opencv/apps/sft/misc/sft.py

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#!/usr/bin/env python
import cv2, re, glob
import numpy as np
import matplotlib.pyplot as plt
""" Convert numpy matrices with rectangles and confidences to sorted list of detections."""
def convert2detections(rects, confs, crop_factor = 0.125):
if rects is None:
return []
dts = zip(*[rects.tolist(), confs.tolist()])
dts = zip(dts[0][0], dts[0][1])
dts = [Detection(r,c) for r, c in dts]
dts.sort(lambda x, y : -1 if (x.conf - y.conf) > 0 else 1)
for dt in dts:
dt.crop(crop_factor)
return dts
def cascade(min_scale, max_scale, nscales, f):
# where we use nms cv::SCascade::DOLLAR == 2
c = cv2.SCascade(min_scale, max_scale, nscales, 2)
xml = cv2.FileStorage(f, 0)
dom = xml.getFirstTopLevelNode()
assert c.load(dom)
return c
def cumsum(n):
cum = []
y = 0
for i in n:
y += i
cum.append(y)
return cum
def computeROC(confidenses, tp, nannotated, nframes):
confidenses, tp = zip(*sorted(zip(confidenses, tp), reverse = True))
fp = [(1 - x) for x in tp]
fp = cumsum(fp)
tp = cumsum(tp)
miss_rate = [(1 - x / (nannotated + 0.000001)) for x in tp]
fppi = [x / float(nframes) for x in fp]
return fppi, miss_rate
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
#
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def initPlot():
fig, ax = plt.subplots()
fig.canvas.draw()
plt.xlabel("fppi")
plt.ylabel("miss rate")
plt.title("ROC curve Bahnhof")
plt.grid(True)
plt.xscale('log')
plt.yscale('log')
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def showPlot(name):
plt.savefig(name)
plt.show()
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def plotLogLog(fppi, miss_rate, c):
plt.semilogy(fppi, miss_rate, color = c, linewidth = 2)
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:
bb = dt.bb
x1, y1, x2, y2 = dt.bb[0], dt.bb[1], dt.bb[2], dt.bb[3]
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
self.conf = conf
self.matched = False
def crop(self, factor):
self.bb = crop_rect(self.bb, factor)
# we use rect-stype for dt and box style for gt. ToDo: fix it
def overlap(self, b):
a = self.bb
w = min( a[0] + a[2], b[2]) - max(a[0], b[0]);
h = min( a[1] + a[3], b[3]) - max(a[1], b[1]);
cross_area = 0.0 if (w < 0 or h < 0) else float(w * h)
union_area = (a[2] * a[3]) + ((b[2] - b[0]) * (b[3] - b[1])) - cross_area;
return cross_area / union_area
def mark_matched(self):
self.matched = True
def parse_inria(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 = l.split('"')[-2]
return Sample(path, bbs)
def glob_set(pattern):
return [__n for __n in glob.iglob(pattern)] #glob.iglob(pattern)
# parse ETH idl file
def parse_idl(f):
map = {}
for l in open(f):
l = re.sub(r"^\"left\/", "{\"", l)
l = re.sub(r"\:", ":[", l)
l = re.sub(r"(\;|\.)$", "]}", l)
map.update(eval(l))
return map
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])
def norm_acpect_ratio(boxes, ratio):
return [ norm_box(box, ratio) for box in boxes]
def match(gts, dts):
# Cartesian product for each detection BB_dt with each BB_gt
overlaps = [[dt.overlap(gt) for gt in gts]for dt in dts]
matches_gt = [0]*len(gts)
matches_dt = [0]*len(dts)
for idx, row in enumerate(overlaps):
imax = row.index(max(row))
if (matches_gt[imax] == 0 and row[imax] > 0.5):
matches_gt[imax] = 1
matches_dt[idx] = 1
return matches_dt