140 lines
4.9 KiB
Python
Executable File
140 lines
4.9 KiB
Python
Executable File
#!/usr/bin/env python
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import sys, os, os.path, glob, math, cv2
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from datetime import datetime
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from optparse import OptionParser
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def parse(ipath, f):
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bbs = []
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path = None
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for l in f:
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box = None
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if l.startswith("Bounding box"):
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b = [x.strip() for x in l.split(":")[1].split("-")]
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c = [x[1:-1].split(",") for x in b]
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d = [int(x) for x in sum(c, [])]
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bbs.append(d)
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if l.startswith("Image filename"):
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path = os.path.join(os.path.join(ipath, ".."), l.split('"')[-2])
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return (path, bbs)
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def adjust(box, tb, lr):
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mix = int(round(box[0] - lr))
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miy = int(round(box[1] - tb))
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max = int(round(box[2] + lr))
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may = int(round(box[3] + tb))
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return [mix, miy, max, may]
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if __name__ == "__main__":
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parser = OptionParser()
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parser.add_option("-i", "--input", dest="input", metavar="DIRECTORY", type="string",
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help="path to Inria train data folder")
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parser.add_option("-o", "--output", dest="output", metavar="DIRECTORY", type="string",
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help="path to store data", default=".")
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parser.add_option("-t", "--target", dest="target", type="string", help="should be train or test", default="train")
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(options, args) = parser.parse_args()
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if not options.input:
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parser.error("Inria data folder required")
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if options.target not in ["train", "test"]:
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parser.error("dataset should contain train or test data")
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octaves = [-1, 0, 1, 2]
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path = os.path.join(options.output, datetime.now().strftime("rescaled-" + options.target + "-%Y-%m-%d-%H-%M-%S"))
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os.mkdir(path)
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neg_path = os.path.join(path, "neg")
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os.mkdir(neg_path)
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pos_path = os.path.join(path, "pos")
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os.mkdir(pos_path)
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print "rescaled Inria training data stored into", path, "\nprocessing",
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for each in octaves:
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octave = 2**each
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whole_mod_w = int(64 * octave) + 2 * int(20 * octave)
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whole_mod_h = int(128 * octave) + 2 * int(20 * octave)
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cpos_path = os.path.join(pos_path, "octave_%d" % each)
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os.mkdir(cpos_path)
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idx = 0
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gl = glob.iglob(os.path.join(options.input, "annotations/*.txt"))
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for image, boxes in [parse(options.input, open(__p)) for __p in gl]:
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for box in boxes:
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height = box[3] - box[1]
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scale = height / float(96)
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mat = cv2.imread(image)
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mat_h, mat_w, _ = mat.shape
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rel_scale = scale / octave
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d_w = whole_mod_w * rel_scale
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d_h = whole_mod_h * rel_scale
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top_bottom_border = (d_h - (box[3] - box[1])) / 2.0
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left_right_border = (d_w - (box[2] - box[0])) / 2.0
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box = adjust(box, top_bottom_border, left_right_border)
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inner = [max(0, box[0]), max(0, box[1]), min(mat_w, box[2]), min(mat_h, box[3]) ]
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cropped = mat[inner[1]:inner[3], inner[0]:inner[2], :]
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top = int(max(0, 0 - box[1]))
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bottom = int(max(0, box[3] - mat_h))
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left = int(max(0, 0 - box[0]))
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right = int(max(0, box[2] - mat_w))
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cropped = cv2.copyMakeBorder(cropped, top, bottom, left, right, cv2.BORDER_REPLICATE)
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resized = sft.resize_sample(cropped, whole_mod_w, whole_mod_h)
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out_name = ".png"
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if round(math.log(scale)/math.log(2)) < each:
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out_name = "_upscaled" + out_name
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cv2.imwrite(os.path.join(cpos_path, "sample_%d" % idx + out_name), resized)
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flipped = cv2.flip(resized, 1)
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cv2.imwrite(os.path.join(cpos_path, "sample_%d" % idx + "_mirror" + out_name), flipped)
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idx = idx + 1
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print "." ,
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sys.stdout.flush()
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idx = 0
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cneg_path = os.path.join(neg_path, "octave_%d" % each)
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os.mkdir(cneg_path)
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for each in [__n for __n in glob.iglob(os.path.join(options.input, "neg/*.*"))]:
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img = cv2.imread(each)
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min_shape = (1.5 * whole_mod_h, 1.5 * whole_mod_w)
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if (img.shape[1] <= min_shape[1]) or (img.shape[0] <= min_shape[0]):
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out_name = "negative_sample_%i_resized.png" % idx
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ratio = float(img.shape[1]) / img.shape[0]
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if (img.shape[1] <= min_shape[1]):
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resized_size = (int(min_shape[1]), int(min_shape[1] / ratio))
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if (img.shape[0] <= min_shape[0]):
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resized_size = (int(min_shape[0] * ratio), int(min_shape[0]))
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img = sft.resize_sample(img, resized_size[0], resized_size[1])
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else:
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out_name = "negative_sample_%i.png" % idx
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cv2.imwrite(os.path.join(cneg_path, out_name), img)
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idx = idx + 1
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print "." ,
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sys.stdout.flush()
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