1 场景
在python环境下，使用EAST文本检测器使用OpenCV检测自然场景图像中的文本。
即通过算法，检测标识出来自然图像中的文字区域。
可以检测图像中的文字区域和视频中的文字区域。
之后可以截取文字区域图像，再通过OCR算法，检测出文字的内容。
检测效果如下：
1.png
2 官网
（1）英文官方地址
https://www.pyimagesearch.com/2018/08/20/opencv-text-detection-east-text-detector/
（2）中文译文地址
https://segmentfault.com/a/1190000018622750
（3）源码地址（百度网盘）
链接：https://pan.baidu.com/s/1DlWXraYRqCsg5WJ-B3te8w
提取码：cmcn
源码项目结构：
.
├── images
│ ├── car_wash.png
│ ├── lebron_james.jpg
│ └── sign.jpg
├── frozen_east_text_detection.pb
├── text_detection.py
└── text_detection_video.py
3 依赖
（1）CV2
pip install opencv-python
（2）numpy
pip install numpy
（3）argparse
pip install argparse
4 代码
4.1 识别图片
（1）识别代码text_detection.py
# USAGE
# python text_detection.py --image images/lebron_james.jpg --east frozen_east_text_detection.pb
# 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])
# apply non-maxima suppression to suppress weak, overlapping bounding
# boxes
boxes = non_max_suppression(np.array(rects), probs=confidences)
# loop over the bounding boxes
for (startX, startY, endX, endY) in boxes:
# scale the bounding box coordinates based on the respective
# ratios
startX = int(startX * rW)
startY = int(startY * rH)
endX = int(endX * rW)
endY = int(endY * rH)
# draw the bounding box on the image
cv2.rectangle(orig, (startX, startY), (endX, endY), (0, 255, 0), 2)
# show the output image
cv2.imshow("Text Detection", orig)
cv2.waitKey(0)
（2）使用
python text_detection.py --image images/car_wash.png --east frozen_east_text_detection.pb
结果如下：
2.png
4.2 识别视频
（1）识别代码text_detection_video.py
# USAGE
# python text_detection_video.py --east frozen_east_text_detection.pb
# import the necessary packages
from imutils.video import VideoStream
from imutils.video import FPS
from imutils.object_detection import non_max_suppression
import numpy as np
import argparse
import imutils
import time
import cv2
def decode_predictions(scores, geometry):
# 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])
# return a tuple of the bounding boxes and associated confidences
return (rects, confidences)
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-east", "--east", type=str, required=True,
help="path to input EAST text detector")
ap.add_argument("-v", "--video", type=str,
help="path to optinal input video file")
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())
# initialize the original frame dimensions, new frame dimensions,
# and ratio between the dimensions
(W, H) = (None, None)
(newW, newH) = (args["width"], args["height"])
(rW, rH) = (None, None)
# 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"])
# if a video path was not supplied, grab the reference to the web cam
if not args.get("video", False):
print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
time.sleep(1.0)
# otherwise, grab a reference to the video file
else:
vs = cv2.VideoCapture(args["video"])
# start the FPS throughput estimator
fps = FPS().start()
# loop over frames from the video stream
while True:
# grab the current frame, then handle if we are using a
# VideoStream or VideoCapture object
frame = vs.read()
frame = frame[1] if args.get("video", False) else frame
# check to see if we have reached the end of the stream
if frame is None:
break
# resize the frame, maintaining the aspect ratio
frame = imutils.resize(frame, width=1000)
orig = frame.copy()
# if our frame dimensions are None, we still need to compute the
# ratio of old frame dimensions to new frame dimensions
if W is None or H is None:
(H, W) = frame.shape[:2]
rW = W / float(newW)
rH = H / float(newH)
# resize the frame, this time ignoring aspect ratio
frame = cv2.resize(frame, (newW, newH))
# construct a blob from the frame and then perform a forward pass
# of the model to obtain the two output layer sets
blob = cv2.dnn.blobFromImage(frame, 1.0, (newW, newH),
(123.68, 116.78, 103.94), swapRB=True, crop=False)
net.setInput(blob)
(scores, geometry) = net.forward(layerNames)
# decode the predictions, then apply non-maxima suppression to
# suppress weak, overlapping bounding boxes
(rects, confidences) = decode_predictions(scores, geometry)
boxes = non_max_suppression(np.array(rects), probs=confidences)
# loop over the bounding boxes
for (startX, startY, endX, endY) in boxes:
# scale the bounding box coordinates based on the respective
# ratios
startX = int(startX * rW)
startY = int(startY * rH)
endX = int(endX * rW)
endY = int(endY * rH)
# draw the bounding box on the frame
cv2.rectangle(orig, (startX, startY), (endX, endY), (0, 255, 0), 2)
# update the FPS counter
fps.update()
# show the output frame
cv2.imshow("Text Detection", orig)
key = cv2.waitKey(1) & 0xFF
# if the `q` key was pressed, break from the loop
if key == ord("q"):
break
# stop the timer and display FPS information
fps.stop()
print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
# if we are using a webcam, release the pointer
if not args.get("video", False):
vs.stop()
# otherwise, release the file pointer
else:
vs.release()
# close all windows
cv2.destroyAllWindows()
（2）使用
python text_detection_video.py --video video/v1.mp4 --east frozen_east_text_detection.pb
结果，将得到一个圈起来文字的视频。
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