173 lines
4.4 KiB
Python
173 lines
4.4 KiB
Python
#!/usr/bin/env python
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import cv2, re, glob
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import numpy as np
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import matplotlib.pyplot as plt
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""" Convert numpy matrices with rectangles and confidences to sorted list of detections."""
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def convert2detections(rects, confs, crop_factor = 0.125):
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if rects is None:
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return []
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dts = zip(*[rects.tolist(), confs.tolist()])
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dts = zip(dts[0][0], dts[0][1])
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dts = [Detection(r,c) for r, c in dts]
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dts.sort(lambda x, y : -1 if (x.conf - y.conf) > 0 else 1)
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for dt in dts:
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dt.crop(crop_factor)
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return dts
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def crop_rect(rect, factor):
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val_x = factor * float(rect[2])
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val_y = factor * float(rect[3])
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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)]
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return x
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#
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def plot_curve():
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fig, ax = plt.subplots()
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fig.canvas.draw()
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x = np.linspace(pow(10,-4), pow(10,1), 101)
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y = 1 - x
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plt.semilogy(x,y,color='m',linewidth=2)
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plt.xlabel("fppi")
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plt.ylabel("miss rate")
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plt.title("ROC curve Bahnhof")
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plt.yticks( [0.05, 0.10, 0.20, 0.30, 0.40, 0.50, 0.64, 0.80])
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ylabels = [item.get_text() for item in ax.get_yticklabels()]
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ax.set_yticklabels( ylabels )
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plt.grid(True)
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# plt.xticks( [pow(10, -4), pow(10, -3), pow(10, -2), pow(10, -1), pow(10, 0), pow(10, 0)])
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# xlabels = [item.get_text() for item in ax.get_xticklabels()]
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# ax.set_xticklabels( xlabels )
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plt.xscale('log')
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plt.show()
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def draw_rects(img, rects, color, l = lambda x, y : x + y):
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if rects is not None:
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for x1, y1, x2, y2 in rects:
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cv2.rectangle(img, (x1, y1), (l(x1, x2), l(y1, y2)), color, 2)
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def draw_dt(img, dts, color, l = lambda x, y : x + y):
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if dts is not None:
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for dt in dts:
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bb = dt.bb
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x1, y1, x2, y2 = dt.bb[0], dt.bb[1], dt.bb[2], dt.bb[3]
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cv2.rectangle(img, (x1, y1), (l(x1, x2), l(y1, y2)), color, 2)
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class Annotation:
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def __init__(self, bb):
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self.bb = bb
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class Detection:
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def __init__(self, bb, conf):
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self.bb = bb
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self.conf = conf
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self.matched = False
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def crop(self, factor):
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self.bb = crop_rect(self.bb, factor)
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# we use rect-stype for dt and box style for gt. ToDo: fix it
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def overlap(self, b):
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print self.bb, "vs", b
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a = self.bb
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w = min( a[0] + a[2], b[2]) - max(a[0], b[0]);
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h = min( a[1] + a[3], b[3]) - max(a[1], b[1]);
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cross_area = 0.0 if (w < 0 or h < 0) else float(w * h)
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union_area = (a[2] * a[3]) + ((b[2] - b[0]) * (b[3] - b[1])) - cross_area;
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return cross_area / union_area
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def mark_matched(self):
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self.matched = True
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def parse_inria(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 = l.split('"')[-2]
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return Sample(path, bbs)
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def glob_set(pattern):
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return [__n for __n in glob.iglob(pattern)] #glob.iglob(pattern)
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# parse ETH idl file
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def parse_idl(f):
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map = {}
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for l in open(f):
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l = re.sub(r"^\"left\/", "{\"", l)
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l = re.sub(r"\:", ":[", l)
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l = re.sub(r"(\;|\.)$", "]}", l)
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map.update(eval(l))
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return map
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def norm_box(box, ratio):
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middle = float(box[0] + box[2]) / 2.0
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new_half_width = float(box[3] - box[1]) * ratio / 2.0
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return (int(round(middle - new_half_width)), box[1], int(round(middle + new_half_width)), box[3])
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def norm_acpect_ratio(boxes, ratio):
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return [ norm_box(box, ratio) for box in boxes]
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def match(gts, dts):
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for dt in dts:
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print dt.bb,
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print
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for gt in gts:
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print gt
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# Cartesian product for each detection BB_dt with each BB_gt
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overlaps = [[dt.overlap(gt) for gt in gts]for dt in dts]
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print overlaps
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matches_gt = [0]*len(gts)
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print matches_gt
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matches_dt = [0]*len(dts)
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print matches_dt
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for idx, row in enumerate(overlaps):
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print idx, row
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imax = row.index(max(row))
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if (matches_gt[imax] == 0 and row[imax] > 0.5):
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matches_gt[imax] = 1
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matches_dt[idx] = 1
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print matches_gt
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print matches_dt
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fp = sum(1 for x in matches_dt if x == 0)
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fn = sum(1 for x in matches_gt if x == 0)
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return fp, fn |