[-1, :]) + np.mean(sample_val[:, -1])) / 4
# DOTO: 是否是dash需要判断。
num_white_points = len(sample_val[0, :][sample_val[0, :] > mean_border]) + \
len(sample_val[:, 0][sample_val[:, 0] > mean_border]) + \
len(sample_val[-1, :][sample_val[-1, :] > mean_border]) + \
len(sample_val[:, -1][sample_val[:, -1] > mean_border])
if num_white_points > grid_num[0] + grid_num[1]:
inverse = True
for i in range(grid_num[0]):
for j in range(grid_num[1]):
interest_area = sample_val[i][j]
if np.mean(interest_area) > mean_val:
if not inverse:
res_img[i * 8:(i + 1) * 8, j * 8:(j + 1) * 8] = 255
elif inverse:
res_img[i * 8:(i + 1) * 8, j * 8:(j + 1) * 8] = 255
# cv2.imwrite(out_dir + '/warp/init_res{}_{}.png'.format(os.path.basename(img_data.img_path), count), res_img)
# refine finder boundary pattern.
border_img = np.zeros_like(res_img)
for i in range(1, grid_num[1], 2):
border_img[-8:, i * 8:(i + 1) * 8] = 255
for i in range(1, grid_num[0], 2):
border_img[i * 8:(i + 1) * 8, -8:] = 255
border_type = ['d', 'd', 'd', 'd']
split_point = np.zeros(4)
# top
top_fake_border = res_img[0, :]
split_point[0] = np.sum(
np.abs(top_fake_border -
np.concatenate((res_img[0, 1:], np.zeros(1)))) / 255)
# bottom
btm_fake_border = res_img[-1, :]
split_point[1] = np.sum(
np.abs(btm_fake_border -
np.concatenate((res_img[-1, 1:], np.zeros(1)))) / 255)
# left
lft_fake_border = res_img[:, 0]
split_point[2] = np.sum(
np.abs(lft_fake_border -
np.concatenate((res_img[1:, 0], np.zeros(1)))) / 255)
# right
rht_fake_border = res_img[:, -1]
split_point[3] = np.sum(
np.abs(rht_fake_border -
np.concatenate((res_img[1:, -1], np.zeros(1)))) / 255)
index = np.argsort(split_point)
# print(np.argsort(split_point), split_point)
border_type[index[0]] = 'l'
border_type[index[1]] = 'l'
if border_type[0] == border_type[3] == 'l':
border_img = cv2.flip(border_img, 1)
elif border_type[1] == border_type[2] == 'l':
border_img = cv2.flip(border_img, 0)
elif border_type[1] == border_type[3] == 'l':
# print('trans')
border_img = cv2.flip(border_img, 1)
border_img = cv2.flip(border_img, 0)
res_img[-8:, :] = border_img[-8:, :]
res_img[:8, :] = border_img[:8, :]
res_img[:, -8:] = border_img[:, -8:]
res_img[:, :8] = border_img[:, :8]
res_img = cv2.copyMakeBorder(res_img,
50,
50,
50,
50,
cv2.BORDER_CONSTANT,
value=255)
# barcode = self.zxing_reader.decode()
# draw some lines to justify the hypothesis.
dst = cv2.cvtColor(dst, cv2.COLOR_GRAY2BGR)
# print(output_dir + '/warp/{}_{}.png'.format(os.path.basename(dm_image.img_path), count))
cv2.imwrite(
output_dir + '/warp/{}_{}.png'.format(
os.path.basename(dm_image.img_path), count), dst)
if min(grid_num) >= 4:
cv2.imwrite(
output_dir + '/recon/{}_{}_code.png'.format(
os.path.basename(dm_image.img_path), count), res_img)
cv2.imwrite(
output_dir + '/mirror/{}_{}_code.png'.format(
os.path.basename(dm_image.img_path), count),
cv2.flip(res_img, 0))
if zxing_enable:
message = zxing.BarCodeReader().decode(
output_dir + '/recon/{}_{}_code.png'.format(
os.path.basename(dm_image.img_path), count))
if message.raw != '':
return message.raw
count += 1
return None
######### drawing examples
fname='/data2/xuyangf/OcclusionProject/NaiveVersion/prunning/prunL3/dictionary_'+cat+'.pickle'
with open(fname,'rb') as fh:
assignment, centers, example, __= pickle.load(fh)
ss=int(math.sqrt(example[0].shape[0]/3))
fname ='/data2/xuyangf/OcclusionProject/NaiveVersion/new_vc_score/layer3/cat'+str(cat)+'.npz'
ff=np.load(fname)
img_vc=ff['vc_score']
vc_num=len(img_vc[0])
img_num=len(img_vc)
img_vc_avg=[]
for i in range(vc_num):
img_vc_avg.append(float(np.sum(img_vc[np.where(img_vc[:,i]!=-1),i]))/img_num)
img_vc_avg=np.asarray(img_vc_avg)
rindexsort=np.argsort(-img_vc_avg)
big_img = np.zeros((5+ss, 5+(ss+5)*len(pool4_merge), 3))
for j in range(len(pool4_merge)):
rnum=5
cnum=5+j*(ss+5)
select_vc=rindexsort[pool4_merge[j]]
big_img[rnum:rnum+ss, cnum:cnum+ss, :] = example[select_vc][:,0].reshape(ss,ss,3).astype(int)
fname = '/data2/xuyangf/OcclusionProject/NaiveVersion/example/tmpexample/'+str(cat)+'.png'
cv2.imwrite(fname, big_img)
def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max_size=640, max_subplots=16):
tl = 3 # line thickness
tf = max(tl - 1, 1) # font thickness
if os.path.isfile(fname): # do not overwrite
return None
if isinstance(images, torch.Tensor):
images = images.cpu().float().numpy()
if isinstance(targets, torch.Tensor):
targets = targets.cpu().numpy()
# un-normalise
if np.max(images[0]) <= 1:
images *= 255
bs, _, h, w = images.shape # batch size, _, height, width
bs = min(bs, max_subplots) # limit plot images
ns = np.ceil(bs ** 0.5) # number of subplots (square)
# Check if we should resize
scale_factor = max_size / max(h, w)
if scale_factor < 1:
h = math.ceil(scale_factor * h)
w = math.ceil(scale_factor * w)
# Empty array for output
mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8)
# Fix class - colour map
prop_cycle = plt.rcParams['axes.prop_cycle']
# https://stackoverflow.com/questions/51350872/python-from-color-name-to-rgb
hex2rgb = lambda h: tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))
color_lut = [hex2rgb(h) for h in prop_cycle.by_key()['color']]
for i, img in enumerate(images):
if i == max_subplots: # if last batch has fewer images than we expect
break
block_x = int(w * (i // ns))
block_y = int(h * (i % ns))
img = img.transpose(1, 2, 0)
if scale_factor < 1:
img = cv2.resize(img, (w, h))
mosaic[block_y:block_y + h, block_x:block_x + w, :] = img
if len(targets) > 0:
image_targets = targets[targets[:, 0] == i]
boxes = xywh2xyxy(image_targets[:, 2:6]).T
classes = image_targets[:, 1].astype('int')
gt = image_targets.shape[1] == 6 # ground truth if no conf column
conf = None if gt else image_targets[:, 6] # check for confidence presence (gt vs pred)
boxes[[0, 2]] *= w
boxes[[0, 2]] += block_x
boxes[[1, 3]] *= h
boxes[[1, 3]] += block_y
for j, box in enumerate(boxes.T):
cls = int(classes[j])
color = color_lut[cls % len(color_lut)]
cls = names[cls] if names else cls
if gt or conf[j] > 0.3: # 0.3 conf thresh
label = '%s' % cls if gt else '%s %.1f' % (cls, conf[j])
plot_one_box(box, mosaic, label=label, color=color, line_thickness=tl)
# Draw image filename labels
if paths is not None:
label = os.path.basename(paths[i])[:40] # trim to 40 char
t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
cv2.putText(mosaic, label, (block_x + 5, block_y + t_size[1] + 5), 0, tl / 3, [220, 220, 220], thickness=tf,
lineType=cv2.LINE_AA)
# Image border
cv2.rectangle(mosaic, (block_x, block_y), (block_x + w, block_y + h), (255, 255, 255), thickness=3)
if fname is not None:
mosaic = cv2.resize(mosaic, (int(ns * w * 0.5), int(ns * h * 0.5)), interpolation=cv2.INTER_AREA)
cv2.imwrite(fname, cv2.cvtColor(mosaic, cv2.COLOR_BGR2RGB))
return mosaic
import cv2
import itertools
import glob
import natsort
source1 = "./reside_dataset/reside_pix2pix/temp/"
source2 = "./reside_dataset/reside_pix2pix/temp1/"
destination = "./reside_dataset/reside_pix2pix/train/"
list1 = os.listdir(source1)
list2 = os.listdir(source2)
list1 = natsort.natsorted(list1,reverse=True)
list2 = natsort.natsorted(list2,reverse=True)
# for (i,j) in zip(list1,list2):
# print(i,j)
count = 1399
for (img1,img2) in zip(list1,list2):
im1 = cv2.imread(source1 + img1)
im2 = cv2.imread(source2 + img2)
width = int(im1.shape[1] * (256/620) )
height = int(im1.shape[0] * (256/460) )
dim = (width, height)
img_resized_1 = cv2.resize(im1, dim, interpolation = cv2.INTER_AREA)
img_resized_2 = cv2.resize(im2, dim, interpolation = cv2.INTER_AREA)
img_con_h = cv2.hconcat([img_resized_1, img_resized_2])
cv2.imwrite(destination + str(count).zfill(6) + ".png", img_con_h )
count = count+1
def main(argv):
tf.logging.set_verbosity(tf.logging.INFO)
common.print_args()
EKF_VIS_DIR = 'EKF_RESULTS'
ekf_vis_dir = os.path.join(FLAGS.ckpt_dir, EKF_VIS_DIR)
if not os.path.exists(ekf_vis_dir):
os.makedirs(ekf_vis_dir)
with tf.Graph().as_default():
dataset = generator.Dataset(
dataset_dir=os.path.join('dataset', 'cityscapes', 'tfrecord'),
dataset_name='cityscapes',
split_name=['val_fine'],
batch_size=1,
crop_size=[HEIGHT, WIDTH],
is_training=False,
model_variant='resnet_v1_101_beta',
min_scale_factor=0.50,
max_scale_factor=2.0,
should_shuffle=False,
should_repeat=False)
ite = dataset.get_one_shot_iterator()
sample = ite.get_next()
images, labels = sample[common.IMAGE], sample[common.LABEL]
module_order = search_model()
model = segModel.SegModel(
num_classes=dataset.num_classes,
model_variant='resnet_v1_101_beta',
output_stride=16,
backbone_atrous_rates=[1, 2, 4],
is_training=False,
ppm_rates=[1, 2, 3, 6],
module_order=module_order,
decoder_output_stride=4)
logits = model.build(images=images)
logits = tf.image.resize_bilinear(logits, tf.shape(images)[1:3], align_corners=True)
# logits = tf.nn.softmax(logits, axis=3)
height_ind = tf.range(HEIGHT, dtype=tf.int32)
width_ind = tf.range(WIDTH, dtype=tf.int32)
height_ind = tf.expand_dims(tf.math.logical_and(tf.math.greater_equal(height_ind, FLAGS.mask_heights[0]),
tf.math.greater_equal(FLAGS.mask_heights[1], height_ind)), 1)
width_ind = tf.expand_dims(tf.math.logical_and(tf.math.greater_equal(width_ind, FLAGS.mask_widths[0]),
tf.math.greater_equal(FLAGS.mask_widths[1], width_ind)), 0)
# height_ind = tf.expand_dims(tf.math.equal(height_ind, HEIGHT//4), 1)
# width_ind = tf.expand_dims(tf.math.equal(width_ind, WIDTH//4), 0)
height_map = []
width_map = []
for w in range(WIDTH):
height_map.append(height_ind)
for h in range(HEIGHT):
width_map.append(width_ind)
height_map = tf.concat(height_map, axis=1)
width_map = tf.concat(width_map, axis=0)
height_map = tf.cast(height_map, tf.float32)
width_map = tf.cast(width_map, tf.float32)
mask = tf.expand_dims(tf.math.multiply(height_map, width_map), axis=2)
m_concat = []
for _ in range(dataset.num_classes):
m_concat.append(mask)
mask = tf.concat(m_concat, axis=2)
masked_logits = tf.multiply(mask, logits)
grad = tf.gradients(masked_logits, [images])
########### SESSION CREATING PROCESS #############
checkpoints_iterator = tf.contrib.training.checkpoints_iterator(
FLAGS.ckpt_dir)
for checkpoint_path in checkpoints_iterator:
restorer = tf.train.Saver()
scaffold = tf.train.Scaffold(init_op=tf.global_variables_initializer(),
saver=restorer,
ready_for_local_init_op=None)
session_creator = tf.train.ChiefSessionCreator(
scaffold=scaffold,
master='',
checkpoint_filename_with_path=checkpoint_path)
with tf.train.MonitoredSession(
session_creator=session_creator, hooks=None) as sess:
grad_list = []
batch_num = 0
while not sess.should_stop():
im, l, g = sess.run([images, logits, grad])
grad_list.append(g[0])
im = im[0]
g = np.abs(g[0][0])
if batch_num == 0:
sum_g = g
else:
sum_g += g
batch_num += 1
g = normalizeImg(g)
im = normalizeImg(im)
img_path = os.path.join(ekf_vis_dir, '{}_img.jpg'.format(batch_num))
grad_path = os.path.join(ekf_vis_dir, '{}_grad.jpg'.format(batch_num))
cv2.imwrite(img_path, cv2.cvtColor(im*255, cv2.COLOR_RGB2BGR))
cv2.imwrite(grad_path, cv2.cvtColor(g*255, cv2.COLOR_RGB2BGR))
print('Processing {} done!'.format(batch_num))
if batch_num % 100 == 0:
print('100 second sleep')
time.sleep(100)
if batch_num >= 20:
break
break
sum_g = sum_g / batch_num
print('max: {:.3f}, min: {:.3f}'.format(np.max(sum_g), np.min(sum_g)))
sum_g = normalizeImg(sum_g)
binary_g = sum_g.copy()
binary_g[sum_g > np.mean(sum_g)] = 255
cv2.imwrite(os.path.join(ekf_vis_dir, 'average_grad.jpg'), cv2.cvtColor(sum_g*255, cv2.COLOR_RGB2BGR))
plt.title('average grad')
plt.imshow(sum_g)
plt.show()
cv2.imwrite(os.path.join(ekf_vis_dir, 'sum_grad_{}.jpg'.format(batch_num)), cv2.cvtColor(binary_g.astype(np.uint8), cv2.COLOR_RGB2BGR))
from keras.optimizers import Adam
from data_loader import load_cucumber
from train import normalize, denormalize
if __name__ == '__main__':
iterations = 100
input_dim = 30
anogan_optim = Adam(lr=0.001, amsgrad=True)
### 0. prepare data
X_train, X_test, y_test = load_cucumber()
X_train = normalize(X_train)
X_test = normalize(X_test)
input_shape = X_train[0].shape
### 1. train generator & discriminator
dcgan = DCGAN(input_dim, input_shape)
dcgan.load_weights('weights/generator_3999.h5', 'weights/discriminator_3999.h5')
for i, test_img in enumerate(X_test):
test_img = test_img[np.newaxis,:,:,:]
anogan = ANOGAN(input_dim, dcgan.g)
anogan.compile(anogan_optim)
anomaly_score, generated_img = anogan.compute_anomaly_score(test_img, iterations)
generated_img = denormalize(generated_img)
imgs = np.concatenate((denormalize(test_img[0]), generated_img[0]), axis=1)
cv2.imwrite('predict' + os.sep + str(int(anomaly_score)) + '_' + str(i) + '.png', imgs)
print(str(i) + ' %.2f'%anomaly_score)
with open('scores.txt', 'a') as f:
f.write(str(anomaly_score) + '\n')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
font = cv2.FONT_HERSHEY_SIMPLEX
bottomLeftCornerOfText = (10, 100)
fontScale = 1
fontColor = (0, 0, 0)
lineType = 2
cv2.putText(img, '98205849',
bottomLeftCornerOfText,
font,
fontScale,
fontColor,
lineType)
cv2.putText(gray, '98205849 ',
bottomLeftCornerOfText,
font,
fontScale,
fontColor,
lineType)
cv2.imshow('image', img)
cv2.waitKey(0)
cv2.imshow('image', gray)
k = cv2.waitKey(0)
if k == ord('e'): # wait for 'e' key to exit
cv2.destroyAllWindows()
elif k == ord('s'): # wait for 's' key to save and exit
cv2.imwrite('edited_img.png', img)
cv2.imwrite('edited_img1.png', gray)
cv2.destroyAllWindows()
def take_photos(photos_path):
'''
Take photos from a camera.
User is required to press SPACEBAR to take photos
Arguments:
1. photos_path: the path to store the photos
Returns:
1. img_num: total number of photos taken
'''
# NOTE: To handle non-consecutive image naming
# For example, the current image file names might be
# ['0001.jpg', '0004.jpg', '0006.jpg, 0007.jpg']
#
# When taking photos, the new naming should first use this set
# 0000.jpg, 0002.jpg, 0003.jpg, 0005.jpg
#
# then, followed by
# 0008.jpg, 0009.jpg, 0010.jpg ... and so on
photos_exist = [x for x in os.listdir(photos_path) if not x.startswith('.')]
num_photos_exist = len(photos_exist)
idx_required = []
idx_max = 0
# Check if there are photos in photos_path
if num_photos_exist > 0:
# Convert all photos name to integer value
idx = [ 0 if photo == "0000.jpg" else int(os.path.splitext(photo)[0].lstrip('0')) for photo in photos_exist ]
idx_max = max(idx)
# Find the missing integer between 0 to n (inclusive)
# These indices would be used for saving the image first
all_idx = list(range(0, idx_max+1))
idx_required = sorted( list(set(all_idx).difference(set(idx))) )
idx_max = idx_max + 1
frame_name = "Photo Taking"
cam = VideoStream(0).start()
cv2.namedWindow(frame_name)
cv2.moveWindow(frame_name, 500,200);
img_num = 0
while True:
frame = cam.read()
if frame.any() == None:
break
frame = resize(frame, width=1024)
frame = cv2.flip(frame, 1)
frame_with_text = np.copy(frame)
text = "Number of photos taken = " + str(img_num) + "/10"
cv2.putText(frame_with_text, text, (20, 450),
cv2.FONT_HERSHEY_TRIPLEX, 1, (255, 255, 0), 2)
text = "Press SPACEBAR to take photo"
cv2.putText(frame_with_text, text, (20, 500),
cv2.FONT_HERSHEY_TRIPLEX, 1, (255, 0, 255), 2)
text = "Press ESC to Exit"
cv2.putText(frame_with_text, text, (20, 550),
cv2.FONT_HERSHEY_TRIPLEX, 1, (0, 128, 0), 2)
cv2.imshow(frame_name, frame_with_text)
# TODO: Handle unicode input
# Now, if the keyboard is Chinese or Russian
# and when the keystroke (QWER ..etc) is pressed
# the program stops with assertion error
# k = cv2.waitKey(1)
k = cv2.waitKeyEx(1)
# ESC is pressed or 10 photos are taken
if k % 256 == 27 or img_num >= 10:
break
# SPACEBAR is pressed to take a photo
elif k % 256 == 32:
if len(idx_required) > 0:
name = str(idx_required.pop(0)).zfill(4)
else:
name = str(idx_max).zfill(4)
idx_max += 1
img_name = os.path.join(photos_path, name + ".jpg")
cv2.imwrite(img_name, frame)
img_num += 1
print("Image saved to " + img_name)
cam.stop()
cv2.destroyAllWindows()
return img_num
from sys import argv
script, filename = argv
import cv2
import numpy as np
import time
img = cv2.imread(filename)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# surf.hessianThreshold=3000
surf = cv2.SURF(3000)
print "**********88"
kp, res = surf.detectAndCompute(gray, None)
print res.shape
img = cv2.drawKeypoints(gray, kp)
#img = cv2.drawKeypoints(img,kp,None,(255,0,255),4)
print(len(kp))
cv2.namedWindow("SURF")
cv2.imshow("SURF", img)
cv2.imwrite("surf.jpg", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
# Export the frame within the being/end slice
video_filename = ann['video_filename']
video_file = glob.glob(join(VIDEO_DIR, '**/' + video_filename))[0]
video_output_dir = join(join(OUTPUT_VIDEO_DIR, str(n_objects)), os.path.splitext(video_filename)[0])
if not os.path.exists(video_output_dir):
os.makedirs(video_output_dir)
cap = cv2.VideoCapture(video_file)
success, frame_image = cap.read()
frame = 1
while success:
frame_file = join(video_output_dir, '%d.png'%(frame - frame_begin))
success, frame_image = cap.read()
if frame >= frame_begin and frame < frame_end:
cv2.imwrite(frame_file, frame_image)
frame += 1
print('Generated sliced video for %d/%d'%(ann_idx, len(annotation_files)))
#================================
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software
# and associated documentation files (the "Software"), to deal in the Software without restriction,
# including without limitation the rights to use, copy, modify, merge, publish, distribute,
# sublicense, and/or sell copies of the Software, and to permit persons to whom the Software
# is furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all copies
for c in range(C):
out[pad + y, pad + x, c] = np.median(tmp[y:y + K_size, x:x + K_size, c])
out = out[pad:pad + H, pad:pad + W].astype(np.uint8)
return out
# Average filter
# 作为对比
def average_filter(img, G=3):
out = img.copy()
H, W, C = img.shape
Nh = int(H / G)
Nw = int(W / G)
for y in range(Nh):
for x in range(Nw):
for c in range(C):
out[G * y:G * (y + 1), G * x:G * (x + 1), c] = np.mean(
out[G * y:G * (y + 1), G * x:G * (x + 1), c]).astype(np.int)
return out
# Read image
pic_name = input('picture name: ')
img = cv2.imread(pic_name)
# Median Filter and Average Filter
out1 = median_filter(img, K_size=3)
# Save result
cv2.imwrite("../report/media_out1.jpg", out1)
cv2.waitKey(0)
cv2.destroyAllWindows()
backbone.load_state_dict(torch.load('weights/backbone.pth')['net'])
backbone = backbone.to(device)
backbone.eval()
print('==> Processing data ...')
with torch.no_grad():
anchor_features = get_features(anchor_loader, len_anchor,
len(anchor_dataset))
test_features = get_features(test_loader, len_test, len(test_dataset))
similarity = anchor_features.dot(test_features.transpose())
result_dir = 'result'
if not os.path.exists(result_dir):
os.mkdir(result_dir)
for i in range(similarity.shape[1]):
top1 = np.argmax(similarity[:, i])
name = anchor_dataset.image_labels[top1]
prob = np.round(
(1.0 / (1 + np.exp(-9.8 * similarity[top1, i] + 3.763)) *
1.0023900077282935 + -1.290327970702342e-06),
decimals=4) * 100
# if prob < 60:
# continue
prob = f'{prob:.2f}'
src_img = cv2.imread(test_dataset.image_names[i])
rst_name = os.path.join(
result_dir, name + '-' + prob + '-' +
test_dataset.image_names[i].split('.')[0].split('/')[-1] + '.jpg')
cv2.imwrite(rst_name, src_img)
print('Saving {} ...'.format(rst_name))
# sample point to avoid out of memory
sample_int = sampleIntensities([x[:, :, i] for x in image_list])
# recover response curse
response_curve = gslove(image_list, sample_int, 100, i)
# reconstruct radiance map
tmp_i = reconstruct_radiance_map(image_list, response_curve, i)
tmp_i = cv2.normalize(tmp_i,
None,
alpha=0,
beta=255,
norm_type=cv2.NORM_MINMAX)
radiance_img.append(tmp_i)
radiance_img = np.array(radiance_img)
# output : reshape radiance image to rgb image
output = np.zeros((radiance_img.shape[1], radiance_img.shape[2], 3))
for i in range(3):
output[:, :, i] = radiance_img[i]
# TODO : tonemapping
# cv2.imwrite(out_dir+'test_out.hdr',output)
# output = tone_mapping(output, 1.5)
# output = cv2.normalize(output, None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX)
cv2.imwrite(out_dir + 'test_out.png', output)
def eval_with_plac(img_dir, det_net, num_imgs, image_ext, draw_imgs=False):
# 1. preprocess img
img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3]) # is RGB. not BGR
img_batch = tf.cast(img_plac, tf.float32)
img_batch = short_side_resize_for_inference_data(img_tensor=img_batch,
target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN,
length_limitation=cfgs.IMG_MAX_LENGTH)
if cfgs.NET_NAME in ['resnet152_v1d', 'resnet101_v1d', 'resnet50_v1d']:
img_batch = (img_batch / 255 - tf.constant(cfgs.PIXEL_MEAN_)) / tf.constant(cfgs.PIXEL_STD)
else:
img_batch = img_batch - tf.constant(cfgs.PIXEL_MEAN)
img_batch = tf.expand_dims(img_batch, axis=0)
detection_scores, detection_category, detection_boxes_angle = det_net.build_whole_detection_network(
input_img_batch=img_batch,
gtboxes_batch_h=None,
gtboxes_batch_r=None,
gt_encode_label=None)
init_op = tf.group(
tf.global_variables_initializer(),
tf.local_variables_initializer()
)
restorer, restore_ckpt = det_net.get_restorer()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(init_op)
if not restorer is None:
restorer.restore(sess, restore_ckpt)
print('restore model')
all_boxes_r = []
imgs = os.listdir(img_dir)
pbar = tqdm(imgs)
for a_img_name in pbar:
a_img_name = a_img_name.split(image_ext)[0]
raw_img = cv2.imread(os.path.join(img_dir,
a_img_name + image_ext))
raw_h, raw_w = raw_img.shape[0], raw_img.shape[1]
resized_img, det_boxes_r_, det_scores_r_, det_category_r_ = \
sess.run(
[img_batch, detection_boxes_angle, detection_scores, detection_category],
feed_dict={img_plac: raw_img[:, :, ::-1]}
)
if draw_imgs:
detected_indices = det_scores_r_ >= cfgs.VIS_SCORE
detected_scores = det_scores_r_[detected_indices]
detected_boxes = det_boxes_r_[detected_indices]
detected_categories = det_category_r_[detected_indices]
det_detections_r = draw_box_in_img.draw_boxes_with_label_and_scores(np.squeeze(resized_img, 0),
boxes=detected_boxes,
labels=detected_categories,
scores=detected_scores,
method=1,
in_graph=True)
save_dir = os.path.join('test_hrsc', cfgs.VERSION, 'hrsc2016_img_vis')
tools.mkdir(save_dir)
cv2.imwrite(save_dir + '/{}.jpg'.format(a_img_name),
det_detections_r[:, :, ::-1])
if det_boxes_r_.shape[0] != 0:
resized_h, resized_w = resized_img.shape[1], resized_img.shape[2]
det_boxes_r_ = forward_convert(det_boxes_r_, False)
det_boxes_r_[:, 0::2] *= (raw_w / resized_w)
det_boxes_r_[:, 1::2] *= (raw_h / resized_h)
det_boxes_r_ = backward_convert(det_boxes_r_, False)
x_c, y_c, w, h, theta = det_boxes_r_[:, 0], det_boxes_r_[:, 1], det_boxes_r_[:, 2], \
det_boxes_r_[:, 3], det_boxes_r_[:, 4]
boxes_r = np.transpose(np.stack([x_c, y_c, w, h, theta]))
dets_r = np.hstack((det_category_r_.reshape(-1, 1),
det_scores_r_.reshape(-1, 1),
boxes_r))
all_boxes_r.append(dets_r)
pbar.set_description("Eval image %s" % a_img_name)
# fw1 = open(cfgs.VERSION + '_detections_r.pkl', 'wb')
# pickle.dump(all_boxes_r, fw1)
return all_boxes_r
loaddir='sketch_png'
savedir='sketch_cls'
outsize=128
numclusters=5
finaldict={}
with open('categories.txt') as file:
for line in file:
ipdir=os.path.join(loaddir,line.strip())
classfile=os.listdir(ipdir)
classdata=np.zeros((len(classfile),outsize**2))
for cfile in range(len(classfile)):
ipfilename=os.path.join(ipdir,classfile[cfile])
tempimage=cv2.imread(ipfilename,0)
tempimage=cv2.resize(tempimage,(outsize,outsize))
classdata[cfile,:]=tempimage.ravel()
classfile[cfile]=classfile[cfile].split('.')[0]
classdata=classdata/255.
kmeansout=KMeans(n_clusters=numclusters,random_state=0).fit(classdata)
kmeanscls=list(kmeansout.labels_)
print str(Counter(kmeanscls))
finaldict[line.strip()]=dict(zip(classfile,kmeanscls))
opdir=os.path.join(savedir,line.strip())
if not os.path.exists(opdir):
os.mkdir(opdir)
outputfiles=kmeansout.cluster_centers_.reshape((numclusters,outsize,outsize))
for i in range(numclusters):
opfilename=os.path.join(opdir,line.strip()+'%'+str(i).zfill(2)+'.png')
cv2.imwrite(opfilename,(outputfiles[i,:,:]*255).astype(int))
print line.strip()+' Done......'
pickle.dump(finaldict,open('clusters.p','wb'))
import numpy as np
import cv2
import glob
import os
import sys
for filename in glob.glob("train/Seq01/imagesjpg/*.jpg"):#specify your own path
img = cv2.imread(filename)
img = cv2.cvtColor( img, cv2.COLOR_RGB2GRAY )
cv2.imwrite( filename, img )
font = cv2.FONT_HERSHEY_SIMPLEX
nm = nm.replace("V ",
"").replace("Liquid Suspension",
"Suspension").replace(" GENERAL", "")
ImOverlay1 = Im.copy()
ImOverlay1[:, :, 1][Lb == 1] = 0
cv2.putText(ImOverlay1, nm, (int(w / 3), int(h / 6)), font, 2,
(0, 255, 0), 2, cv2.LINE_AA)
ImOverlay1[:, :, 0][Lb == 1] = 255
OutMain[h * y:h * (y + 1), w * x:w * (x + 1)] = ImOverlay1
x += 1
if x >= mx:
x = 0
y += 1
h, w, d = OutMain.shape
cv2.imwrite(OutDir + "/" + name[:-4] + "_Main.png", OutMain)
fr = np.max([h / 700, w / 1400])
OIm = np.zeros([704, 1404, 3], np.uint8)
OutMain = cv2.resize(OutMain, (int(w / fr), int(h / fr)))
h, w, d = OutMain.shape
OIm[0:h, 0:w, :] = OutMain
print(OIm.shape)
MainCatsVideoWriter.write(OIm)
cv2.imwrite(OutDir + "/" + name[:-4] + "_Main.png", OIm)
cv2.imshow('Main Classes', OIm)
cv2.waitKey(25)
#------------------------------------Display all classes on the image----------------------------------------------------------------------------------
h, w, d = Im.shape
cat_name = one['cat_name']
labels.append("%s: %.2f"%(cat_name, float(one['score'])))
if not color_assign.has_key(cat_name):
this_color = color_queue.pop()
color_assign[cat_name] = this_color
# recycle it
color_queue.insert(0, this_color)
color = color_assign[cat_name]
box_colors.append(color)
else:
if args.show_only_result_frame:
continue
ori_im = cv2.imread(frame, cv2.IMREAD_COLOR)
new_im = draw_boxes(ori_im, boxes, labels, box_colors, font_scale=0.8,
font_thick=10, box_thick=2, bottom_text=False)
if args.show_frame_num:
# write the frame idx
cv2.putText(new_im, "# %d" % frameIdx,
(0, 20), cv2.FONT_HERSHEY_SIMPLEX,
1, (0, 255, 0), 2)
if args.show_only_result_frame:
filename = "%08d" % actual_count
actual_count += 1
target_file = os.path.join(target_path, "%s.jpg" % filename)
cv2.imwrite(target_file, new_im)
"default: piece_parameters.json",
default="piece_parameters.json")
ap.add_argument('-s', '--save', help="Path to the created pattern", required=True)
args = vars(ap.parse_args())
border, shape_height, rect_width, start_x, start_y, cyan, \
yellow, magenta, t_height, t_width = pattern_pars('piece_parameters.json')
c = 0
# the shape is symmetrical and the pattern depends on the initial parameters above
# when the first side is drawn the shape is rotated at 90 degrees and the next side is drawn in the same fashion
img = np.zeros((t_height, t_width, 3), np.uint8)
img[:] = (0, 0, 0)
while c < 4:
img = side_drawer(shape_height, rect_width, start_x, start_y, cyan, yellow, magenta)
cols, rows, dims = img.shape
M = cv2.getRotationMatrix2D((cols / 2, rows / 2), 90, 1)
img = cv2.warpAffine(img, M, (cols, rows))
c += 1
# Draw a white frame
cv2.rectangle(img, (0, 0), (t_height, t_width), (255, 255, 255), 120)
# Draw the ROI area
cv2.rectangle(img, (cyan, cyan), (380, 380), (255, 255, 255), -1)
# Save
name = os.path.join(args["save"], "piece.jpg")
cv2.imwrite(name, img)
# Display the image
cv2.imshow("img", img)
cv2.waitKey(0)