def compute_match_save(SLIDES_PATH, SLIDE_START, SLIDE_END,
FRAME_SAMPLE_SAVE_FOLDER,
SLIDE_KEYPTS_DESCS_PKL_SAVE_PATH, VID_START, VID_END,
VID_KEYPTS_DESCS_PKL_SAVE_PATH,
FINAL_FIGURES_RESULTS_SAVE_PATH):
NUM_SLIDES = SLIDE_END - SLIDE_START
NUM_FRAMES = VID_END - VID_START
## getting filenames
videoFrameNames = [
filename for filename in os.listdir(FRAME_SAMPLE_SAVE_FOLDER)
if filename.endswith(".png")
]
videoFrameNames = slidesInRange(videoFrameNames, VID_START, VID_END)
slideNames = [
filename for filename in os.listdir(SLIDES_PATH)
if filename.endswith(".png")
]
slideNames = slidesInRange(slideNames, SLIDE_START, SLIDE_END)
sampleImgSlides = cv2.imread(slideImgPath + slideNames[0], 0)
sampleVidSlides = cv2.imread(vidFramePath + videoFrameNames[0], 0)
## slide and video dimensions
slideDimensions = (sampleImgSlides.shape[0], sampleImgSlides.shape[1])
vidDimensions = (sampleVidSlides.shape[0], sampleVidSlides.shape[1])
## load dictionary of things
slideImgKeypts = pickle.load(open(SLIDE_KEYPTS_DESCS_PKL_SAVE_PATH, "rb"))
vidImgKeypts = pickle.load(open(VID_KEYPTS_DESCS_PKL_SAVE_PATH, "rb"))
slideImgKeyptsDesc = dict()
vidImgKeyptsDesc = dict()
## load slide img, keypoints
for sn in slideNames:
path = slideImgPath + sn
slideImgKeyptsDesc[sn] = convertPickledToKPDesc(slideImgKeypts[path])
for vidName in videoFrameNames:
path = vidFramePath + vidName
vidImgKeyptsDesc[vidName] = convertPickledToKPDesc(vidImgKeypts[path])
FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)
## matches is a list of DMatch objects
flann = cv2.FlannBasedMatcher(index_params, search_params)
matchSlideFrameDict = dict()
matchMetric = dict()
matchMetricNotNormalized = dict()
## cross matching
matchID = 0
for slide in range(0, NUM_SLIDES):
for frame in range(0, NUM_FRAMES):
print("Slide, Frame", (slide, frame), slideNames[slide],
videoFrameNames[frame])
slidekpts, slideDescs = slideImgKeyptsDesc[slideNames[slide]]
framekpts, frameDescs = vidImgKeyptsDesc[videoFrameNames[frame]]
matches = flann.knnMatch(np.asarray(slideDescs, np.float32),
np.asarray(frameDescs, np.float32),
k=2)
matchPairs = []
distances = []
good = []
slideKeypoints = []
frameKeypoints = []
good = []
for m, n in matches:
if m.distance < 0.75 * n.distance:
slideKeypoints.append(slidekpts[m.queryIdx].pt)
frameKeypoints.append(framekpts[m.trainIdx].pt)
good.append(m)
'''
slideImg = cv2.imread(slideImgPath + slideNames[slide], 0)
vidImg = cv2.imread(vidFramePath + videoFrameNames[frame], 0)
saveMatches(slidekpts, framekpts, slideImg, vidImg, good, savePath + str(slide) + '-' + str(frame) + '-match.jpg')
'''
matchMetric[(slide, frame)] = computeSumDiffKeypoints(
slideKeypoints, frameKeypoints, slideDimensions, vidDimensions)
matchMetricNotNormalized[(slide, frame)] = computeSumDiffKeypoints(
slideKeypoints, frameKeypoints, slideDimensions, vidDimensions,
False)
## save visualizations
mat = visualize(
matchMetric, NUM_SLIDES, NUM_FRAMES, FINAL_FIGURES_RESULTS_SAVE_PATH +
"normalized_sumDistanceKeypoints.jpg")
visualize(matchMetricNotNormalized, NUM_SLIDES, NUM_FRAMES,
FINAL_FIGURES_RESULTS_SAVE_PATH + "sumDistanceKeypoints.jpg")
## save matrix as txt
np.savetxt(FINAL_FIGURES_RESULTS_SAVE_PATH +
"slides_by_vidframes_metric.txt",
mat,
fmt='%d')
col_best_prediction = {}
for col in range(NUM_FRAMES):
slides = list(mat[:, col])
best = slides.index(min(slides))
col_best_prediction[col] = best
print(json.dumps(col_best_prediction, sort_keys=True))
filename for filename in os.listdir(slideImgPath)
if filename.endswith(".png")
]
slideNames = slidesInRange(slideNames, 2, 30)
## load dictionary of things
slideImgKeypts = pickle.load(open(slideImgKeyptsFile, "rb"))
vidImgKeypts = pickle.load(open(vidImgKeyptsFle, "rb"))
slideImgKeyptsDesc = dict()
vidImgKeyptsDesc = dict()
## load slide img, keypoints
for sn in slideNames:
path = slideImgPath + sn
slideImgKeyptsDesc[sn] = convertPickledToKPDesc(slideImgKeypts[path])
for vidName in videoFrameNames:
path = vidFramePath + vidName
vidImgKeyptsDesc[vidName] = convertPickledToKPDesc(vidImgKeypts[path])
FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)
## matches is a list of DMatch objects
flann = cv2.FlannBasedMatcher(index_params, search_params)
matchSlideFrameDict = dict()
## cross matching
## Load slides, compute keypoint matching to following slide
slideNames = [ filename for filename in os.listdir( slideImgPath) if filename.endswith(".jpg")]
slideNames = slidesInRange(slideNames, 0, 74)
## load dictionary of things
slideImgKeyptsFile = "./slide_keypts/slideKeyptsDesc.pkl"
slideImgKeypts = pickle.load( open( slideImgKeyptsFile, "rb" ) )
print(slideImgKeypts.keys())
slideImgKeyptsDesc = dict()
## load slide img, keypoints
for sn in slideNames:
path = slideImgPath + sn
slideImgKeyptsDesc[sn] = convertPickledToKPDesc(slideImgKeypts[path])
FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
search_params = dict(checks = 50)
## matches is a list of DMatch objects
flann = cv2.FlannBasedMatcher(index_params, search_params)
matchSlideFrameDict = dict()
matchMetric = dict()
matchMetricNotNormalized = dict()