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Open Source Computer Vision Library
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103 lines
3.9 KiB
103 lines
3.9 KiB
#!/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', 'c', 'r', 'g', '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 range(s): |
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try: |
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lb, rb = map(int, s.split(',')) |
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return lb, rb |
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except: |
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raise argparse.ArgumentTypeError("Must be lb, rb") |
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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 method 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 resulting image.", default = "./roc.png") |
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parser.add_argument("-n", "--nscales", dest = "nscales", type = int, metavar= "n", help = "Preferred count of scales from min to max.", default = 55) |
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parser.add_argument("-r", "--scale-range", dest = "scale_range", type = range, default = (128 * 0.4, 128 * 2.4)) |
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parser.add_argument("-e", "--extended-range-ratio", dest = "ext_ratio", type = float, default = 1.25) |
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parser.add_argument("-t", "--title", dest = "title", type = str, default = "ROC curve Bahnhof") |
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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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parser.add_argument("-l", "--labels", dest = "labels" ,required=True, help = "Plot labels for legend.", nargs='+') |
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args = parser.parse_args() |
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print args.scale_range |
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print args.cascade |
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# parse annotations |
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sft.initPlot(args.title) |
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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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ignored = [] |
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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 = sft.filter_for_range(samples[tail], args.scale_range, args.ext_ratio) |
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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, skip_list = sft.match(boxes, dts) |
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tp = tp + matched |
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ignored = ignored + skip_list |
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print nframes, nannotated |
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fppi, miss_rate = sft.computeROC(confidenses, tp, nannotated, nframes, ignored) |
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sft.plotLogLog(fppi, miss_rate, plot_colors[idx]) |
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sft.showPlot(args.output, args.labels)
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