I am able to extract the landmark coordinates from results.pose_landmarks. Now, I want to send it to another device and there, using the x, y, z and visibility values, the datatype will be reconstructed so as to build the pose on a frame.
Store the iterated values in a list. Then use this snippet below,
from mediapipe.framework.formats import landmark_pb2
reconstructed = landmark_pb2.NormalizedLandmarkList(extracted_points_list)
This will reconstruct the entire datatype.
I am trying to apply a function to an Xarray dataset, using dataset.where mask to decide where to apply it. I am not sure how to do it.
Some context: the dataset has two variables (A and B), which are two overlapping raster images (same size and coordinates). I want to run a function (defined by me), that runs a calculation on each gridcell of the raster image, using the two variables. This is not a u_func, it is just a function that takes the values of the two overlapping gridcells, does some calculations and returns a third value I want to save to a third raster image (C).
How do I do that?
I have gotten this far, but it does not work because ds.A passes the entire array to the function, not just the value of ds.A in that particular gridcell:
def my_func(x, y):
..do things
return result
ds = xr.Dataset({
"A": xarray.open_rasterio("A.tif"),
"B": xarray.open_rasterio("B.tif"),
"C": xarray.open_rasterio("C.tif"),
})
ds.C.where(ds.A > 0, my_func(ds.A, ds.B))
Say you have the matrix given by three arrays, being:
x = N-dimensional array.
y = M-dimensional array.
And z is a set of "somewhat random" values from -0.3 to 0.3 in a NxM shape. I need to create a plot in which the x values are in the x-axis, y values are in the y-axis and using z as the source to indicate the intensity of each pixel with a color map.
So far, I have tried using
plt.contourf(x,y,z)
and the resulting plot is very nice for me (attached at the end of this paragraph), but a smoothing is automatically applied to the plot! I need to be able to distinguish the pixels and I cannot find a way to do it.
contourf result
I have also studied the possibility of using
ax.matshow(z)
in order to sucesfully see the pixels... but then I am struggling trying to personalize the x and y axis, since only the index of the pixel is shown (see below).
matshow result
Would you please give me some ideas? Thank you.
Without more information on your x,y data it's hard to know, but I would guess you are looking for pcolormesh.
plt.pcolormesh(x,y,z)
This would take the x and y data as input and hence shows the z data at the appropriate coordinates.
You can use imshow with the keyword interpolation='nearest'.
plt.imshow(z, interpolation='nearest')
i have an error at this line:neigh.fit(X, y) :
ValueError: setting an array element with a sequence.
I checked fit function and X is: {array-like, sparse matrix, BallTree, cKDTree}
My X is a list of list with first element solidity number and second elemnt humoment list (7 cells).
If i change and i take only first humoment number for having a pure list of list
give this error: query data dimension must match BallTree data dimension.
My code:
listafeaturevector = list()
path = 'imgknn/'
for infile in glob.glob( os.path.join(path, '*.jpg') ):
print("current file is: " + infile )
gray = cv2.imread(infile,0)
element = cv2.getStructuringElement(cv2.MORPH_CROSS,(6,6))
graydilate = cv2.erode(gray, element)
ret,thresh = cv2.threshold(graydilate,127,255,cv2.THRESH_BINARY_INV)
imgbnbin = thresh
#CONTOURS
contours, hierarchy = cv2.findContours(imgbnbin, cv2.RETR_TREE ,cv2.CHAIN_APPROX_SIMPLE)
print(len(contours))
for i in range (0, len(contours)):
fv = list() #1 feature vector
#HUMOMENTS
#print("humoments")
mom = cv2.moments(contours[i], 1)
Humoments = cv2.HuMoments(mom)
#print(Humoments)
fv.append(Humoments) #query data dimension must match BallTree data dimension
#SOLIDITY
area = cv2.contourArea(contours[i])
hull = cv2.convexHull(contours[i]) #ha tanti valori
hull_area = cv2.contourArea(hull)
solidity = float(area)/hull_area
fv.append(solidity)
#fv.append(elongation)
listafeaturevector.append(fv)
print("i have done")
print(len(listafeaturevector))
lenmatrice=len(listafeaturevector)
#KNN
X = listafeaturevector
y = [0,1,2,3]* (lenmatrice/4)
from sklearn.neighbors import KNeighborsClassifier
neigh = KNeighborsClassifier(n_neighbors=3)
neigh.fit(X, y) #ValueError: setting an array element with a sequence.
print(neigh.predict([[1.1]]))
print(neigh.predict_proba([[0.9]]))
If i try to covert it in a numpy array:
listafv = np.dstack(listafeaturevector)
listafv=np.rollaxis(listafv,-1)
print(listafv.shape)
data = listafv.reshape((lenmatrice, -1))
print(data.shape)
#KNN
X = data
i got: setting an array element with a sequence
A couple of suggestions/questions:
Humoments = cv2.HuMoments(mom)
What is the class of the return value Humoments? a float or a list? If float, that is fine.
for each image file
for i in range (0, len(contours)):
fv = list() #1 feature vector
...
fv.append(Humoments)
...
fv.append(solidity)
listafeaturevector.append(fv)
The above code does not seem correct. In your problem, I think you need to a construct a feature vector for each image. So anything that is related to image i should go to the same feature vector x_i. Then you combine all feature vectors to get a list of feature vectors X. However, your listafeaturevector (or X) presents in the inner-most loop, it's obviously not correct.
Second, you have a loop against the number of elements in the contours, are you sure the number of elements stays the same for each image? Otherwise, the number of features (|x_i|) is totally different across different images, that might cause the error of
setting an array element with a sequence.
Third, are you clear about how you want to classify the images? what are the target values/labels of different images? I see you just setting labels with [0,1,2,3]* (lenmatrice/4). Can you elaborate on what you are trying to do with those images? Are they containing different type of object? Are they showing different patterns? Are those images describe different topic/color? If yes, for each different type, you give a different label - either 0,1,2 or 'red','white','black' (assume you have only 3 types). The values of the label do not matter. What matters is how many values they have. I am trying to understand the difference of labels in your case.
On the other hand, if you only want to retrieve similar images, you don't need to use a classifier or specify a label for each image. Instead, try to use NearestNeighbors.
print(neigh.predict([[1.1]]))
print(neigh.predict_proba([[0.9]]))
Fourth, the above two lines of test are not correct. You need to set an X-like object in order to get a prediction from the classifier. That is to say, you need a feature vector x with the identical structure as you constructed in your training examples (with all h,e,s in the same order).
How can i represent a 3D coordinate as a singular value?
I need this since the libsvm requieres the input data to be 1-dimensional?
Thank you
You can simply use 3 features for each coordinate. (They even write so in their README)