def detect_text_symbol(img, symbol_list):
"""
Detects text in a list of symbols.
Arguments:
img {numpy.ndarray} -- Reference image
symbol_list {list} -- List of coordinates of the symbols
Returns:
list -- List of dictionaries containing the coordinates of the symbols and the coordinates of text detected in symbols
"""
try:
ret = []
for l in symbol_list:
a, b, c, d = l[0].item(), l[1].item(), l[2].item(), l[3].item(),
print(a)
print(type(a))
crop = img[b:d, a:c]
lst = detect_text(img_in_memory=True, img=crop, img_name='temp_symbol')
lt = []
for t in lst:
lt.append([int(t[0]) + l[0], int(t[1]) + l[1], int(t[2]) + l[2], int(t[3]) + l[3], t[4]])
ret.append({'symbol':l, 'text':lt})
return ret
except:
error_present = 1
logging.error(' Error in detect_text_symbol function : ')
error_message = PrintException()
logging.error(error_message)
raise
def receipt_parse(image):
preprocessed_image = preprocess(image)
cv2.imwrite("temp.jpg", preprocessed_image)
with io.open("temp.jpg", 'rb') as image_file:
content = image_file.read()
text_list = detect_text(content)
if text_list == []:
text_list = detect_text(image)
information = parse_text(text_list)
return information
def getRes(data, pic):
result = {}
# 3. crop pic using dimentions in template and save
for key in data:
coord = data[key]['coord']
for i in range(len(coord)):
coord[i] = int(coord[i] * pic.size[i % 2] / 100)
temp = pic.crop((coord))
temp.save('tmp.jpg', )
# 4. send the cropped pic in detect_text
text = detect_text('tmp.jpg')
# 5. save the text in dict w key from template name
result[key] = text.rpartition('\n')[2].lstrip()
return result
def main():
bucket='itemstodetect'
photo='cheetos.jpeg'
#photo='abc.jpg'
text=detect_text(photo,bucket)
manywords = False
combinewords = 1
print(text)
if text==None or len(text)<1:
labels=detect_labels_local_file(photo,bucket)
try:
for i in labels:
query = f"Amazon:{i[0]}"
for j in search(query, tld="co.in",num=1,stop=1,pause=1):
if j[0:22]!='https://www.amazon.com':
continue
print(i[0])
print(j)
webbrowser.open_new(j)
except:
print('Not found')
else:
try:
if manywords:
text = "Amazon:"+' '.join(text[0:combinewords])
for j in search(text, tld="co.in",num=1,stop=1,pause=1):
if j[0:22]!='https://www.amazon.com':
continue
print(j)
#webbrowser.open_new(j)
else:
for i in text:
query = f"Amazon:{i}"
for j in search(query, tld="co.in",num=1,stop=1,pause=1):
if j[0:22]!='https://www.amazon.com':
continue
print(i)
print(j)
webbrowser.open_new(j)
except:
print('Too many words')
1] < 1010: #center[1]>820 and center[1]<1010: center[1]>366 and center[1]<447:
cv2.imwrite('images/outcam.jpg', image)
number_plate, score = number_detect('images/outcam.jpg')
if score is not None:
lis.append(number_plate)
lis1.append(score)
j = j + 1
cv2.circle(image, center, 10, (0, 0, 255), -1)
cv2.rectangle(image, (startX, startY), (endX, endY),
COLORS[idx], 2)
if center[1] > 1010 and len(lis) != 0 and len(lis1) != 0:
s = datetime.datetime.now().strftime('%d-%m-%Y %H:%M:%S')
img_path = lis[lis1.index(max(lis1))]
path = 'number_plate/image' + str(j) + '.jpg'
cv2.imwrite(path, img_path)
number_string = detect_text(path)
#print(number_string)
data = {
"vehicle": {
"out_time": s,
"number_plate": number_string,
}
}
r = requests.put(url=API_ENDPOINT, json=data)
del (lis[:])
del (lis1[:])
#cv2.imshow('vid3',image)
#if cv2.waitKey(30) and 0xFF==ord('q'):
# break
return information
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('input_file', help='The image for text detection.')
args = parser.parse_args()
preprocessed_image = preprocess(args.input_file)
cv2.imwrite("temp.jpg", preprocessed_image)
#work on changing this later ^^^
with io.open("temp.jpg", 'rb') as image_file:
content = image_file.read()
text_list = detect_text(content)
if text_list == []:
with io.open(args.input_file, 'rb') as image_file:
content = image_file.read()
text_list = detect_text(content)
information = parse_text(text_list)
print(information)