mirror of
https://github.com/opencv/opencv.git
synced 2024-11-30 06:10:02 +08:00
104 lines
3.9 KiB
Python
Executable File
104 lines
3.9 KiB
Python
Executable File
#!/usr/bin/env python
|
|
|
|
import argparse
|
|
import sft
|
|
|
|
import sys, os, os.path, glob, math, cv2
|
|
from datetime import datetime
|
|
import numpy
|
|
|
|
plot_colors = ['b', 'c', 'r', 'g', 'm']
|
|
|
|
# "key" : ( b, g, r)
|
|
bgr = { "red" : ( 0, 0, 255),
|
|
"green" : ( 0, 255, 0),
|
|
"blue" : (255, 0 , 0)}
|
|
|
|
def range(s):
|
|
try:
|
|
lb, rb = map(int, s.split(','))
|
|
return lb, rb
|
|
except:
|
|
raise argparse.ArgumentTypeError("Must be lb, rb")
|
|
|
|
def call_parser(f, a):
|
|
return eval( "sft.parse_" + f + "('" + a + "')")
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser(description = 'Plot ROC curve using Caltech method of per image detection performance estimation.')
|
|
|
|
# positional
|
|
parser.add_argument("cascade", help = "Path to the tested detector.", nargs='+')
|
|
parser.add_argument("input", help = "Image sequence pattern.")
|
|
parser.add_argument("annotations", help = "Path to the annotations.")
|
|
|
|
# optional
|
|
parser.add_argument("-m", "--min_scale", dest = "min_scale", type = float, metavar= "fl", help = "Minimum scale to be tested.", default = 0.4)
|
|
parser.add_argument("-M", "--max_scale", dest = "max_scale", type = float, metavar= "fl", help = "Maximum scale to be tested.", default = 5.0)
|
|
parser.add_argument("-o", "--output", dest = "output", type = str, metavar= "path", help = "Path to store resulting image.", default = "./roc.png")
|
|
parser.add_argument("-n", "--nscales", dest = "nscales", type = int, metavar= "n", help = "Preferred count of scales from min to max.", default = 55)
|
|
|
|
parser.add_argument("-r", "--scale-range", dest = "scale_range", type = range, default = (128 * 0.4, 128 * 2.4))
|
|
parser.add_argument("-e", "--extended-range-ratio", dest = "ext_ratio", type = float, default = 1.25)
|
|
parser.add_argument("-t", "--title", dest = "title", type = str, default = "ROC curve Bahnhof")
|
|
|
|
# required
|
|
parser.add_argument("-f", "--anttn-format", dest = "anttn_format", choices = ['inria', 'caltech', "idl"], help = "Annotation file for test sequence.", required = True)
|
|
parser.add_argument("-l", "--labels", dest = "labels" ,required=True, help = "Plot labels for legend.", nargs='+')
|
|
|
|
args = parser.parse_args()
|
|
|
|
print args.scale_range
|
|
|
|
print args.cascade
|
|
# parse annotations
|
|
sft.initPlot(args.title)
|
|
samples = call_parser(args.anttn_format, args.annotations)
|
|
for idx, each in enumerate(args.cascade):
|
|
print each
|
|
cascade = sft.cascade(args.min_scale, args.max_scale, args.nscales, each)
|
|
pattern = args.input
|
|
camera = cv2.VideoCapture(pattern)
|
|
|
|
# for plotting over dataset
|
|
nannotated = 0
|
|
nframes = 0
|
|
|
|
confidenses = []
|
|
tp = []
|
|
ignored = []
|
|
|
|
while True:
|
|
ret, img = camera.read()
|
|
if not ret:
|
|
break;
|
|
|
|
name = pattern % (nframes,)
|
|
_, tail = os.path.split(name)
|
|
|
|
boxes = sft.filter_for_range(samples[tail], args.scale_range, args.ext_ratio)
|
|
|
|
nannotated = nannotated + len(boxes)
|
|
nframes = nframes + 1
|
|
rects, confs = cascade.detect(img, rois = None)
|
|
|
|
if confs is None:
|
|
continue
|
|
|
|
dts = sft.convert2detections(rects, confs)
|
|
|
|
confs = confs.tolist()[0]
|
|
confs.sort(lambda x, y : -1 if (x - y) > 0 else 1)
|
|
confidenses = confidenses + confs
|
|
|
|
matched, skip_list = sft.match(boxes, dts)
|
|
tp = tp + matched
|
|
ignored = ignored + skip_list
|
|
|
|
print nframes, nannotated
|
|
|
|
fppi, miss_rate = sft.computeROC(confidenses, tp, nannotated, nframes, ignored)
|
|
sft.plotLogLog(fppi, miss_rate, plot_colors[idx])
|
|
|
|
sft.showPlot(args.output, args.labels)
|