def upload_image():
form = MyForm()
if form.validate_on_submit():
filename = images.save(form.image.data)
# return f'Filename: { filename }'
img = cv2.imread(r'D:\HTML\Flask Tutorial\static\Image\Uploads\\' +
filename)
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
_, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
# cv2.imshow('',thresh)
mor_img = cv2.morphologyEx(thresh,
cv2.MORPH_OPEN, (100, 100),
iterations=1)
contours, hierarchy = cv2.findContours(
mor_img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE
) # I addapted this part of the code. This is how my version works (2.4.16), but it could be different for OpenCV 3
sorted_contours = sorted(contours, key=cv2.contourArea, reverse=True)
for c in sorted_contours[1:]:
area = cv2.contourArea(c)
if area > 6000:
print(area)
cv2.drawContours(img, [c], -1, (0, 0, 200), 3)
x, y, w, h = cv2.boundingRect(
c
) # the lines below are for getting the approximate center of the rooms
cx = x + w / 2
cy = y + h / 2
# cv2.putText(img,str(area),(cx,cy), cv2.FONT_HERSHEY_SIMPLEX, .5,(255,0,0),1,cv2.CV_AA)
# cv2.imshow('',mor_img)
# cv2.imshow('',img)
# cv2.waitKey(0)
text = pytesseract.image_to_string(img, config="--psm 11")
sentence = text
print(nums_from_string.get_nums(sentence))
output = nums_from_string.get_nums(sentence)
return render_template(
r"UploadImage.html",
form=form,
detected_dimensions='Dimensions are {}'.format(output))
else:
return render_template(r"UploadImage.html", form=form)
def get_book_informations(url):
soup = get_soup(url)
books = [url]
product_info = {}
for row in soup.find(class_="table table-striped").find_all('tr'):
product_info[row.th.text] = row.td.text
books.append(product_info['UPC'])
books.append(soup.find(class_="product_main").h1.text)
books.append(product_info['Price (incl. tax)'].strip('Â'))
books.append(product_info['Price (excl. tax)'].strip('Â'))
books.append(nums_from_string.get_nums(product_info['Availability'])[0])
if soup.find("div", id={'product_description'}):
books.append(
soup.find("div",
id={'product_description'}).find_next_sibling("p").text)
books.append(
soup.find('li', class_='active').find_previous_sibling('li').a.text)
books.append(soup.find('p', class_='star-rating')['class'][1])
books.append(
urlRoot +
soup.find('div', class_="item active").img['src'].replace('../', ""))
return books
folders = os.listdir(inDir)
for folder in folders:
PATH = os.path.join(inDir, folder)
folder_2 = os.listdir(PATH)
for folder in folder_2:
PATH_2 = os.path.join(PATH, folder)
folder_3 = os.listdir(PATH_2)
for folder in folder_3:
PATH_3 = os.path.join(PATH_2, folder)
folder_4 = os.listdir(PATH_3)
for folder in folder_4:
PATH_4 = os.path.join(PATH_3, folder)
images = [
os.path.basename(x) for x in glob.glob(PATH_4 + '/*.dcm')
]
for f in images:
nums = nums_from_string.get_nums(f)
idx = int(nums[4])
if idx > 30 and idx < 70:
# split = int(f.split('_')[13])
# if split > 30 and split < 70:
ds = dicom.read_file(PATH_4 + '/' +
f) # read dicom image
img = ds.pixel_array # get image array
img = np.flip(img, axis=0)
imageio.imwrite(outDir + f.replace('.dcm', '.png'),
img)
# In[ ]:
cv2.imshow('', thresh)
mor_img = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, (100, 100), iterations=1)
contours, hierarchy = cv2.findContours(
mor_img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE
) # I addapted this part of the code. This is how my version works (2.4.16), but it could be different for OpenCV 3
sorted_contours = sorted(contours, key=cv2.contourArea, reverse=True)
for c in sorted_contours[1:]:
area = cv2.contourArea(c)
if area > 6000:
print(area)
cv2.drawContours(img, [c], -1, (0, 0, 200), 3)
x, y, w, h = cv2.boundingRect(
c
) # the lines below are for getting the approximate center of the rooms
cx = x + w / 2
cy = y + h / 2
# cv2.putText(img,str(area),(cx,cy), cv2.FONT_HERSHEY_SIMPLEX, .5,(255,0,0),1,cv2.CV_AA)
cv2.imshow('', mor_img)
# cv2.imshow('',img)
cv2.waitKey(0)
text = pytesseract.image_to_string(img, config="--psm 11")
sentence = text
print(nums_from_string.get_nums(sentence))