script for scaling inria dataset for multiple-octaves training

diff --git a/apps/misk/scale_inria.py b/apps/misk/scale_inria.py
new file mode 100644 (file)
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+++ b/apps/misk/scale_inria.py
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+#!/usr/bin/env python
+
+import sys, os, os.path, glob, math, cv2
+from datetime import datetime
+from optparse import OptionParser
+
+def parse(ipath, f):
+    bbs = []
+    path = None
+    for l in f:
+        box = None
+        if l.startswith("Bounding box"):
+            b = [x.strip() for x in l.split(":")[1].split("-")]
+            c = [x[1:-1].split(",") for x in b]
+            d = [int(x) for x in sum(c, [])]
+            bbs.append(d)
+
+        if l.startswith("Image filename"):
+            path = os.path.join(os.path.join(ipath, ".."), l.split('"')[-2])
+
+    return (path, bbs)
+
+def adjust(box, tb, lr):
+
+    mix = int(round(box[0] - lr))
+    miy = int(round(box[1] - tb))
+
+    max = int(round(box[2] + lr))
+    may = int(round(box[3] + tb))
+
+    return [mix, miy, max, may]
+
+def resize(image, d_w, d_h):
+    if (d_h < image.shape[0]) or (d_w < image.shape[1]):
+        ratio = min(d_h / float(image.shape[0]), d_w / float(image.shape[1]))
+
+        kernel_size = int( 5 / (2 * ratio))
+        sigma = 0.5 / ratio
+        image_to_resize = cv2.filter2D(image, cv2.CV_8UC3, cv2.getGaussianKernel(kernel_size, sigma))
+        interpolation_type = cv2.INTER_AREA
+    else:
+        image_to_resize = image
+        interpolation_type = cv2.INTER_CUBIC
+
+    return cv2.resize(image_to_resize,(d_w, d_h), None, 0, 0, interpolation_type)
+
+if __name__ == "__main__":
+    parser = OptionParser()
+    parser.add_option("-i", "--input", dest="input", metavar="DIRECTORY", type="string",
+                       help="path to Inria train data folder")
+
+    parser.add_option("-o", "--output", dest="output", metavar="DIRECTORY", type="string",
+                       help="path to store data", default=".")
+
+    parser.add_option("-t", "--target", dest="target", type="string", help="should be train or test", default="train")
+
+    (options, args) = parser.parse_args()
+    if not options.input:
+        parser.error("Inria data folder required")
+
+    if options.target not in ["train", "test"]:
+        parser.error("dataset should contain train or test data")
+
+    octaves = [-1, 0, 1, 2]
+
+    path = os.path.join(options.output, datetime.now().strftime("rescaled-" + options.target + "-%Y-%m-%d-%H-%M-%S"))
+    os.mkdir(path)
+
+    neg_path = os.path.join(path, "neg")
+    os.mkdir(neg_path)
+
+    pos_path = os.path.join(path, "pos")
+    os.mkdir(pos_path)
+
+    print "rescaled Inria training data stored into", path, "\nprocessing",
+    for each in octaves:
+        octave = 2**each
+
+        whole_mod_w = int(64 * octave) + 2 * int(20 * octave)
+        whole_mod_h = int(128 * octave) + 2 * int(20 * octave)
+
+        cpos_path = os.path.join(pos_path, "octave_%d" % each)
+        os.mkdir(cpos_path)
+        idx = 0
+
+        gl = glob.iglob(os.path.join(options.input, "annotations/*.txt"))
+        for image, boxes in [parse(options.input, open(__p)) for __p in gl]:
+            for box in boxes:
+                height = box[3] - box[1]
+                scale = height / float(96)
+
+                mat = cv2.imread(image)
+                mat_h, mat_w, _ = mat.shape
+
+                rel_scale = scale / octave
+
+                d_w = whole_mod_w * rel_scale
+                d_h = whole_mod_h * rel_scale
+
+                top_bottom_border = (d_h - (box[3] - box[1])) / 2.0
+                left_right_border = (d_w - (box[2] - box[0])) / 2.0
+
+                box = adjust(box, top_bottom_border, left_right_border)
+                inner = [max(0, box[0]), max(0, box[1]), min(mat_w, box[2]), min(mat_h, box[3]) ]
+
+                cropped = mat[inner[1]:inner[3], inner[0]:inner[2], :]
+
+                top = int(max(0, 0 - box[1]))
+                bottom = int(max(0, box[3] - mat_h))
+                left = int(max(0, 0 - box[0]))
+                right = int(max(0, box[2] - mat_w))
+                cropped = cv2.copyMakeBorder(cropped, top, bottom, left, right, cv2.BORDER_REPLICATE)
+                resized = resize(cropped, whole_mod_w, whole_mod_h)
+
+                out_name = ".png"
+                if round(math.log(scale)/math.log(2)) < each:
+                    out_name = "_upscaled" + out_name
+
+                cv2.imwrite(os.path.join(cpos_path, "sample_%d" % idx + out_name), resized)
+
+                flipped = cv2.flip(resized, 1)
+                cv2.imwrite(os.path.join(cpos_path, "sample_%d" % idx + "_mirror" + out_name), flipped)
+                idx = idx + 1
+                print "." ,
+                sys.stdout.flush()
+
+        idx = 0
+        cneg_path = os.path.join(neg_path, "octave_%d" % each)
+        os.mkdir(cneg_path)
+
+        for each in [__n for __n in glob.iglob(os.path.join(options.input, "neg/*.*"))]:
+            img = cv2.imread(each)
+            min_shape = (1.5 * whole_mod_h, 1.5 * whole_mod_w)
+
+            if (img.shape[1] <= min_shape[1]) or (img.shape[0] <= min_shape[0]):
+                out_name = "negative_sample_%i_resized.png" % idx
+
+                ratio = float(img.shape[1]) / img.shape[0]
+
+                if (img.shape[1] <= min_shape[1]):
+                    resized_size = (int(min_shape[1]), int(min_shape[1] / ratio))
+
+                if (img.shape[0] <= min_shape[0]):
+                    resized_size = (int(min_shape[0] * ratio), int(min_shape[0]))
+
+                img = resize(img, resized_size[0], resized_size[1])
+            else:
+                out_name = "negative_sample_%i.png" % idx
+
+            cv2.imwrite(os.path.join(cneg_path, out_name), img)
+            idx = idx + 1
+            print "." ,
+            sys.stdout.flush()
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