def test_api_post2():
path = "C:\\Users\\CAU\\Desktop\\capstone\\text_recognition\demo_image"
if os.path.exists(path):
for file in os.scandir(path):
os.remove(file.path)
imagefile = request.files['image']
filename = werkzeug.utils.secure_filename(imagefile.filename)
print("\nReceived image File name : " + imagefile.filename)
imagefile.save("./text_detection/test/" + filename)
detection.run_detection()
img_files, img_bbox = load_files()
crop_img(img_files, img_bbox)
pred_str = recognition.run_recognition()
for i, file in enumerate(img_files):
txt = pd.read_csv(img_bbox[i], header=None)
df = pd.DataFrame(columns=["x1", "y1", "x2", "y2", "x3", "y3", "x4", "y4", "result_text"])
for num, _col in enumerate(list(df)[:-1]):
df[_col] = txt[num]
df["result_text"] = pred_str
df.to_csv("./result.csv")
return "done"
def test_api_post():
response_header = response_json_ops()
# ------
imagefile = request.files['image']
filename = werkzeug.utils.secure_filename(imagefile.filename)
print("\nReceived image File name : " + imagefile.filename)
imagefile.save("./text_detection/test/" + filename)
detection.run_detection()
img_files, img_bbox = load_files()
crop_img(img_files, img_bbox)
pred_str = recognition.run_recognition()
# [l, t], [r, t], [r, b], [l, b]
for i, file in enumerate(img_files):
txt = pd.read_csv(img_bbox[i], header=None)
df = pd.DataFrame(columns=[
"x1", "y1", "x2", "y2", "x3", "y3", "x4", "y4", "result_text"
])
for num, _col in enumerate(list(df)[:-1]):
df[_col] = txt[num]
df["result_text"] = pred_str
df.to_csv("./result.csv")
return response_header
def test_api_post():
path = "C:\\Users\\CAU\\Desktop\\capstone\\text_recognition\demo_image"
if os.path.exists(path):
for file in os.scandir(path):
os.remove(file.path)
imagefile = request.files['image']
filename = werkzeug.utils.secure_filename(imagefile.filename)
print("\nReceived image File name : " + imagefile.filename)
imagefile.save("./text_detection/test/" + filename)
# time.sleep(5)
detection.run_detection()
# time.sleep(5)
img_files, img_bbox = load_files()
crop_img(img_files, img_bbox)
pred_str = recognition.run_recognition()
# underline detection
cfg = PredictionConfig()
# define the model
model = MaskRCNN(mode='inference', model_dir='./', config=cfg)
# load model weights
model_path = 'mask_rcnn_underline_cfg_0020.h5'
model.load_weights(model_path, by_name=True)
temp = cv2.imread("./text_detection/test/androidFlask.jpg")
yhat = model.detect([temp], verbose=0)[0]
print(len(yhat['rois']))
# [l, t], [r, t], [r, b], [l, b]
for i, file in enumerate(img_files):
txt = pd.read_csv(img_bbox[i], header=None)
df = pd.DataFrame(columns=["x1", "y1", "x2", "y2", "x3", "y3", "x4", "y4", "result_text"])
# compare
for i, bb in enumerate(txt.values):
x1, y1, x2, y2, x3, y3, x4, y4 = bb
# textbb = [x1, y1, x3, y3]
for underline in yhat['rois']:
uy1, ux1, uy2, ux2 = underline
if (ux1 + ux2) / 2 > x1 and (ux1 + ux2) / 2 < x3 and y1 < uy1 and uy1 < y3:
df = df.append({"x1": x1, "y1": y1, "x2": x2, "y2": y2, "x3": x3, "y3": y3, "x4": x4, "y4": y4,
"result_text": pred_str[i]}, ignore_index=True)
temp = cv2.rectangle(temp, (x1, y1), (x3, y3), (0, 0, 255), 3)
# top-left corner and bottom-right corner of rectangle.
df.to_csv("./result.csv")
cv2.imwrite("./result.jpg", temp)
from keras import backend as K
K.clear_session()
cuda.select_device(0)
cuda.close()
del model
return "done"
def run_main():
detection.run_detection()
img_files, img_bbox = load_files()
crop_img(img_files, img_bbox)
pred_str = recognition.run_recognition()
# [l, t], [r, t], [r, b], [l, b]
for i, file in enumerate(img_files):
txt = pd.read_csv(img_bbox[i], header=None)
df = pd.DataFrame(columns=[
"x1", "y1", "x2", "y2", "x3", "y3", "x4", "y4", "result_text"
])
for num, _col in enumerate(list(df)[:-1]):
df[_col] = txt[num]
df["result_text"] = pred_str
df.to_csv("./result.csv")
def run_main():
detection.run_detection()
img_files, img_bbox = load_files()
crop_img(img_files, img_bbox)
pred_str = recognition.run_recognition()
print("recog done")
from Mask_RCNN.mrcnn.model import MaskRCNN
cfg = PredictionConfig()
# define the model
model = MaskRCNN(mode='inference', model_dir='./', config=cfg)
# load model weights
model_path = 'mask_rcnn_underline_cfg_0019.h5'
model.load_weights(model_path, by_name=True)
temp = cv2.imread("./text_detection/test/androidFlask.jpg")
yhat = model.detect([temp], verbose=0)[0]
# [l, t], [r, t], [r, b], [l, b]
for i, file in enumerate(img_files):
txt = pd.read_csv(img_bbox[i], header=None)
df = pd.DataFrame(columns=[
"x1", "y1", "x2", "y2", "x3", "y3", "x4", "y4", "result_text"
])
# compare
#temp = []
for i, bb in enumerate(txt.values):
x1, y1, _, _, x3, y3, _, _ = bb
# textbb = [x1, y1, x3, y3]
for underline in yhat['rois']:
uy1, ux1, uy2, ux2 = underline
if (ux1 + ux2) / 2 > x1 and (
ux1 + ux2) / 2 < x3 and y1 < uy1 and uy1 < y3:
print(pred_str[i])
for num, _col in enumerate(list(df)[:-1]):
df[_col] = txt[num]
df["result_text"] = pred_str
df.to_csv("./result.csv")