# This code is a version of https://www.pyimagesearch.com/2018/08/20/opencv-text-detection-east-text-detector/
# that has been converted to functions and has some additions.
import logging
import time
import cv2
import numpy as np
from imutils.object_detection import non_max_suppression
logger = logging.getLogger(__name__)
[docs]def load_east(east_path="frozen_east_text_detection.pb"):
"""Load the pre-trained EAST model.
Args:
east_path (str, optional): Path to the EAST model file. Defaults to
"frozen_east_text_detection.pb".
"""
if type(east_path) is cv2.dnn_Net:
return east_path
logger.debug("Loading EAST text detector...")
net = cv2.dnn.readNet(east_path)
return net
[docs]def get_text_bounding_boxes(
image, net, min_confidence=0.5, resized_width=320, resized_height=320
):
"""Determine the locations of text in an image.
Args:
image (np.array): The image to be processed.
net (cv2.dnn_Net): The EAST model loaded with :meth:`~lecture2notes.end_to_end.text_detection.load_east`.
min_confidence (float, optional): Minimum probability required to inspect a region. Defaults to 0.5.
resized_width (int, optional): Resized image width (should be multiple of 32). Defaults to 320.
resized_height (int, optional): Resized image height (should be multiple of 32). Defaults to 320.
Returns:
list: The coordinates of bounding boxes containing text.
"""
if type(net) is str:
net = load_east(net)
# load the input image and grab the image dimensions
(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) = (resized_width, resized_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"]
# 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
logger.debug("Text detection took %.6f seconds", 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] < 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
scaled_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)
scaled_boxes.append((endX, endY, startX, startY))
# draw the bounding box on the image
# cv2.rectangle(orig, (startX, startY), (endX, endY), (0, 255, 0), 2)
# cv2.imwrite("text_bounding_boxes.png", orig)
return scaled_boxes
# show the output image
# cv2.imshow("Text Detection", orig)
# cv2.waitKey(0)