util.py

import numpy as np
import cv2
from collections import deque

pts = [deque(maxlen=30) for _ in range(1000)]

COLORS_10 =[(144,238,144),(178, 34, 34),(221,160,221),(  0,255,  0),(  0,128,  0),(210,105, 30),(220, 20, 60),
            (192,192,192),(255,228,196),( 50,205, 50),(139,  0,139),(100,149,237),(138, 43,226),(238,130,238),
            (255,  0,255),(  0,100,  0),(127,255,  0),(255,  0,255),(  0,  0,205),(255,140,  0),(255,239,213),
            (199, 21,133),(124,252,  0),(147,112,219),(106, 90,205),(176,196,222),( 65,105,225),(173,255, 47),
            (255, 20,147),(219,112,147),(186, 85,211),(199, 21,133),(148,  0,211),(255, 99, 71),(144,238,144),
            (255,255,  0),(230,230,250),(  0,  0,255),(128,128,  0),(189,183,107),(255,255,224),(128,128,128),
            (105,105,105),( 64,224,208),(205,133, 63),(  0,128,128),( 72,209,204),(139, 69, 19),(255,245,238),
            (250,240,230),(152,251,152),(  0,255,255),(135,206,235),(  0,191,255),(176,224,230),(  0,250,154),
            (245,255,250),(240,230,140),(245,222,179),(  0,139,139),(143,188,143),(255,  0,  0),(240,128,128),
            (102,205,170),( 60,179,113),( 46,139, 87),(165, 42, 42),(178, 34, 34),(175,238,238),(255,248,220),
            (218,165, 32),(255,250,240),(253,245,230),(244,164, 96),(210,105, 30)]


def draw_bbox(img, box, cls_name, identity=None, offset=(0,0)):
    '''
        draw box of an id
    '''
    x1,y1,x2,y2 = [int(i+offset[idx%2]) for idx,i in enumerate(box)]
    # set color and label text
    color = COLORS_10[identity%len(COLORS_10)] if identity is not None else COLORS_10[0]
    label = '{} {}'.format(cls_name, identity)
    # box text and bar
    t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 1 , 1)[0]
    cv2.rectangle(img,(x1, y1),(x2,y2),color,2)
    cv2.rectangle(img,(x1, y1),(x1+t_size[0]+3,y1+t_size[1]+4), color,-1)
    cv2.putText(img,label,(x1,y1+t_size[1]+4), cv2.FONT_HERSHEY_PLAIN, 1, [255,255,255], 1)
    return img


def draw_bboxes(img, bbox, identities, binary_masks=[], alpha = 0.33, offset=(0,0)):
    for i,box in enumerate(bbox):
        x1,y1,x2,y2 = [int(i) for i in box]
        x1 += offset[0]
        x2 += offset[0]
        y1 += offset[1]
        y2 += offset[1]
        # box text and bar
        id = int(identities[i]) if identities is not None else 0    
        color = COLORS_10[id%len(COLORS_10)]
        label = '{}{:d}'.format("", id)
        t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 2 , 2)[0]
        cv2.rectangle(img,(x1, y1),(x2,y2),color,3)
        cv2.rectangle(img,(x1, y1),(x1+t_size[0]+3,y1+t_size[1]+4), color,-1)
        cv2.putText(img,label,(x1,y1+t_size[1]+4), cv2.FONT_HERSHEY_PLAIN, 2, [255,255,255], 2)
        
        #draw trajectories
        center = (int((x1+x2)/2), int((y1+y2)/2))
        pts[id].append(center)
        cv2.circle(img, (center), 1, color, 5)
        #draw motion path
        for j in range(1, len(pts[id])):
            if pts[id][j-1] is None or pts[id][j] is None:
                continue
            thickness = int(np.sqrt(64/float(j+1))*2)
            #print((pts[id][j-1]), (pts[id][j]), (color), thickness)
            cv2.line(img, (pts[id][j-1]), (pts[id][j]), (color), thickness)
        
        # draw mask
        if i < len(binary_masks):
            mask = binary_masks[i]
            color = COLORS_10[0] #make all masks same color
            for c in range(3):
                img[:, :, c] = np.where(mask > 0, img[:, :, c] * (1-alpha) + alpha*color[c]*255, img[:, :, c])
    return img

def draw_polys(im, polys):
    for poly in polys:
        cv2.polylines(im, [poly], True, (255,255,255))
    return im

def draw_detections(detections, img):
    for det in detections:
        bbox = det.to_tlbr()
        if len(bbox):
            cv2.rectangle(img, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])), (255, 255, 255), 2)
    return img

def softmax(x):
    assert isinstance(x, np.ndarray), "expect x be a numpy array"
    x_exp = np.exp(x*5)
    return x_exp/x_exp.sum()

def softmin(x):
    assert isinstance(x, np.ndarray), "expect x be a numpy array"
    x_exp = np.exp(-x)
    return x_exp/x_exp.sum()



if __name__ == '__main__':
    x = np.arange(10)/10.
    x = np.array([0.5,0.5,0.5,0.6,1.])
    y = softmax(x)
    z = softmin(x)
    import ipdb; ipdb.set_trace()