def my_stitch_multiple(images, ratio=0.75, reprojThresh=4.0, affine=False):
firstLoop = True
first_image = Image(None, None)
second_image = Image(None, None)
temp_image = Image(None, None)
for i in range(len(images)):
if firstLoop:
first_image = images[i]
second_image = images[i + 1]
firstLoop = False
else:
first_image = temp_image
second_image = images[i]
try:
temp_image, left_image, homograph = stitch_two(
first_image,
second_image,
affine=affine,
ratio=ratio,
reprojThresh=reprojThresh)
temp_image.image = fix_distortion(temp_image.image, strength=0.41)
temp_image.image = _crop_black(temp_image.image)
print("Progress: " + str(i) + "/" + str(len(images) - 1))
except:
print("Error: " + str(i) + "/" + str(len(images) - 1) +
", with image " + str(images[i]))
output_image_name = "stitched_img_.png"
temp_image.save(path="/home/hduser/dev-materials/",
filename=output_image_name)
return temp_image
def warp_perspective(img1, img2, H, affine=False, skip=False):
# Gets the width and height of the current images for future point
# reading use
h1, w1 = img1.image.shape[:2]
h2, w2 = img2.image.shape[:2]
# Convert the top left, bottom left, bottom right, top left
# (respectively) points of the image into numpy arrays
pts1 = np.float32([[0, 0], [0, h1], [w1, h1], [w1, 0]]).reshape(-1, 1, 2)
pts2 = np.float32([[0, 0], [0, h2], [w2, h2], [w2, 0]]).reshape(-1, 1, 2)
# Find dimensions of the transformed right image with a homograph for
# things to be the correct size for the future warp
pts2_ = cv2.perspectiveTransform(pts2, H)
# If the user wants an affine transformation (removes warping and
# keeps parellel lines parellel), remove the bottom line of the
# homograph. This is the quickest method to both warp the photo to the
# best of SURF abilities and still keep it affine
if affine:
[[a, b, c], [d, e, f], [g, h, i]] = H
H = [[a, b, c], [d, e, f], [0, 0, 1]]
pts = np.concatenate((pts1, pts2_), axis=0)
[xmin, ymin] = np.int32(pts.min(axis=0).ravel() - 0.5)
[xmax, ymax] = np.int32(pts.max(axis=0).ravel() + 0.5)
t = [-xmin, -ymin]
# The homograph that will move the image around with the given
# homograph so that no edges are cropped off, and most of the black
# border is gone
Ht = np.array([[1, 0, t[0]], [0, 1, t[1]], [0, 0, 1]]) # translate
# Using the calculated homograph and the given homograph, warp the img
result = Image(None, None)
if skip:
result.image = img2.image
else:
result.image = cv2.warpPerspective(img2.image, Ht.dot(H),
(xmax - xmin, ymax - ymin))
# Place the unwarped photo over top of the warped one. You now have a
# stitched image!
result.image[t[1]:h1 + t[1], t[0]:w1 + t[0]] = overlay_image(
result.image[t[1]:h1 + t[1], t[0]:w1 + t[0]], img1.image)
# Update the unwarped image's coordinates, since it is now a part of
# the stitched picture
img1.place_image(t[0], t[1], w1 + t[0], h1 + t[1])
# Return the stitched image, the image that was placed on top of the
# result (with an updated current coordinates), and the homograph for
# bookkeeping reasons
return result, img1, Ht.dot(H)
def upload(request):
if request.method == 'POST':
data = request.POST.get('img')
u_id = request.POST.get('uid')
g_id = request.POST.get('gid')
buf = StringIO.StringIO()
for line in data.splitlines():
line = line.strip().replace(" ", "")
if not line:
continue
bytes = binascii.unhexlify(line)
buf.write(bytes)
path = str(g_id) + str(u_id) + ".jpg"
with open(path, "wb") as f:
f.write(buf.getvalue())
img = Image()
img.image = path
img.save()
text = pytesseract.image_to_string(Image.open(path))
texts = text.split('\n')
items = {}
for t in texts:
m = re.search("\w", t.reverse())
if m and m.start() == 0:
price = t[m.start():]
item = t[:m.start()]
items[item] = price
return JsonResponse(items)
def draw_matches(left_image, right_image, left_key_points, right_key_points,
matches, status):
# Shows all the little lines between the two pictures that were matches
# Plays connect the dots with green lines and the keypoints
(left_height, left_width) = left_image.shape[:2]
(right_height, right_width) = right_image.shape[:2]
vis = np.zeros(
(max(left_height, right_height), left_width + right_width, 3),
dtype="uint8")
vis[0:left_height, 0:left_width] = left_image
vis[0:right_height, left_width:] = right_image
# loop over the matches
for ((trainIdx, queryIdx), s) in zip(matches, status):
# Only process the match if the keypoint was successfully matched
if s == 1:
# Draw the match
left_point = (int(left_key_points[queryIdx][0]),
int(left_key_points[queryIdx][1]))
right_point = (int(right_key_points[trainIdx][0]) + left_width,
int(right_key_points[trainIdx][1]))
cv2.line(vis, left_point, right_point, (0, 255, 0), 1)
# return the visualization
img_vis = Image(None, None)
img_vis.image = vis
return img_vis
def post(self, request, *args, **kwargs):
post = request.POST
image_data = request.FILES
print("\n\n\n\n\n")
# print(post)
# print(image_data['image'])
dynamic_fields = {}
try:
delete = post['delete']
Item.objects.get(id=kwargs['id']).delete()
return redirect('adminview')
except Exception as e:
print(e)
# print(post)
for key, value in post.items():
# print(value)
if key not in [
'name', 'brand', 'price', 'stock', 'category', 'engine',
'csrfmiddlewaretoken'
] and value != '':
print("{}:{} \n".format(key, value))
dynamic_fields[key] = value
name = post['name']
brand = post['brand']
price = post['price']
stock = post['stock']
category = post['category']
category = Category.objects.filter(name__icontains=category).first()
data = dynamic_fields
new_item = Item.objects.get(id=kwargs['id'])
new_item.name = name
new_item.brand = brand
new_item.price = price
new_item.stock = stock
new_item.category = category
new_item.data = data
new_item.save()
# for image in images:
if 'image' in image_data:
new_image = Image()
new_image.image = image_data['image']
new_image.item = new_item
new_image.save()
context = {}
context['categories'] = Category.objects.all()
context['item'] = Item.objects.get(id=kwargs['id'])
return render(request, self.template_name, context)
def post(self, request, *args, **kwargs):
post = request.POST
image_data = request.FILES
print("\n\n\n\n\n")
# print(post)
# print(image_data['image'])
dynamic_fields = {}
# print(post)
for key, value in post.items():
# print(value)
if key not in [
'name', 'brand', 'price', 'stock', 'category', 'engine',
'csrfmiddlewaretoken'
] and value != '':
print("{}:{} \n".format(key, value))
dynamic_fields[key] = value
name = post['name']
brand = post['brand']
price = post['price']
stock = post['stock']
category = post['category']
category = Category.objects.filter(name__icontains=category).first()
data = dynamic_fields
new_item = Item()
new_item.name = name
new_item.brand = brand
new_item.price = price
new_item.stock = stock
new_item.category = category
new_item.data = data
new_item.save()
# for image in images:
new_image = Image()
new_image.image = image_data['image']
new_image.item = new_item
new_image.save()
return render(request, self.template_name, {})
def crop(self, face):
""" Return a new Image cropped to the face """
result = Image(self.name)
result.image = self.image.crop(face.as_box())
return result
def crop(self, face):
""" Return a new Image cropped to the face """
result = Image(self.name)
result.image = self.image.crop(face.as_box())
return result
def clone(self):
new = Image(self.width, self.height)
new.image = self.image.copy()
return new
