python - 如何制作单个边界框

Question

我正在使用 OCR 进行文本检测，在我的程序中，如果一张照片有多个文本，它会生成多个边界框。我想知道是否有一种方法可以组合所有框并制作文本的新裁剪输出图像。PS：我使用的是 EAST 深度学习文本。问题是它检测到图像中的文本，但如果文本相距较远，它会根据该原始图像创建 2 或 3 个图像，我正在尝试寻找一种方法将这 2-3 个作物组合成1.

(newW, newH) = (args["width"], args["height"])
rW = W / float(newW)
rH = H / float(newH)

image = cv2.resize(image, (newW, newH))
(H, W) = image.shape[:2]


layerNames = [
    "feature_fusion/Conv_7/Sigmoid",
    "feature_fusion/concat_3"]


print("[INFO] loading EAST text detector...")
net = cv2.dnn.readNet(args["east"])


blob = cv2.dnn.blobFromImage(image, 1.0, (W, H),
                             (123.68, 116.78, 103.94), swapRB=True, 
crop=False)
start = time.time()
net.setInput(blob)
(scores, geometry) = net.forward(layerNames)
end = time.time()

print("[INFO] text detection took {:.6f} seconds".format(end - start))


(numRows, numCols) = scores.shape[2:4]
rects = []
confidences = []

for y in range(0, numRows):

scoresData = scores[0, 0, y]
xData0 = geometry[0, 0, y]
xData1 = geometry[0, 1, y]
xData2 = geometry[0, 2, y]
xData3 = geometry[0, 3, y]
anglesData = geometry[0, 4, y]


for x in range(0, numCols):

    if scoresData[x] < args["min_confidence"]:
    continue


    (offsetX, offsetY) = (x * 4.0, y * 4.0)


    angle = anglesData[x]
    cos = np.cos(angle)
    sin = np.sin(angle)


    h = xData0[x] + xData2[x]
    w = xData1[x] + xData3[x]


    endX = int(offsetX + (cos * xData1[x]) + (sin * xData2[x]))
    endY = int(offsetY - (sin * xData1[x]) + (cos * xData2[x]))
    startX = int(endX - w)
    startY = int(endY - h)


    rects.append((startX, startY, endX, endY))
    confidences.append(scoresData[x])


boxes = non_max_suppression(np.array(rects), probs=confidences)

for number, (startX, startY, endX, endY) in enumerate(boxes):

    startX = int(startX * rW)
    startY = int(startY * rH)
    endX = int(endX * rW)
    endY = int(endY * rH)

   Final = orig[startY:endY, startX:endX]
   cv2.imshow("Text Detection", Final)
   cv2.waitKey(0)

   cv2.imwrite("crop{}.jpg".format(number), Final)

标签： pythonopencvimage-processingocrbounding-box