def ReadImages(ImageSize, DataPath):
"""
Inputs:
ImageSize - Size of the Image
DataPath - Paths of all images where testing will be run on
Outputs:
I1Combined - I1 image after any standardization and/or cropping/resizing to ImageSize
I1 - Original I1 image for visualization purposes only
"""
ImageName = DataPath
Image = cv2.imread(ImageName)
Gray = cv2.cvtColor(Image, cv2.COLOR_BGR2GRAY)
if (Image is None):
# OpenCV returns empty list if image is not read!
print('ERROR: Image cannot be read')
sys.exit()
Tau = min(ImageSize[0], ImageSize[1]) / 3
# generate label
Label = np.random.randint(2 * Tau, size=8) - Tau
# get corner correspondences
Corners, Warped = iu.getCorrespondence(Image.shape, Label, ImageSize)
# get forward and backward homographies
CroppedPatch, WarpedPatch = iu.getWarpingPair(Gray, ImageSize, Corners,
Warped)
I1 = np.float32(np.stack([CroppedPatch, WarpedPatch], -1))
#cv2.imshow("Image", Image)
#cv2.imshow("Warped Patch", WarpedPatch)
#cv2.imshow("Cropped Patch", CroppedPatch)
I1S = iu.StandardizeInputs(np.float32(I1))
I1Combined = np.expand_dims(I1S, axis=0)
return I1Combined, Image, Label, Corners
def GenerateBatch(BasePath, DirNamesTrain, ImageSize, LargeImgSize,
MiniBatchSize):
"""
Inputs:
BasePath - Path to COCO folder without "/" at the end
DirNamesTrain - Variable with Subfolder paths to train files
NOTE that Train can be replaced by Val/Test for generating batch corresponding to validation (held-out testing in this case)/testing
NOTE that TrainLabels can be replaced by Val/TestLabels for generating batch corresponding to validation (held-out testing in this case)/testing
ImageSize - Size of the Image
MiniBatchSize is the size of the MiniBatch
Outputs:
I1Batch - Batch of images
LabelBatch - Batch of one-hot encoded labels
"""
Tau = min(ImageSize[0], ImageSize[1]) / 3
# Share the random crop across the batch
x, y = iu.randomCrop((LargeImgSize[0], LargeImgSize[1]), ImageSize)
Corners = np.array([(x, y), (x + ImageSize[1], y),
(x + ImageSize[1], y + ImageSize[0]),
(x, y + ImageSize[0])],
dtype=np.float32)
# Get indices for the patch
Indices = np.arange(0, LargeImgSize[0] * LargeImgSize[1])
Indices = Indices.reshape(LargeImgSize[0], LargeImgSize[1])
Indices = Indices[y:y + ImageSize[0], x:x + ImageSize[1]]
I1Batch = []
ImgOrgBatch = []
LabelBatch = []
CornerBatch = []
ImageNum = 0
while ImageNum < MiniBatchSize:
# Generate random image
RandIdx = random.randint(0, len(DirNamesTrain) - 1)
RandImageName = BasePath + "/Data" + os.sep + DirNamesTrain[
RandIdx] + '.jpg'
ImageNum += 1
Image = cv2.imread(RandImageName, 0)
Image = cv2.resize(Image, (LargeImgSize[1], LargeImgSize[0]))
Label = np.random.randint(2 * Tau, size=8) - Tau
# distination (mapped) corners with (x, y) format
Warped = np.array(
[(Corners[0, 0] + Label[0], Corners[0, 1] + Label[1]),
(Corners[1, 0] + Label[2], Corners[1, 1] + Label[3]),
(Corners[2, 0] + Label[4], Corners[2, 1] + Label[5]),
(Corners[3, 0] + Label[6], Corners[3, 1] + Label[7])],
dtype=np.float32)
CroppedPatch, WarpedPatch = iu.getWarpingPair(Image, ImageSize,
Corners, Warped)
#print('c', CroppedPatch.shape)
#print('w', WarpedPatch.shape)
#cv2.imshow("Image", Image)
#cv2.imshow("Warped Patch", WarpedPatch)
#cv2.imshow("Cropped Patch", CroppedPatch)
I1 = np.float32(np.stack([CroppedPatch, WarpedPatch], -1))
# Append All Images and Mask
I1Batch.append(I1)
ImgOrgBatch.append(Image[..., None])
LabelBatch.append(Label)
CornerBatch.append(Corners.reshape(8))
#cv2.waitKey()
return I1Batch, ImgOrgBatch, LabelBatch, CornerBatch, Indices