def _segmentImage(self, imgPath):
"""Segment the given image into sections to for smoke classificaiton
Args:
imgPath (str): filepath of the image
Returns:
List of dictionary containing information on each segment
"""
img = Image.open(imgPath)
ppath = pathlib.PurePath(imgPath)
segments = rect_to_squares.cutBoxes(img, str(ppath.parent), imgPath)
img.close()
return segments
def main():
global fen
reqArgs = [
["i", "image", "filename of the image"],
["o", "output", "output directory name"],
]
optArgs = [[
"d", "display",
"(optional) specify any value to display image and boxes"
], ["f", "fixedSize", "(optional) use 299x299 segments"]]
args = collect_args.collectArgs(reqArgs, optionalArgs=optArgs)
# print(args)
imgOrig = Image.open(args.image)
callBackFn = None
if args.display:
imageDisplay(imgOrig)
callBackFn = showSquares
if args.fixedSize:
rect_to_squares.cutBoxesFixed(imgOrig, args.output, args.image,
callBackFn)
else:
rect_to_squares.cutBoxes(imgOrig, args.output, args.image, callBackFn)
if args.display:
fen.mainloop()
def main():
reqArgs = [
["i", "image", "filename of the image"],
["o", "output", "output directory name"],
]
optArgs = [["l", "labels", "labels file generated during retraining"],
["m", "model", "model file generated during retraining"],
[
"d", "display",
"(optional) specify any value to display image and boxes"
]]
args = collect_args.collectArgs(reqArgs, optionalArgs=optArgs)
model_file = args.model if args.model else settings.model_file
labels_file = args.labels if args.labels else settings.labels_file
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
graph = tf_helper.load_graph(model_file)
labels = tf_helper.load_labels(labels_file)
segments = []
with tf.Session(graph=graph) as tfSession:
if True: # chops image in segment files and classifies each segment
imgOrig = Image.open(args.image)
segments = rect_to_squares.cutBoxes(imgOrig, args.output,
args.image)
tf_helper.classifySegments(tfSession, graph, labels, segments)
if False: # version that classifies entire image without cropping
imgOrig = Image.open(args.image)
segments = [{'imgPath': args.image}]
tf_helper.classifySegments(tfSession, graph, labels, segments)
if False: # chops image into in-memory segments and classifies each segment
calcScoresInMemory(args.model, args.labels, args.image)
for segmentInfo in segments:
print(segmentInfo['imgPath'], segmentInfo['score'])
if args.display:
drawBoxesAndScores(imgOrig, segments)
displayImageWithScores(imgOrig, [])
def segmentImage(imgPath):
img = Image.open(imgPath)
ppath = pathlib.PurePath(imgPath)
return rect_to_squares.cutBoxes(img, str(ppath.parent), imgPath)
