def get_scores(image,
east='frozen_east_text_detection.pb',
min_confidence=0.5,
width=320,
height=320):
if TESTING:
cv.imshow('SCORES INPUT', image)
(imageHeight, imageWidth) = image.shape[:2]
layerNames = ["feature_fusion/Conv_7/Sigmoid", "feature_fusion/concat_3"]
# load the pre-trained EAST text detector
log("[INFO] loading EAST text detector...")
log('east:', east)
net = cv.dnn.readNetFromTensorflow(east)
blob = cv.dnn.blobFromImage(image,
1.0, (imageWidth, imageHeight),
(123.68, 116.78, 103.94),
swapRB=True,
crop=False)
start = time.time()
net.setInput(blob)
(scores, geometry) = net.forward(layerNames)
end = time.time()
log("[INFO] text detection took {:.6f} seconds".format(end - start))
confidenceThreshold = min_confidence
return decode(scores, geometry, confidenceThreshold)
def text_detection(image, east, min_confidence, width, height):
# load the input image and grab the image dimensions
image = cv2.imread(image)
orig = image.copy()
(origHeight, origWidth) = image.shape[:2]
# set the new width and height and then determine the ratio in change
# for both the width and height
(newW, newH) = (width, height)
ratioWidth = origWidth / float(newW)
ratioHeight = origHeight / float(newH)
# resize the image and grab the new image dimensions
image = cv2.resize(image, (newW, newH))
(imageHeight, imageWidth) = 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(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, (imageWidth, imageHeight),
(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))
# NMS on the the unrotated rects
confidenceThreshold = min_confidence
nmsThreshold = 0.4
# decode the blob info
(rects, confidences, baggage) = decode(scores, geometry,
confidenceThreshold)
offsets = []
thetas = []
for b in baggage:
offsets.append(b['offset'])
thetas.append(b['angle'])
##########################################################
functions = [nms.felzenszwalb.nms, nms.fast.nms, nms.malisiewicz.nms]
print("[INFO] Running nms.boxes . . .")
for i, function in enumerate(functions):
start = time.time()
indicies = nms.boxes(rects,
confidences,
nms_function=function,
confidence_threshold=confidenceThreshold,
nsm_threshold=nmsThreshold)
end = time.time()
indicies = np.array(indicies).reshape(-1)
drawrects = np.array(rects)[indicies]
name = function.__module__.split('.')[-1].title()
print("[INFO] {} NMS took {:.6f} seconds and found {} boxes".format(
name, end - start, len(drawrects)))
drawOn = orig.copy()
drawBoxes(drawOn, drawrects, ratioWidth, ratioHeight, (0, 255, 0), 2)
title = "nms.boxes {}".format(name)
cv2.imshow(title, drawOn)
cv2.moveWindow(title, 150 + i * 300, 150)
cv2.waitKey(0)
# convert rects to polys
polygons = utils.rects2polys(rects, thetas, offsets, ratioWidth,
ratioHeight)
print("[INFO] Running nms.polygons . . .")
for i, function in enumerate(functions):
start = time.time()
indicies = nms.polygons(polygons,
confidences,
nms_function=function,
confidence_threshold=confidenceThreshold,
nsm_threshold=nmsThreshold)
end = time.time()
indicies = np.array(indicies).reshape(-1)
drawpolys = np.array(polygons)[indicies]
name = function.__module__.split('.')[-1].title()
print("[INFO] {} NMS took {:.6f} seconds and found {} boxes".format(
name, end - start, len(drawpolys)))
drawOn = orig.copy()
drawPolygons(drawOn, drawpolys, ratioWidth, ratioHeight, (0, 255, 0),
2)
title = "nms.polygons {}".format(name)
cv2.imshow(title, drawOn)
cv2.moveWindow(title, 150 + i * 300, 150)
cv2.waitKey(0)
def text_detect(image, min_confidence, width, height):
# load the input image and grab the image dimensions
image = cv2.imread(image)
orig = image.copy()
(origHeight, origWidth) = image.shape[:2]
# set the new width and height and then determine the ratio in change
# for both the width and height
(newW, newH) = (width, height)
ratioWidth = origWidth / float(newW)
ratioHeight = origHeight / float(newH)
# resize the image and grab the new image dimensions
image = cv2.resize(image, (newW, newH))
(imageHeight, imageWidth) = 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 = text_model
# 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, (imageWidth, imageHeight),
(123.68, 116.78, 103.94),
swapRB=True,
crop=False)
start = time.time()
net.setInput(blob)
(scores, geometry) = net.forward(layerNames)
end = time.time()
# NMS on the the unrotated rects
confidenceThreshold = min_confidence
nmsThreshold = 0.4
# decode the blob info
(rects, confidences, baggage) = decode(scores, geometry,
confidenceThreshold)
offsets = []
thetas = []
for b in baggage:
offsets.append(b['offset'])
thetas.append(b['angle'])
##########################################################
functions = [nms.felzenszwalb.nms, nms.fast.nms, nms.malisiewicz.nms]
drawpolys_count, drawrects_count = 0, 0
for i, function in enumerate(functions):
indicies = nms.boxes(rects,
confidences,
nms_function=function,
confidence_threshold=confidenceThreshold,
nsm_threshold=nmsThreshold)
indicies = np.array(indicies).reshape(-1)
if len(indicies) != 0:
drawrects = np.array(rects)[indicies]
drawpolys_count = len(drawrects)
# convert rects to polys
polygons = utils.rects2polys(rects, thetas, offsets, ratioWidth,
ratioHeight)
for i, function in enumerate(functions):
start = time.time()
indicies = nms.polygons(polygons,
confidences,
nms_function=function,
confidence_threshold=confidenceThreshold,
nsm_threshold=nmsThreshold)
end = time.time()
indicies = np.array(indicies).reshape(-1)
if len(indicies) != 0:
drawpolys = np.array(polygons)[indicies]
drawrects_count = len(drawpolys)
return [drawpolys_count, drawrects_count]