add ROC estimation
in the same way as Dallar's matlab toolbox does
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marina.kolpakova
parent
4c4c878b1b
commit
469eeea370
@ -34,43 +34,47 @@ if __name__ == "__main__":
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args = parser.parse_args()
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# parse annotations
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samples = call_parser(args.anttn_format, args.annotations)
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# where we use nms cv::SCascade::DOLLAR == 2
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cascade = cv2.SCascade(args.min_scale, args.max_scale, args.nscales, 2)
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xml = cv2.FileStorage(args.cascade, 0)
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dom = xml.getFirstTopLevelNode()
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assert cascade.load(dom)
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cascade = sft.cascade(args.min_scale, args.max_scale, args.nscales, args.cascade)
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pattern = args.input
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camera = cv2.VideoCapture(pattern)
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frame = 0
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# for plotting over dataset
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nannotated = 0
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nframes = 0
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confidenses = []
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tp = []
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while True:
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ret, img = camera.read()
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if not ret:
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break;
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name = pattern % (frame,)
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qq = pattern.format(frame)
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name = pattern % (nframes,)
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_, tail = os.path.split(name)
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boxes = samples[tail]
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boxes = sft.norm_acpect_ratio(boxes, 0.5)
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frame = frame + 1
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nannotated = nannotated + len(boxes)
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nframes = nframes + 1
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rects, confs = cascade.detect(img, rois = None)
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if confs is None:
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continue
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dts = sft.convert2detections(rects, confs)
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sft.draw_dt(img, dts, bgr["green"])
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fp, fn = sft.match(boxes, dts)
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print "fp and fn", fp, fn
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confs = confs.tolist()[0]
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confs.sort(lambda x, y : -1 if (x - y) > 0 else 1)
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confidenses = confidenses + confs
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matched = sft.match(boxes, dts)
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tp = tp + matched
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sft.draw_rects(img, boxes, bgr["blue"], lambda x, y : y)
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cv2.imshow("result", img);
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if (cv2.waitKey (0) == 27):
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break;
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print nframes, nannotated
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# sft.plot_curve()
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fppi, miss_rate = sft.computeROC(confidenses, tp, nannotated, nframes)
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sft.plotLogLog(fppi, miss_rate)
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@ -19,6 +19,34 @@ def convert2detections(rects, confs, crop_factor = 0.125):
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return dts
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def cascade(min_scale, max_scale, nscales, f):
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# where we use nms cv::SCascade::DOLLAR == 2
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c = cv2.SCascade(min_scale, max_scale, nscales, 2)
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xml = cv2.FileStorage(f, 0)
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dom = xml.getFirstTopLevelNode()
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assert c.load(dom)
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return c
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def cumsum(n):
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cum = []
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y = 0
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for i in n:
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y += i
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cum.append(y)
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return cum
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def computeROC(confidenses, tp, nannotated, nframes):
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confidenses, tp = zip(*sorted(zip(confidenses, tp), reverse = True))
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fp = [(1 - x) for x in tp]
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fp = cumsum(fp)
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tp = cumsum(tp)
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miss_rate = [(1 - x / (nannotated + 0.000001)) for x in tp]
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fppi = [x / float(nframes) for x in fp]
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return fppi, miss_rate
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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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@ -27,29 +55,28 @@ def crop_rect(rect, factor):
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#
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def plot_curve():
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def plotLogLog(fppi, miss_rate):
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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.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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# plt.xticks( [pow(10, -4), pow(10, -3), pow(10, -2), pow(10, -1), pow(10, 0), pow(10, 1)])
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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.yscale('log')
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plt.semilogy(fppi, miss_rate, color='m', linewidth=2)
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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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@ -81,7 +108,6 @@ class Detection:
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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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@ -135,39 +161,16 @@ def norm_acpect_ratio(boxes, ratio):
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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
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return matches_dt
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