def yolo_draw_detections(boxes, im, anchors, labels, threshold, nms_threshold):
def get_color(c,x,max):
colors = ( (1,0,1), (0,0,1),(0,1,1),(0,1,0),(1,1,0),(1,0,0) )
ratio = (float(x)/max)*5
i = np.floor(ratio)
j = np.ceil(ratio)
ratio -= i
r = (1-ratio) * colors[int(i)][int(c)] + ratio*colors[int(j)][int(c)]
return r*255
if type(im) is not np.ndarray:
imgcv = cv2.imread(im)
else: imgcv = im
h, w, _ = imgcv.shape
for b in boxes:
max_indx = np.argmax(b.probs)
max_prob = b.probs[max_indx]
label = 'object' * int(len(labels) < 2)
label += labels[max_indx] * int(len(labels)>1)
if max_prob > threshold:
left = int ((b.x - b.w/2.) * w)
right = int ((b.x + b.w/2.) * w)
top = int ((b.y - b.h/2.) * h)
bot = int ((b.y + b.h/2.) * h)
if left < 0 : left = 0
if right > w - 1: right = w - 1
if top < 0 : top = 0
if bot > h - 1: bot = h - 1
thick = int((h+w)/300)
mess = '{}'.format(label)
offset = max_indx*123457 % len(labels)
color = (get_color(2,offset,len(labels)),
get_color(1,offset,len(labels)),
get_color(0,offset,len(labels)))
cv2.rectangle(imgcv,
(left, top), (right, bot),
color, thick)
font = cv2.FONT_HERSHEY_SIMPLEX
scale = 0.65
thickness = 1
size=cv2.getTextSize(mess, font, scale, thickness)
cv2.rectangle(im, (left-2,top-size[0][1]-4), (left+size[0][0]+4,top), color, -1)
cv2.putText(im, mess, (left+2,top-2), font, scale, (0,0,0), thickness, cv2.LINE_AA)
return imgcv
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