文本检测实战：使用OpenCV实现文本检测（EAST 文本检测器）

$ tree --dirsfirst
.
├── images
│   ├── car_wash.png
│   ├── lebron_james.jpg
│   └── sign.jpg
├── frozen_east_text_detection.pb
├── text_detection.py
└── text_detection_video.py

# import the necessary packages
from imutils.object_detection import non_max_suppression
import numpy as np
import argparse
import time
import cv2
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", type=str,
  help="path to input image")
ap.add_argument("-east", "--east", type=str,
  help="path to input EAST text detector")
ap.add_argument("-c", "--min-confidence", type=float, default=0.5,
  help="minimum probability required to inspect a region")
ap.add_argument("-w", "--width", type=int, default=320,
  help="resized image width (should be multiple of 32)")
ap.add_argument("-e", "--height", type=int, default=320,
  help="resized image height (should be multiple of 32)")
args = vars(ap.parse_args())

# load the input image and grab the image dimensions
image = cv2.imread(args["image"])
orig = image.copy()
(H, W) = image.shape[:2]
# set the new width and height and then determine the ratio in change
# for both the width and height
(newW, newH) = (args["width"], args["height"])
rW = W / float(newW)
rH = H / float(newH)
# resize the image and grab the new image dimensions
image = cv2.resize(image, (newW, newH))
(H, W) = image.shape[:2]

# define the two output layer names for the EAST detector model that
# we are interested -- the first is the output probabilities and the
# second can be used to derive the bounding box coordinates of text
layerNames = [
  "feature_fusion/Conv_7/Sigmoid",
  "feature_fusion/concat_3"]

# load the pre-trained EAST text detector
print("[INFO] loading EAST text detector...")
net = cv2.dnn.readNet(args["east"])
# construct a blob from the image and then perform a forward pass of
# the model to obtain the two output layer sets
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()
# show timing information on text prediction
print("[INFO] text detection took {:.6f} seconds".format(end - start))

# grab the number of rows and columns from the scores volume, then
# initialize our set of bounding box rectangles and corresponding
# confidence scores
(numRows, numCols) = scores.shape[2:4]
rects = []
confidences = []
# loop over the number of rows
for y in range(0, numRows):
  # extract the scores (probabilities), followed by the geometrical
  # data used to derive potential bounding box coordinates that
  # surround text
  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]

    # loop over the number of columns
  for x in range(0, numCols):
    # if our score does not have sufficient probability, ignore it
    if scoresData[x] < args["min_confidence"]:
      continue
    # compute the offset factor as our resulting feature maps will
    # be 4x smaller than the input image
    (offsetX, offsetY) = (x * 4.0, y * 4.0)
    # extract the rotation angle for the prediction and then
    # compute the sin and cosine
    angle = anglesData[x]
    cos = np.cos(angle)
    sin = np.sin(angle)
    # use the geometry volume to derive the width and height of
    # the bounding box
    h = xData0[x] + xData2[x]
    w = xData1[x] + xData3[x]
    # compute both the starting and ending (x, y)-coordinates for
    # the text prediction bounding box
    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)
    # add the bounding box coordinates and probability score to
    # our respective lists
    rects.append((startX, startY, endX, endY))
    confidences.append(scoresData[x])