def detect_box(image, scale=600, maxScale=900):
H, W = image.shape[:2]
image, rate = resize_img(image, scale, maxScale=maxScale)
h, w = image.shape[:2]
if GPU:
im = array_to_image(image)
res = predict_image(textNet, im)
scale = 16
iw = int(np.ceil(im.w / scale))
ih = int(np.ceil(im.h / scale))
h, w = image.shape[:2]
out = [res[i] for i in range(40 * ih * iw)]
out = np.array(out).reshape((1, 40, ih, iw))
else:
inputBlob = cv2.dnn.blobFromImage(image,
scalefactor=1.0,
size=(w, h),
swapRB=False,
crop=False)
outputName = textNet.getUnconnectedOutLayersNames()
textNet.setInput(inputBlob)
out = textNet.forward(outputName)[0]
clsOut = reshape(out[:, :20, ...])
boxOut = reshape(out[:, 20:, ...])
boxes = get_origin_box((w, h), anchors, boxOut[0])
scores = soft_max(clsOut[0])
boxes[:, 0:4][boxes[:, 0:4] < 0] = 0
boxes[:, 0][boxes[:, 0] >= w] = w - 1
boxes[:, 1][boxes[:, 1] >= h] = h - 1
boxes[:, 2][boxes[:, 2] >= w] = w - 1
boxes[:, 3][boxes[:, 3] >= h] = h - 1
# print (boxes)
return scores, boxes, rate, w, h
def detect_box(image, scale=600, maxScale=900):
H, W = image.shape[:2]
image, rate = resize_img(image, scale, maxScale=maxScale)
h, w = image.shape[:2]
inputBlob = cv2.dnn.blobFromImage(image,
scalefactor=1.0,
size=(w, h),
swapRB=False,
crop=False)
outputName = textNet.getUnconnectedOutLayersNames()
textNet.setInput(inputBlob)
out = textNet.forward(outputName)[0]
clsOut = reshape(out[:, :20, ...])
boxOut = reshape(out[:, 20:, ...])
boxes = get_origin_box((w, h), anchors, boxOut[0])
scores = soft_max(clsOut[0])
boxes[:, 0:4][boxes[:, 0:4] < 0] = 0
boxes[:, 0][boxes[:, 0] >= w] = w - 1
boxes[:, 1][boxes[:, 1] >= h] = h - 1
boxes[:, 2][boxes[:, 2] >= w] = w - 1
boxes[:, 3][boxes[:, 3] >= h] = h - 1
return scores, boxes, rate, w, h