def optflowHornSchunk(new: np.ndarray,
ref: np.ndarray,
uv,
smoothing=0.01) -> np.ndarray:
if cv2 is not None:
"""
http://docs.opencv.org/modules/legacy/doc/motion_analysis.html
Note that smoothness parameter for cv.CalcOpticalFlowHS needs to be SMALLER than matlab
to get similar result. Useless when smoothness was 1 in OpenCV, but it's 1 in Matlab!
"""
cvref = cv2.fromarray(ref)
cvnew = cv2.fromarray(new)
# result is placed in u,v
# matlab vision.OpticalFlow Horn-Shunck has default maxiter=10, terminate=eps, smoothness=1
cv2.CalcOpticalFlowHS(
cvref, cvnew, False, uv[0], uv[1], smoothing,
(cv2.CV_TERMCRIT_ITER | cv2.CV_TERMCRIT_EPS, 8, 0.1))
# reshape to numpy float32, xpix x ypix x 2
flow = dstack((asarray(uv[0]), asarray(uv[1])))
else: # use Python method
u, v = HornSchunck(ref, new)
flow = dstack((u, v))
return flow
def getDisparity(imgLeft, imgRight, method="BM"):
gray_left = cv.cvtColor(imgLeft, cv.COLOR_BGR2GRAY)
gray_right = cv.cvtColor(imgRight, cv.COLOR_BGR2GRAY)
print(gray_left.shape)
c, r = gray_left.shape
if method == "BM":
sbm = cv.StereoBM_create()
disparity = cv.stereoRectify(c, r, cv.CV_32F)
sbm.SADWindowSize = 11
sbm.preFilterType = 1
sbm.preFilterSize = 5
sbm.preFilterCap = 61
sbm.minDisparity = -50
sbm.numberOfDisparities = 112
sbm.textureThreshold = 507
sbm.uniquenessRatio = 0
sbm.speckleRange = 8
sbm.speckleWindowSize = 0
gray_left = cv.fromarray(gray_left)
gray_right = cv.fromarray(gray_right)
cv.FindStereoCorrespondenceBM(gray_left, gray_right, disparity, sbm)
disparity_visual = cv.CreateMat(c, r, cv.CV_8U)
cv.Normalize(disparity, disparity_visual, 0, 255, cv.CV_MINMAX)
disparity_visual = np.array(disparity_visual)
elif method == "SGBM":
sbm = cv.StereoSGBM()
sbm.SADWindowSize = 9
sbm.numberOfDisparities = 0
sbm.preFilterCap = 63
sbm.minDisparity = -21
sbm.uniquenessRatio = 7
sbm.speckleWindowSize = 0
sbm.speckleRange = 8
sbm.disp12MaxDiff = 1
sbm.fullDP = False
disparity = sbm.compute(gray_left, gray_right)
disparity_visual = cv.normalize(disparity,
alpha=0,
beta=255,
norm_type=cv.cv.CV_MINMAX,
dtype=cv.cv.CV_8U)
return disparity_visual
def plotptrn(self, ptrnlst, xres=0, yres=0):
'''
functions plots the modules and returns an image
e.g. xres=5,yres=5 resuts in a 611x1221 image
:param ptrnfile: result of the functions patternfiles
'''
if xres == 0 or yres == 0:
xres = self.xpermm
yres = self.ypermm
# i diode j pixel, np.int32 --> input to array
xcor = np.fromfunction(lambda i, j: self.fxpos(i, j) * xres,
(20 * self.nmod, ptrnlst[0].shape[1]),
dtype=np.int32)
ycor = np.fromfunction(lambda i, j: self.fypos(i, j) * yres,
(20 * self.nmod, ptrnlst[0].shape[1]),
dtype=np.int32)
# here the output of the array is defined
xcor = xcor.astype(np.int32, copy=False)
ycor = ycor.astype(np.int32, copy=False)
#ycor=ycor+abs(ycor.min())
# x and y cannot be negative
if xcor.min() < 0:
xcor += abs(xcor.min())
if ycor.min() < 0:
ycor += abs(ycor.min())
# number of pixels ptrnfile.shape[1]
a = np.zeros((ycor.max() + 1, xcor.max() + 1))
for i in range(0, 20 * self.nmod):
#adding prevents zeroes from overwriting ones
a[ycor[i, :], xcor[i, :]] += ptrnlst[i // 20][i % 20, :]
img = cv2.fromarray(np.uint8(a * (255 / 3)))
return img
def __getitem__(self, index):
dataset = self.train_dataset if self.mode == True else self.test_dataset
img_path, label_path = dataset[index]
# Uniform image size
#image = Image.open(img_path).convert("RGB")
image = cv2.imread(img_path)
#label = Image.open(label_path).convert("L")
label = cv2.imread(label_path， 0)
# Image resized to the same dimension
image, label = Compose(
[FreeScale(self.resize)])(image, label)
if self.mode:
if self.trainsform is not None:
image, label = self.trainsform(image, label)
# Convert it to pytorch style
image = img_transform(image)
mask = mask_transform(label)
#edge = mask_transform(Image.fromarray(edge_contour(np.asarray(label))))
edge = mask_transform(cv2.fromarray(edge_contour(np.asarray(label))))
return image, mask, edge
else:
image = img_transform(image)
mask = mask_transform(label)
return image, mask
def show_seg_result(image, label, seg_label):
"""show (image, label, ground_truth)"""
# prepare img
npimage = np.array(torchvision.utils.make_grid(image))
# npimage /= 255 # normailze value
nplabel = np.array((torchvision.utils.make_grid(label)))
# nplabel /= 255 # normailze value
npseg_label = np.array(torchvision.utils.make_grid(seg_label.detach()))
npseg_label = npseg_label > 0.5
npseg_label = np.uint8(npseg_label * 255)
# npseg_label /= 255 # normailze value
# show image
print('Image: {}'.format(npimage.shape))
# # use plt
# plt.imshow(np.transpose(npimage, (1,2,0)), cmap='Accent')
# plt.show()
# use cv2
img = cv2.fromarray(npimage)
cv2.namedWindow("Image")
cv2.imshow("Image", img)
cv2.waitKey(0)
print('Ground truth: {}'.format(nplabel.shape))
# # use plt
# plt.imshow(np.transpose(nplabel, (1,2,0)), cmap='Greys')
# plt.show()
# use cv2
label = cv2.fromarray(nplabel)
cv2.namedWindow("Ground_truth")
cv2.imshow("Ground_truth", label)
cv2.waitKey(0)
print('Seg result: {}'.format(npseg_label.shape))
# # use plt
# plt.imshow(np.transpose(npseg_label, (1,2,0)), cmap='Accent')
# plt.show()
# use cv2
seg_label = cv2.fromarray(npseg_label)
cv2.namedWindow("Seg result")
cv2.imshow("Seg result", seg_label)
cv2.waitKey(0)
cv2.destroyAllWindows()
def logpolar(src, center, magnitude_scale=40):
mat1 = cv.fromarray(numpy.float64(src))
mat2 = cv.CreateMat(src.shape[0], src.shape[1], mat1.type)
cv.LogPolar(mat1, mat2, center, magnitude_scale, \
cv.CV_INTER_CUBIC+cv.CV_WARP_FILL_OUTLIERS)
return numpy.asarray(mat2)
def detect(image):
image_faces = []
bitmap = cv.fromarray(image)
faces = cv.HaarDetectObjects(bitmap, cascade, cv.CreateMemStorage(0))
if faces:
for (x, y, w, h), n in faces:
image_faces.append(image[y:(y + h), x:(x + w)])
#cv2.rectangle(image,(x,y),(x+w,y+h),(255,255,255),3)
return image_faces
def cal_grad(B):
B = B[0,...]
H, W = B.shape
#for i in range(H):
# for j in range(W):
B_cv2 = cv2.fromarray(B)
B_lap = cv2.Laplacian(img,cv2.CV_64F)
return np.array(B_lap)
def template_match(img, refimg, confidence=0.6, xwin=19, ywin=19):
"""
Return all matches of refimg inside img, using Template Matching.
(Gratefully) borrowed from:
http://stackoverflow.com/questions/7670112/finding-a-subimage-inside-a-numpy-image/9253805# 9253805
Input:
obj img: A numpy array representing an image
obj refimg: A numpy array representing the reference image
float confidence: threshold value (from [0,1]) for template
matching
Output:
A tuple of (x,y) coodinates, w.r.t the coordinate system of
img.
"""
# OpenCV requires either uint8, or float, but with floats it got
# buggy and failed badly (I think it had to do with it not
# correctly handling when 'img' had no decimals, but 'refimg'
# had decimal expansions, which I suppose means that internally
# OpenCV.matchTemplate does exact integer comparisons.
img = img.astype('uint8')
refimg = refimg.astype('uint8')
I = cv.fromarray(img)
ref = cv.fromarray(refimg)
# I = cv.fromarray(np.copy(img))
# ref = cv.fromarray(np.copy(refimg))
I_s = cv.CreateMat(I.rows, I.cols, I.type)
cv.Smooth(I, I_s, cv.CV_GAUSSIAN, param1=xwin, param2=ywin)
ref_s = cv.CreateMat(ref.rows, ref.cols, ref.type)
cv.Smooth(ref, ref_s, cv.CV_GAUSSIAN, param1=xwin, param2=ywin)
# img = np.array(img, dtype='uint8')
# refimg = np.array(refimg, dtype='uint8')
result = cv.CreateMat(I_s.rows - ref_s.rows + 1,
I_s.cols - ref_s.cols + 1, cv.CV_32F)
cv.MatchTemplate(I_s, ref_s, result, cv.CV_TM_CCOEFF_NORMED)
# result = cv2.matchTemplate(img, refimg, cv2.TM_CCOEFF_NORMED)
# result is a 'similarity' matrix, with values from -1.0 (?) to 1.0,
# where 1.0 is most similar to the template image.
result_np = np.asarray(result)
match_flatidxs = np.arange(result_np.size)[
(result_np > confidence).flatten()]
return [flatidx_to_pixelidx(flatidx, result_np.shape) for flatidx in match_flatidxs]
def fastResize(I, rszFac, sig=-1):
if rszFac == 1:
return I
else:
Icv = cv.fromarray(np.copy(I))
I1cv = cv.CreateMat(int(math.floor(
I.shape[0] * rszFac)), int(math.floor(I.shape[1] * rszFac)), Icv.type)
cv.Resize(Icv, I1cv)
Iout = np.asarray(I1cv)
if sig > 0:
Iout = gaussian_filter(Iout, sig)
return Iout
def analyzeimage(old_image_file):
old_image = Image.open(old_image_file)
old_image_arr = np.array(old_image)
nir_img = np.array[2]
green_img = np.array[1] - nir_img
built_up_index = (green_img - 2 * nir_img) / (green_img + 2 * nir_img)
built_up_image = cv2.fromarray(built_up_index)
cv2.imwrite('static/temp.jpg', built_up_image)
# analyze the image 'old_image' here
# save the analyzed image to 'static/temp.jpg'
# return the path of the new image file
return "static/temp.jpg"
def img_from_buffer(buffer):
np_arr = numpy.fromstring(buffer, 'uint8')
np_mat = cv2.imdecode(np_arr, 0)
return cv2.fromarray(np_mat)
rec.read("recognizer\\trainingData.yml")
id=0
font=cv2.FONT_HERSHEY_SIMPLEX
while(True):
# Capture frame-by-frame
ret, img = cam.read()
# Our operations on the frame come here
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Detect faces in the image
faces = faceDetect.detectMultiScale(gray,1.3,5)
print("Found {0} faces!".format(len(faces)))
# Draw a rectangle around the faces
for (x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)
id,conf=rec.predict(gray[y:y+h,x:x+w])
cv2.putText(cv2.fromarray(img),str(id),(x,y+h),font,255)
# Display the resulting frame
cv2.imshow('Face', img)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything done, release the capture
cam.release()
cv2.destroyAllWindows()
recognizer.read('trainer/trainer.yml')
cascadePath = "Classifiers/face.xml"
faceCascade = cv2.CascadeClassifier(cascadePath)
path = 'dataSet'
cam = cv2.VideoCapture(0)
fontFace = cv2.FONT_HERSHEY_SIMPLEX
fontScale = 1
fontColor = (255, 255, 255)
while True:
ret, im = cam.read()
gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(gray,
scaleFactor=1.2,
minNeighbors=5,
minSize=(100, 100),
flags=cv2.CASCADE_SCALE_IMAGE)
for (x, y, w, h) in faces:
nbr_predicted, conf = recognizer.predict(gray[y:y + h, x:x + w])
cv2.rectangle(im, (x - 50, y - 50), (x + w + 50, y + h + 50),
(225, 0, 0), 2)
if (nbr_predicted == 7):
nbr_predicted = 'Obama'
elif (nbr_predicted == 2):
nbr_predicted = 'Anirban'
cv2.PutText(cv2.fromarray(im),
str(nbr_predicted) + "--" + str(conf), (x, y + h), font,
255) #Draw the text
cv2.imshow('im', im)
cv2.waitKey(10)
def detect(image):
bitmap = cv2.fromarray(image)
import cv2
import numpy as np
face_detect = cv2.CascadeClassifier('haarcascade_frontalface_alt.xml')
eyes_detect = cv2.CascadeClassifier('haarcascade_eye.xml')
cam = cv2.VideoCapture(0)
rec = cv2.face.LBPHFaceRecognizer_create()
rec.load('recognizer\\trainData.yml')
id = 0
while (True):
ret, img = cam.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_detect.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
id, conf = rec.predict(gray[y:y + h, x:x + w])
cv2.putText(cv2.fromarray(img), str(id), cv2.FONT_HERSHEY_PLAIN,
(x, y + h), (0, 255, 0))
cv2.imshow('Face', img)
if (cv2.waitKey() == ord('q')):
break
cam.release()
cv2.destroyAllWindows()
import numpy as np
import cv2
from matplotlib import pyplot as plt
img = cv2.imread('C:\Users\Rakib\Documents\messi5.jpg')
r,g,b = cv2.split(img)
img_bgr = cv2.merge([r,g,b])
b=cv2.fromarray(r)
print b.shape
#A =np.asanyarray(r)
# print r.dtype
# print r.shape
# print type(r)
#print A.shape
# cv2.imshow('image',img_bgr)
# cv2.imshow('image',img)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
#img = cv2.imread('messi5.jpg',0)
#
fig = plt.figure(1)
plt.subplot(221),plt.imshow(r,'gray'),plt.title('Original')
plt.subplot(222),plt.imshow(g,'gray'),plt.title('Original')
plt.subplot(223),plt.imshow(b,'gray'),plt.title('Original')
plt.subplot(224),plt.imshow(img_bgr,'gray'),plt.title('Reconstract')
# #plt.xticks([]), plt.yticks([]) # to hide tick values on X and Y axis # to remove x and y levels
plt.show()
# #How to add another plot
# fig = plt.figure(5)
# plt.subplot(121),plt.imshow(img,'gray'),plt.title('Original')
