def compositeNImages(listOfImages, listOfH, weights, abrupt=False):
# Computes the composite image. listOfH is of the form returned by computeNHomographies.
# Hint: You will need to deal with bounding boxes and translations again in this function.
bbox = a6.computeTransformedBBox(listOfImages[0].shape, listOfH[0])
for img, H in zip(listOfImages, listOfH):
currentbbox = a6.computeTransformedBBox(img.shape, H)
bbox = a6.bboxUnion(currentbbox, bbox)
trans = a6.translate(bbox)
out = np.zeros((bbox[1][0] - bbox[0][0], bbox[1][1] - bbox[0][1], 3))
weightIm = np.zeros((out.shape[0], out.shape[1], 3))
for img, H in zip(listOfImages, listOfH):
applyHomographyFast(img, out, trans.dot(H), weights, weightIm, abrupt)
weightIm[weightIm == 0] = np.min(weightIm[weightIm.nonzero()]) # no zero in denominators
return out / weightIm
def compositeNImages(listOfImages, listOfH, weights, abrupt=False):
# Computes the composite image. listOfH is of the form returned by computeNHomographies.
# Hint: You will need to deal with bounding boxes and translations again in this function.
bbox = a6.computeTransformedBBox(listOfImages[0].shape, listOfH[0])
for img, H in zip(listOfImages, listOfH):
currentbbox = a6.computeTransformedBBox(img.shape, H)
bbox = a6.bboxUnion(currentbbox, bbox)
trans = a6.translate(bbox)
out = np.zeros((bbox[1][0] - bbox[0][0], bbox[1][1] - bbox[0][1], 3))
weightIm = np.zeros((out.shape[0], out.shape[1], 3))
for img, H in zip(listOfImages, listOfH):
applyHomographyFast(img, out, trans.dot(H), weights, weightIm, abrupt)
weightIm[weightIm == 0] = np.min(
weightIm[weightIm.nonzero()]) # no zero in denominators
return out / weightIm
def compositeNImages_blended(listOfImages, listOfHomographies, weights, onlyHighestWeight=False):
bbox = a6.computeTransformedBBox(np.shape(listOfImages[0]), listOfHomographies[0])
for (i, H) in enumerate(listOfHomographies):
bboxI = a6.computeTransformedBBox(np.shape(listOfImages[i]), H)
bbox = a6.bboxUnion(bboxI, bbox)
height = bbox[1][0] - bbox[0][0]
width = bbox[1][1] - bbox[0][1]
out = np.zeros([height, width, 3])
out_weight = np.zeros([height, width, 3])
translation = a6.translate(bbox)
for i in xrange(len(listOfImages)):
applyHomographyFast_blended(
listOfImages[i], out, np.dot(translation, listOfHomographies[i]), out_weight, weights, onlyHighestWeight
)
out_weight[out_weight == 0] = 1e-12
return out / out_weight
