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ROC script refactoring

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marina.kolpakova 2013-01-24 16:22:08 +04:00
parent e9232a4b67
commit faecb4f01d
4 changed files with 45 additions and 53 deletions
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5
apps/sft/misc/roc_test.py
View File

@ -74,10 +74,7 @@ if __name__ == "__main__":
name = pattern % (nframes,)
_, tail = os.path.split(name)
boxes = samples[tail]
boxes = sft.norm_acpect_ratio(boxes, 0.5)
boxes = [b for b in boxes if (b[3] - b[1]) > args.scale_range[0] / args.ext_ratio]
boxes = [b for b in boxes if (b[3] - b[1]) < args.scale_range[1] * args.ext_ratio]
boxes = sft.filter_for_range(samples[tail], args.scale_range, args.ext_ratio)
nannotated = nannotated + len(boxes)
nframes = nframes + 1

16
apps/sft/misc/scale_caltech.py
View File

@ -6,20 +6,6 @@ from optparse import OptionParser
import re
import numpy as np
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)
def extractPositive(f, path, opath, octave, min_possible):
newobj = re.compile("^lbl=\'(\w+)\'\s+str=(\d+)\s+end=(\d+)\s+hide=0$")
pos = re.compile("^pos\s=(\[[((\d+\.+\d*)|\s+|\;)]*\])$")
@ -107,7 +93,7 @@ def extractPositive(f, path, opath, octave, min_possible):
continue
cropped = cv2.copyMakeBorder(cropped, top, bottom, left, right, cv2.BORDER_REPLICATE)
resized = resize(cropped, whole_mod_w, whole_mod_h)
resized = sft.resize_sample(cropped, whole_mod_w, whole_mod_h)
flipped = cv2.flip(resized, 1)
cv2.imshow("resized", resized)

18
apps/sft/misc/scale_inria.py
View File

@ -30,20 +30,6 @@ def adjust(box, tb, lr):
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",
@ -110,7 +96,7 @@ if __name__ == "__main__":
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)
resized = sft.resize_sample(cropped, whole_mod_w, whole_mod_h)
out_name = ".png"
if round(math.log(scale)/math.log(2)) < each:
@ -143,7 +129,7 @@ if __name__ == "__main__":
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])
img = sft.resize_sample(img, resized_size[0], resized_size[1])
else:
out_name = "negative_sample_%i.png" % idx

59
apps/sft/misc/sft.py
View File

@ -30,7 +30,7 @@ def cascade(min_scale, max_scale, nscales, f):
assert c.load(dom)
return c
""" Compute prefix sum for en array"""
""" Compute prefix sum for en array."""
def cumsum(n):
cum = []
y = 0
@ -39,7 +39,7 @@ def cumsum(n):
cum.append(y)
return cum
""" Compute x and y arrays for ROC plot"""
""" Compute x and y arrays for ROC plot."""
def computeROC(confidenses, tp, nannotated, nframes, ignored):
confidenses, tp, ignored = zip(*sorted(zip(confidenses, tp, ignored), reverse = True))
@ -53,14 +53,14 @@ def computeROC(confidenses, tp, nannotated, nframes, ignored):
return fppi, miss_rate
""" Crop rectangle by factor"""
""" Crop rectangle by factor."""
def crop_rect(rect, factor):
val_x = factor * float(rect[2])
val_y = factor * float(rect[3])
x = [int(rect[0] + val_x), int(rect[1] + val_y), int(rect[2] - 2.0 * val_x), int(rect[3] - 2.0 * val_y)]
return x
"""Initialize plot axises"""
""" Initialize plot axises."""
def initPlot(name = "ROC curve Bahnhof"):
fig, ax = plt.subplots()
@ -73,13 +73,14 @@ def initPlot(name = "ROC curve Bahnhof"):
plt.xscale('log')
plt.yscale('log')
"""Show resulted plot"""
""" Show resulted plot."""
def showPlot(file_name):
plt.savefig(file_name)
plt.axis((pow(10, -3), pow(10, 1), 0.0, 1))
plt.yticks( [0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.64, 0.8, 1], ['.05', '.10', '.20', '.30', '.40', '.50', '.64', '.80', '1'] )
plt.show()
""" Filter true positives and ignored detections for cascade detector output."""
def match(gts, dts):
matches_gt = [0]*len(gts)
matches_dt = [0]*len(dts)
@ -94,7 +95,7 @@ def match(gts, dts):
for idx, row in enumerate(overlaps):
imax = row.index(max(row))
# try to match ground thrush
# try to match ground truth
if (matches_gt[imax] == 0 and row[imax] > 0.5):
matches_gt[imax] = 1
matches_dt[idx] = 1
@ -109,17 +110,18 @@ def match(gts, dts):
matches_ignore[idx] = 1
return matches_dt, matches_ignore
""" Draw plot."""
def plotLogLog(fppi, miss_rate, c):
print
plt.loglog(fppi, miss_rate, color = c, linewidth = 2)
""" Draw detections or ground truth on image."""
def draw_rects(img, rects, color, l = lambda x, y : x + y):
if rects is not None:
for x1, y1, x2, y2 in rects:
cv2.rectangle(img, (x1, y1), (l(x1, x2), l(y1, y2)), color, 2)
def draw_dt(img, dts, color, l = lambda x, y : x + y):
if dts is not None:
for dt in dts:
@ -128,10 +130,6 @@ def draw_dt(img, dts, color, l = lambda x, y : x + y):
cv2.rectangle(img, (x1, y1), (l(x1, x2), l(y1, y2)), color, 2)
class Annotation:
def __init__(self, bb):
self.bb = bb
class Detection:
def __init__(self, bb, conf):
self.bb = bb
@ -168,7 +166,7 @@ class Detection:
def mark_matched(self):
self.matched = True
"""Parse INPIA annotation format"""
def parse_inria(ipath, f):
bbs = []
path = None
@ -185,10 +183,11 @@ def parse_inria(ipath, f):
return Sample(path, bbs)
def glob_set(pattern):
return [__n for __n in glob.iglob(pattern)] #glob.iglob(pattern)
# parse ETH idl file
def glob_set(pattern):
return [__n for __n in glob.iglob(pattern)]
""" Parse ETH idl file. """
def parse_idl(f):
map = {}
for l in open(f):
@ -198,11 +197,35 @@ def parse_idl(f):
map.update(eval(l))
return map
""" Normalize detection box to unified aspect ration."""
def norm_box(box, ratio):
middle = float(box[0] + box[2]) / 2.0
new_half_width = float(box[3] - box[1]) * ratio / 2.0
return (int(round(middle - new_half_width)), box[1], int(round(middle + new_half_width)), box[3])
""" Process array of boxes."""
def norm_acpect_ratio(boxes, ratio):
return [ norm_box(box, ratio) for box in boxes]
return [ norm_box(box, ratio) for box in boxes]
""" Filter detections out of extended range. """
def filter_for_range(boxes, scale_range, ext_ratio):
boxes = sft.norm_acpect_ratio(boxes, 0.5)
boxes = [b for b in boxes if (b[3] - b[1]) > scale_range[0] / ext_ratio]
boxes = [b for b in boxes if (b[3] - b[1]) < scale_range[1] * ext_ratio]
return boxes
""" Resize sample for training."""
def resize_sample(image, d_w, d_h):
h, w, _ = image.shape
if (d_h < h) or (d_w < w):
ratio = min(d_h / float(h), d_w / float(w))
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)