def processLine(i, w, index, inputImage, tileSize, channels):
for j in range(0, w / tileSize):
roi = inputImage[i * tileSize:(i + 1) * tileSize, j * tileSize:(j + 1) * tileSize]
fts = features.extractFeature(roi)
patch = preparePatch(getIndexImage(fts, index, channels), tileSize)
inputImage[i * tileSize:(i + 1) * tileSize, j * tileSize:(j + 1) * tileSize] = patch
cv2.imshow("Progress", inputImage)
cv2.waitKey(1)
def CalcLine(i, w, index, inputImage, number_of_tiles, channels):
for j in range(0, w // number_of_tiles):
roi = inputImage[i * number_of_tiles:(i + 1) * number_of_tiles, j * number_of_tiles:(j + 1) * number_of_tiles]
fts = features.extractFeature(roi)
patcher = startpatching(importImage_index(fts, index, channels), number_of_tiles)
inputImage[i * number_of_tiles:(i + 1) * number_of_tiles, j * number_of_tiles:(j + 1) * number_of_tiles] = patcher
cv2.imshow("Progress", inputImage)
cv2.waitKey(1)
def processLine(i, w, index, inputImage, tileSize, channels):
for j in range(0, w / tileSize):
roi = inputImage[i * tileSize:(i + 1) * tileSize, j * tileSize:(j + 1) * tileSize]
fts = features.extractFeature(roi)
patch = preparePatch(getIndexImage(fts, index, channels), tileSize)
inputImage[i * tileSize:(i + 1) * tileSize, j * tileSize:(j + 1) * tileSize] = patch
cv2.imshow("Progress", inputImage)
cv2.waitKey(1)
def return_avgs(imgfile_imgbytes, bucketBroadcast):
file_bytes = np.asarray(bytearray(imgfile_imgbytes[1]), dtype=np.uint8)
img = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
min_distance = 277
small_img_avg = features.extractFeature(img)
buckets = bucketBroadcast.value
dominant_colour_bucket = buckets[0]
for bucket in buckets:
current_distance = calcEuclideanColourDistance(bucket[1],
small_img_avg)
if (current_distance < min_distance):
min_distance = current_distance
dominant_colour_bucket = bucket
# return tile/bucket's average color, (name of current small img, distance to bucket,
# .. average of current small image, coordinates of tile (for the final stitch step))
return (dominant_colour_bucket[1], (imgfile_imgbytes[0], min_distance,
features.extractFeature(img),
dominant_colour_bucket[0]))
def processLine(i, w, index, inputImage, tileSize, channels, mosaic_tiles):
mosaic_tiles.append([])
for j in range(0, w / tileSize):
roi = inputImage[i * tileSize:(i + 1) * tileSize,
j * tileSize:(j + 1) * tileSize]
fts = features.extractFeature(roi)
index_image = getIndexImage(fts, index, channels)
mosaic_tiles[-1].append(index_image)
patch = preparePatch(index_image, tileSize)
inputImage[i * tileSize:(i + 1) * tileSize,
j * tileSize:(j + 1) * tileSize] = patch
cv2.imshow("Progress", inputImage)
def main():
index = []
if not os.path.exists(INDEX_PATH):
os.makedirs(INDEX_PATH)
files = glob.glob(DB_PATH + "/" + "*.jpg")
entry = []
fileName = []
for file in files:
print "Processing file: " + file
image = convertImage(file)
entry = features.extractFeature(image)
print(entry)
#entry["file"] = ntpath.basename(file)
index.append(entry)
fileName.append(ntpath.basename(file))
trained_model = cluster_img(index)
print("Train Model COMPLETED! Showing the result..")
for mCenter in trained_model.centers:
print(mCenter)
print("Now Saving the K Measns Model")
trained_model.save(sc, "myModelPath")
#with open(INDEX_PATH + "histogram.index", 'w') as outfile:
#json.dump(index, outfile, indent=4)
#rdd = sc.parallelize(index)
#print rdd.collect()
#rdd.saveAsTextFile("historgam")
i = 0
text_file = open("images_cluster.txt", "w")
for image in index:
result = trained_model.predict(image)
text_file = open("images_cluster.txt", "a")
text_file.write(fileName[i] + ":" + str(result) + "\n")
text_file.close()
print(fileName[i] + ":" + str(result))
i += 1
print("Index written to: " + INDEX_PATH + "histogram.index")
def main():
index = []
if not os.path.exists(INDEX_PATH):
os.makedirs(INDEX_PATH)
files = glob.glob(DB_PATH + "/" + "*.jpg")
entry = {}
for file in files:
print "Processing file: " + file
image = convertImage(file)
entry = features.extractFeature(image)
entry["file"] = ntpath.basename(file)
index.append(entry)
with open(INDEX_PATH + "histogram.index", 'w') as outfile:
json.dump(index, outfile, indent=4)
print("Index written to: " + INDEX_PATH + "histogram.index")
def main():
index = []
if not os.path.exists(path_index):
os.makedirs(path_index)
files = glob.glob(IMAGE_PATH + "/" + "*.jpg")
access = {}
for file in files:
print("File Processing: " + file)
image = Convert(file)
access = features.extractFeature(image)
access["file"] = ntpath.basename(file)
index.append(access)
with open(path_index + "histogram.index", 'w') as outfile:
json.dump(index, outfile, indent=4)
print("Written to: " + path_index + "histogram.index")
def main():
index = []
if not os.path.exists(INDEX_PATH):
os.makedirs(INDEX_PATH)
files = glob.glob(DB_PATH + "/" + "*.jpg")
entry = {}
for file in files:
print "Processing file: " + file
image = convertImage(file)
entry = features.extractFeature(image)
entry["file"] = ntpath.basename(file)
index.append(entry)
with open(INDEX_PATH + "histogram.index", 'w') as outfile:
json.dump(index, outfile, indent=4)
print ("Index written to: " + INDEX_PATH + "histogram.index")
def compute_tile_avgs(cvimg):
block_width = gcd(cvimg.shape[0], cvimg.shape[1])
block_height = block_width
numrows = int(cvimg.shape[0] / block_height)
numcols = int(cvimg.shape[1] / block_width)
tile_avgs = []
for row in range(0, numrows):
for col in range(0, numcols):
y0 = row * block_height
y1 = y0 + block_height
x0 = col * block_width
x1 = x0 + block_width
tile_avg = features.extractFeature(cvimg[y0:y1, x0:x1])
tile_coords = [y0, y1, x0, x1]
tile_stats = [tile_coords, tile_avg]
tile_avgs.append(tile_stats)
return tile_avgs
