First commit; add validation code

Signed-off-by: tzzcl <zclnjucs@gmail.com>

utils/IoU.py

@@ -0,0 +1,106 @@
+import numpy as np
+import xml.etree.ElementTree as ET
+
+def get_gt_boxes(xmlfile):
+    '''get ground-truth bbox from VOC xml file'''
+    tree = ET.parse(xmlfile)
+    objs = tree.findall('object')
+    num_objs = len(objs)
+    gt_boxes = []
+    for obj in objs:
+        bbox = obj.find('bndbox')
+        x1 = float(bbox.find('xmin').text)-1
+        y1 = float(bbox.find('ymin').text)-1
+        x2 = float(bbox.find('xmax').text)-1
+        y2 = float(bbox.find('ymax').text)-1
+
+        gt_boxes.append((x1, y1, x2, y2))
+    return gt_boxes
+
+def get_cls_gt_boxes(xmlfile, cls):
+    '''get ground-truth bbox from VOC xml file'''
+    tree = ET.parse(xmlfile)
+    objs = tree.findall('object')
+    num_objs = len(objs)
+    gt_boxes = []
+    for obj in objs:
+        bbox = obj.find('bndbox')
+        cls_name = obj.find('name').text
+        #print(cls_name, cls)
+        if cls_name != cls:
+            continue
+        x1 = float(bbox.find('xmin').text)-1
+        y1 = float(bbox.find('ymin').text)-1
+        x2 = float(bbox.find('xmax').text)-1
+        y2 = float(bbox.find('ymax').text)-1
+
+        gt_boxes.append((x1, y1, x2, y2))
+    if len(gt_boxes)==0:
+        pass
+        #print('%s bbox = 0'%cls)
+
+    return gt_boxes
+
+def get_cls_and_gt_boxes(xmlfile, cls,class_to_idx):
+    '''get ground-truth bbox from VOC xml file'''
+    tree = ET.parse(xmlfile)
+    objs = tree.findall('object')
+    num_objs = len(objs)
+    gt_boxes = []
+    for obj in objs:
+        bbox = obj.find('bndbox')
+        cls_name = obj.find('name').text
+        #print(cls_name, cls)
+        if cls_name != cls:
+            continue
+        x1 = float(bbox.find('xmin').text)-1
+        y1 = float(bbox.find('ymin').text)-1
+        x2 = float(bbox.find('xmax').text)-1
+        y2 = float(bbox.find('ymax').text)-1
+
+        gt_boxes.append((class_to_idx[cls_name],[x1, y1, x2-x1, y2-y1]))
+    if len(gt_boxes)==0:
+        pass
+        #print('%s bbox = 0'%cls)
+
+    return gt_boxes
+def convert_boxes(boxes):
+    ''' convert the bbox to the format (x1, y1, x2, y2) where x1,y1<x2,y2'''
+    converted_boxes = []
+    for bbox in boxes:
+        (x1, y1, x2, y2) = bbox
+        converted_boxes.append((min(x1, x2), min(y1, y2), max(x1, x2), max(y1, y2)))
+    return converted_boxes
+
+def IoU(a, b):
+    #print(a, b)
+    x1 = max(a[0], b[0])
+    y1 = max(a[1], b[1])
+    x2 = min(a[2], b[2])
+    y2 = min(a[3], b[3])
+
+    def compute_area(box):
+        dx = max(0, box[2]-box[0])
+        dy = max(0, box[3]-box[1])
+        dx = float(dx)
+        dy = float(dy)
+        return dx*dy
+
+    #print(x1, y1, x2, y2)
+    w = max(0, x2-x1+1)
+    h = max(0, y2-y1+1)
+    #inter = w*h
+    #aarea = (a[2]-a[0]+1)*(a[3]-a[1]+1)
+    #barea = (b[2]-b[0]+1)*(b[3]-b[1]+1)
+    inter = compute_area([x1, y1, x2, y2])
+    aarea = compute_area(a)
+    barea = compute_area(b)
+
+    #assert aarea+barea-inter>0
+    if aarea + barea - inter <=0:
+        print(a)
+        print(b)
+    o = inter / (aarea+barea-inter)
+    #if w<=0 or h<=0:
+    #    o = 0
+    return o

utils/func.py

@@ -0,0 +1,78 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def count_max(x):
+    count_dict = {}
+    for xlist in x:
+        for item in xlist:
+            if item==0:
+                continue
+            if item not in count_dict.keys():
+                count_dict[item] = 0
+            count_dict[item] += 1
+    if count_dict == {}:
+        return -1
+    count_dict = sorted(count_dict.items(), key=lambda d:d[1], reverse=True)
+    return count_dict[0][0]
+
+
+def sk_pca(X, k):
+    from sklearn.decomposition import PCA
+    pca = PCA(k)
+    pca.fit(X)
+    vec = pca.components_
+    #print(vec.shape)
+    return vec
+
+def fld(x1, x2):
+    x1, x2 = np.mat(x1), np.mat(x2)
+    n1 = x1.shape[0]
+    n2 = x2.shape[0]
+    k = x1.shape[1]
+
+    m1 = np.mean(x1, axis=0)
+    m2 = np.mean(x2, axis=0)
+    m = np.mean(np.concatenate((x1, x2), axis=0), axis=0)
+    print(x1.shape, m1.shape)
+
+
+    c1 = np.cov(x1.T)
+    s1 = c1*(n1-1)
+    c2 = np.cov(x2.T)
+    s2 = c2*(n2-1)
+    Sw = s1/n1 + s2/n2
+    print(Sw.shape)
+    W = np.dot(np.linalg.inv(Sw), (m1-m2).T)
+    print(W.shape)
+    W = W / np.linalg.norm(W, 2)
+    return np.mean(np.dot(x1, W)), np.mean(np.dot(x2, W)), W
+
+def pca(X, k):
+    n, m = X.shape
+    mean = np.mean(X, 0)
+    #print(mean.shape)
+    temp = X - mean
+    conv = np.cov(X.T)
+    #print(conv.shape)
+    conv1 = np.cov(temp.T)
+    #print(conv-conv1)
+
+    w, v = np.linalg.eig(conv)
+    #print(w.shape)
+    #print(v.shape)
+    index = np.argsort(-w)
+    vec = np.matrix(v.T[index[:k]])
+    #print(vec.shape)
+
+    recon = (temp * vec.T)*vec+mean
+
+    #print(X-recon)
+    return vec
+
+
+
+
+
+
+

utils/nms.py

@@ -0,0 +1,36 @@
+import numpy as np
+
+def nms(boxes, scores, thresh):
+    if len(boxes)==0:
+        return []
+    boxes = np.array(boxes)
+    #scores = np.array(scores)
+    x1 = boxes[:, 0]
+    y1 = boxes[:, 1]
+    x2 = boxes[:, 2]
+    y2 = boxes[:, 3]
+
+    areas = (x2-x1+1)*(y2-y1+1)
+
+    scores = np.array(scores)
+
+    order = np.argsort(scores)[-100:]
+    keep_boxes = []
+    while order.size > 0:
+        i = order[-1]
+        keep_boxes.append(boxes[i])
+
+        xx1 = np.maximum(x1[i], x1[order[:-1]])
+        yy1 = np.maximum(y1[i], y1[order[:-1]])
+        xx2 = np.minimum(x2[i], x2[order[:-1]])
+        yy2 = np.minimum(y2[i], y2[order[:-1]])
+
+        w = np.maximum(0.0, xx2-xx1+1)
+        h = np.maximum(0.0, yy2-yy1+1)
+        inter = w*h
+
+        ovr = inter / (areas[i] + areas[order[:-1]] - inter)
+        inds = np.where(ovr <= thresh)
+        order = order[inds]
+
+    return keep_boxes

utils/vis.py

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+from __future__ import print_function
+
+import cv2
+import numpy as np
+import os
+
+_GREEN = (18, 217, 15)
+_RED = (15, 18, 217)
+
+def vis_bbox(img, bbox, color=_GREEN, thick=1):
+    '''Visualize a bounding box'''
+    img = img.astype(np.uint8)
+    (x0, y0, x1, y1) = bbox
+    cv2.rectangle(img, (int(x0), int(y0)), (int(x1), int(y1)), color, thickness=thick)
+    return img
+
+def vis_one_image(img, boxes, color=_GREEN):
+    for bbox in boxes:
+        img = vis_bbox(img, (bbox[0], bbox[1], bbox[2], bbox[3]), color)
+    return img
+
+