def pre_process_image(file_path):
norm2dp_image_path = 'norm2dp.jpg'
final_image_path = 'final_image_processed.jpg'
im = Image.open(file_path)
l, w = im.size
factor = fact(l)
size = int(factor * l), int(factor * w)
im_resized = im.resize(size, Image.ANTIALIAS)
im_resized.save(norm2dp_image_path, dpi=(200, 200))
im_new = smooth_image.smooth2(norm2dp_image_path)
cv2.imwrite(final_image_path, im_new)
return final_image_path
def bhayankar_image_processing(file_path):
norm2dp_image_path = 'norm2dp.jpg'
final_image_path = 'final_image_processed.jpg'
im = Image.open(file_path)
l, w = im.size
factor = fact(l)
size = int(factor * l), int(factor * w)
im_resized = im.resize(size, Image.ANTIALIAS)
im_resized.save(norm2dp_image_path, dpi=(200, 200))
im_new = smooth_image.smooth2(norm2dp_image_path)
cv2.imwrite(final_image_path, im_new)
return final_image_path
def extract_image(file_name):
img = cv2.imread(file_name)
img_final = cv2.imread(file_name)
img2gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
inv_img = (255 - img2gray)
kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (5, 2))
dilated = cv2.dilate(inv_img, kernel, iterations=7) # dilate
type_image = dilated
_, contours, hierarchy = cv2.findContours(
type_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) # get contours
ind = 0
pix = {}
value_at = {}
index = 0
P = {}
image_2_text = smooth_image.smooth2(file_name)
for contour in contours:
# get rectangle bounding contour
[x, y, w, h] = cv2.boundingRect(contour)
# draw rectangle around contour on original image
if w < 20 or h < 20:
continue
if w > 500 and h > 500:
continue
cv2.rectangle(img, (x, y), (x + w + 10, y + h + 10), (255, 0, 255), 2)
s = '/tmp/' + str(ind) + '.tif'
box_read = image_process_extract_string(s, image_2_text, x, y, w, h)
# print box_read
D[(x, y)] = box_read
ind += 1
box_read_to_lines = box_read.split('\n')
for lines in box_read_to_lines:
P[(x, y)] = lines
value_at[index] = (x, y)
index += 1
x1 = x / 50
x1 = x1 * 50
tup = [[x, lines]]
for key, val in tup:
pix.setdefault(key, []).append(val)
cv2.imwrite('boxed_image.jpg', img)
# print D
final_list2 = []
sorted_x = sorted(D.items(), key=operator.itemgetter(0))
# print sorted_x
for k, v in sorted(D.items()):
# print v
list_new = str(v).split('\n')
for l in list_new:
final_list2.append(l)
'''final_list = []
for val in pix:
for dish in pix[val]:
if len(dish) > 1:
final_list.append(dish)
'''
return final_list2
def extract_image(file_name):
try:
img = cv2.imread(file_name)
img_final = cv2.imread(file_name)
img2gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
inv_img = (255 - img2gray)
kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (5, 2))
dilated = cv2.dilate(inv_img, kernel, iterations=7) # dilate
type_image = dilated
contours, hierarchy = cv2.findContours(type_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) # get contours
except:
print
'Image_Processing Error in menu_json'
ind = 0
pix = {}
value_at = {}
index = 0
P = {}
image_2_text = smooth_image.smooth2(file_name)
try:
for contour in contours:
# get rectangle bounding contour
[x, y, w, h] = cv2.boundingRect(contour)
# draw rectangle around contour on original image
if w < 20 or h < 20:
continue
if w > 500 and h > 500:
continue
cv2.rectangle(img, (x, y), (x + w + 10, y + h + 10), (255, 0, 255), 2)
s = 'meta/' + str(ind) + '.tif'
box_read = image_process_extract_string(s, image_2_text, x, y, w, h)
# print box_read
D[(x, y)] = box_read
ind += 1
box_read_to_lines = box_read.split('\n')
for lines in box_read_to_lines:
P[(x, y)] = lines;
value_at[index] = (x, y)
index += 1
x1 = x / 50
x1 = x1 * 50
tup = [[x, lines]]
for key, val in tup:
pix.setdefault(key, []).append(val)
cv2.imwrite('boxed_image.jpg', img)
except:
print
'In menu_json'
# print D
final_list2 = []
sorted_x = sorted(D.items(), key=operator.itemgetter(0))
# print sorted_x
for k, v in sorted(D.items()):
# print v
list_new = str(v).split('\n')
for l in list_new:
final_list2.append(l)
'''final_list = []
for val in pix:
for dish in pix[val]:
if len(dish) > 1:
final_list.append(dish)
'''
return final_list2
