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88 lines
3.1 KiB
Python
Executable File
88 lines
3.1 KiB
Python
Executable File
#!/usr/bin/env python
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import argparse
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import sft
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import sys, os, os.path, glob, math, cv2
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from datetime import datetime
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import numpy
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plot_colors = ['b', 'r', 'g', 'c', 'm']
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# "key" : ( b, g, r)
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bgr = { "red" : ( 0, 0, 255),
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"green" : ( 0, 255, 0),
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"blue" : (255, 0 , 0)}
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def call_parser(f, a):
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return eval( "sft.parse_" + f + "('" + a + "')")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description = 'Plot ROC curve using Caltech mathod of per image detection performance estimation.')
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# positional
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parser.add_argument("cascade", help = "Path to the tested detector.", nargs='+')
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parser.add_argument("input", help = "Image sequence pattern.")
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parser.add_argument("annotations", help = "Path to the annotations.")
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# optional
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parser.add_argument("-m", "--min_scale", dest = "min_scale", type = float, metavar= "fl", help = "Minimum scale to be tested.", default = 0.4)
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parser.add_argument("-M", "--max_scale", dest = "max_scale", type = float, metavar= "fl", help = "Maximum scale to be tested.", default = 5.0)
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parser.add_argument("-o", "--output", dest = "output", type = str, metavar= "path", help = "Path to store resultiong image.", default = "./roc.png")
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parser.add_argument("-n", "--nscales", dest = "nscales", type = int, metavar= "n", help = "Prefered count of scales from min to max.", default = 55)
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# required
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parser.add_argument("-f", "--anttn-format", dest = "anttn_format", choices = ['inria', 'caltech', "idl"], help = "Annotation file for test sequence.", required = True)
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args = parser.parse_args()
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print args.cascade
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# # parse annotations
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sft.initPlot()
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samples = call_parser(args.anttn_format, args.annotations)
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for idx, each in enumerate(args.cascade):
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print each
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cascade = sft.cascade(args.min_scale, args.max_scale, args.nscales, each)
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pattern = args.input
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camera = cv2.VideoCapture(pattern)
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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 % (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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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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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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print nframes, nannotated
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fppi, miss_rate = sft.computeROC(confidenses, tp, nannotated, nframes)
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sft.plotLogLog(fppi, miss_rate, plot_colors[idx])
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sft.showPlot("roc_curve.png") |