282 lines
8.6 KiB
Python
282 lines
8.6 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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from itertools import izip
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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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""" Create new instance of soft cascade."""
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def cascade(min_scale, max_scale, nscales, f):
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# where we use nms cv::SoftCascadeDetector::DOLLAR == 2
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c = cv2.softcascade_Detector(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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""" Compute prefix sum for en array."""
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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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""" Compute x and y arrays for ROC plot."""
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def computeROC(confidenses, tp, nannotated, nframes, ignored):
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confidenses, tp, ignored = zip(*sorted(zip(confidenses, tp, ignored), reverse = True))
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fp = [(1 - x) for x in tp]
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fp = [(x - y) for x, y in izip(fp, ignored)]
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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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""" Crop rectangle by factor."""
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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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""" Initialize plot axises."""
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def initPlot(name):
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plt.xlabel("fppi")
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plt.ylabel("miss rate")
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plt.title(name)
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plt.grid(True)
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plt.xscale('log')
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plt.yscale('log')
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""" Draw plot."""
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def plotLogLog(fppi, miss_rate, c):
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plt.loglog(fppi, miss_rate, color = c, linewidth = 2)
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""" Show resulted plot."""
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def showPlot(file_name, labels):
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plt.axis((pow(10, -3), pow(10, 1), .035, 1))
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plt.yticks( [0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.64, 0.8, 1], ['.05', '.10', '.20', '.30', '.40', '.50', '.64', '.80', '1'] )
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plt.legend(labels, loc = "lower left")
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plt.savefig(file_name)
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plt.show()
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""" Filter true positives and ignored detections for cascade detector output."""
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def match(gts, dts):
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matches_gt = [0]*len(gts)
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matches_dt = [0]*len(dts)
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matches_ignore = [0]*len(dts)
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if len(gts) == 0:
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return matches_dt, matches_ignore
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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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for idx, row in enumerate(overlaps):
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imax = row.index(max(row))
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# try to match ground truth
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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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for idx, dt in enumerate(dts):
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# try to math ignored
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if matches_dt[idx] == 0:
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row = gts
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row = [i for i in row if (i[3] - i[1]) < 53 or (i[3] - i[1]) > 256]
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for each in row:
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if dts[idx].overlapIgnored(each) > 0.5:
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matches_ignore[idx] = 1
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return matches_dt, matches_ignore
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""" Draw detections or ground truth on image."""
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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 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-style for dt and box style for gt. ToDo: fix it
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def overlap(self, 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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# we use rect-style for dt and box style for gt. ToDo: fix it
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def overlapIgnored(self, 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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self_area = (a[2] * a[3]);
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return cross_area / self_area
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def mark_matched(self):
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self.matched = True
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"""Parse INPIA annotation format"""
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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)]
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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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""" Normalize detection box to unified aspect ration."""
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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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""" Process array of boxes."""
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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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""" Filter detections out of extended range. """
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def filter_for_range(boxes, scale_range, ext_ratio):
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boxes = norm_acpect_ratio(boxes, 0.5)
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boxes = [b for b in boxes if (b[3] - b[1]) > scale_range[0] / ext_ratio]
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boxes = [b for b in boxes if (b[3] - b[1]) < scale_range[1] * ext_ratio]
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return boxes
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""" Resize sample for training."""
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def resize_sample(image, d_w, d_h):
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h, w, _ = image.shape
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if (d_h < h) or (d_w < w):
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ratio = min(d_h / float(h), d_w / float(w))
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kernel_size = int( 5 / (2 * ratio))
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sigma = 0.5 / ratio
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image_to_resize = cv2.filter2D(image, cv2.CV_8UC3, cv2.getGaussianKernel(kernel_size, sigma))
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interpolation_type = cv2.INTER_AREA
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else:
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image_to_resize = image
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interpolation_type = cv2.INTER_CUBIC
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return cv2.resize(image_to_resize,(d_w, d_h), None, 0, 0, interpolation_type)
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newobj = re.compile("^lbl=\'(\w+)\'\s+str=(\d+)\s+end=(\d+)\s+hide=0$")
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class caltech:
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@staticmethod
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def extract_objects(f):
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objects = []
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tmp = []
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for l in f:
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if newobj.match(l) is not None:
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objects.append(tmp)
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tmp = []
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tmp.append(l)
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return objects[1:]
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@staticmethod
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def parse_header(f):
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_ = f.readline() # skip first line (version string)
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head = f.readline()
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(nFrame, nSample) = re.search(r'nFrame=(\d+) n=(\d+)', head).groups()
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return (int(nFrame), int(nSample))
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@staticmethod
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def parse_pos(l):
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pos = re.match(r'^posv?\s*=(\[[\d\s\.\;]+\])$', l).group(1)
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pos = re.sub(r"(\[)(\d)", "\\1[\\2", pos)
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pos = re.sub(r"\s", ", ", re.sub(r"\;\s+(?=\])", "]", re.sub(r"\;\s+(?!\])", "],[", pos)))
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return eval(pos)
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@staticmethod
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def parse_occl(l):
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occl = re.match(r'^occl\s*=(\[[\d\s\.\;]+\])$', l).group(1)
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occl = re.sub(r"\s(?!\])", ",", occl)
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return eval(occl)
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def parse_caltech(f):
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(nFrame, nSample) = caltech.parse_header(f)
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objects = caltech.extract_objects(f)
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annotations = [[] for i in range(nFrame)]
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for obj in objects:
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(type, start, end) = re.search(r'^lbl=\'(\w+)\'\s+str=(\d+)\s+end=(\d+)\s+hide=0$', obj[0]).groups()
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print type, start, end
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start = int(start) -1
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end = int(end)
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pos = caltech.parse_pos(obj[1])
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posv = caltech.parse_pos(obj[2])
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occl = caltech.parse_occl(obj[3])
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for idx, (p, pv, oc) in enumerate(zip(*[pos, posv, occl])):
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annotations[start + idx].append((type, p, oc, pv))
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return annotations
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