def blend_images(opacity, mode, img_1, img_2):
if opacity > 0:
if mode == "Dodge":
return blend_modes.dodge(img_1, img_2, opacity)
elif mode == "Addition":
return blend_modes.addition(img_1, img_2, opacity)
elif mode == "Overlay":
return blend_modes.overlay(img_1, img_2, opacity)
elif mode == "Subtract":
return blend_modes.subtract(img_1, img_2, opacity)
elif mode == "Grain Extract":
return blend_modes.grain_extract(img_1, img_2, opacity)
elif mode == "Darken Only":
return blend_modes.darken_only(img_1, img_2, opacity)
elif mode == "Screen":
return blend_modes.screen(img_1, img_2, opacity)
elif mode == "Divide":
return blend_modes.divide(img_1, img_2, opacity)
elif mode == "Grain Merge":
return blend_modes.grain_merge(img_1, img_2, opacity)
elif mode == "Difference":
return blend_modes.difference(img_1, img_2, opacity)
elif mode == "Multiply":
return blend_modes.multiply(img_1, img_2, opacity)
elif mode == "Soft Light":
return blend_modes.soft_light(img_1, img_2, opacity)
elif mode == "Hard Light":
return blend_modes.hard_light(img_1, img_2, opacity)
elif mode == "Lighten Only":
return blend_modes.lighten_only(img_1, img_2, opacity)
elif mode == "None":
return blend_modes.normal(img_1, img_2, opacity)
else:
return img_1
def mergeRGB(video_dict, codec, mode):
capA = cv2.VideoCapture(video_dict['A'])
capB = cv2.VideoCapture(video_dict['B'])
frame_width = int(capA.get(3))
frame_height = int(capA.get(4))
frame_rate = round(capA.get(5), 2)
input_name = os.path.splitext(os.path.basename(video_dict['A']))
output_name = mode + "_RGBMerge_" + input_name[0][:-4] + input_name[1]
out = cv2.VideoWriter(output_name, codec, frame_rate,
(frame_width, frame_height))
while (capA.isOpened()):
retA, frameA = capA.read()
retB, frameB = capB.read()
if retA == True:
## give frames an alpha channel to prepare for blending; blend_modes requires 32bit
frameA = cv2.cvtColor(frameA, cv2.COLOR_BGR2BGRA,
4).astype(np.float32)
frameB = cv2.cvtColor(frameB, cv2.COLOR_BGR2BGRA,
4).astype(np.float32)
if mode == "difference":
extraChannel = blend_modes.difference(frameA, frameB, 1)
elif mode == "multiply":
extraChannel = blend_modes.multiply(frameA, frameB, 1)
else:
extraChannel = np.zeros((frame_width, frame_height, 3),
np.uint8)
extraChannel = cv2.cvtColor(extraChannel, cv2.COLOR_BGR2BGRA,
4).astype(np.float32)
## get rid of alpha channel in preparation for converting back to grayscale; opencv prefers 8bit
frameA = cv2.cvtColor(frameA, cv2.COLOR_BGRA2BGR).astype(np.uint8)
frameB = cv2.cvtColor(frameB, cv2.COLOR_BGRA2BGR).astype(np.uint8)
extraChannel = cv2.cvtColor(extraChannel,
cv2.COLOR_BGRA2BGR).astype(np.uint8)
## convert to grayscale so we can merge into 3-channel image
frameA = cv2.cvtColor(frameA, cv2.COLOR_BGR2GRAY)
frameB = cv2.cvtColor(frameB, cv2.COLOR_BGR2GRAY)
extraChannel = cv2.cvtColor(extraChannel, cv2.COLOR_BGR2GRAY)
## merge, show and write
merged = cv2.merge((extraChannel, frameB, frameA))
cv2.imshow('merged', merged)
out.write(merged)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
break
capA.release()
capB.release()
out.release()
cv2.destroyAllWindows()
print("done!")
def apply_scan_filter(
image: Image,
texture: Image,
position: Tuple[int, int],
limit: int=10,
opacity: float=0.7) -> Image:
"""
Merge a list of images into a gif.
Parameters
----------
image: Image.
A list of image objects, one for each frame.
Returns
-------
out: None.
"""
__x, __y = position
__wi, __hi = image.size
__wt, __ht = texture.size
# add a random move_around as noise
# to simulate the imperfect alignment of a picture in a scanner
__dx, __dy = move_around(
x=__x,
y=__y,
w1=__wi,
h1=__hi,
w2=__wt,
h2=__ht,
limit=limit) # don't move more than 10 pixels along each axis
# crop the texture to match the input image size
__mask = texture.crop((
__x + __dx,
__y + __dy,
__x + __dx + __wi,
__y + __dy + __hi))
# blend the mask and the input image
__filtered = pil.fromarray(np.uint8(difference(
np.array(image).astype(float),
np.array(__mask).astype(float),
opacity=opacity)))
# sharpen the result
return ops.invert(ops.invert(__filtered.convert('RGB')).filter(fil.MaxFilter(3))).convert('RGBA')
# imgOut = blend_modes.darken_only(imgA, imgB, 1.0) # imgOut = blend_modes.multiply(imgA, imgB, 1.0) # imgOut = blend_modes.hard_light(imgA, imgB, 0.9) # imgOut = blend_modes.difference(imgA, imgB, 1.0) # imgOut = blend_modes.subtract(imgA, imgB, 1.0) # imgOut = blend_modes.grain_extract(imgA, imgB, 1.0) # imgOut = blend_modes.divide(imgA, imgB, 1.0) ## # imgOut = blend_modes.grain_merge(imgA, imgB, 1.0) # imgOut = blend_modes.grain_merge(imgOut, imgB, 1.0) ## # imgOut = blend_modes.difference(imgA, imgB, 1.0) # imgOut = blend_modes.overlay(imgOut, imgB, 1.0) ## # imgOut = blend_modes.overlay(imgA, imgB, 1.0) imgOut = blend_modes.difference(imgA, imgB, 0.1) imgOut = blend_modes.overlay(imgOut, imgB, 1.0) imgOut = blend_modes.overlay(imgOut, imgB, 1.0) # imgOut = blend_modes.overlay(imgOut, imgB, 1.0) # imgOut = blend_modes.overlay(imgOut, imgB, 1.0) # imgOut = blend_modes.overlay(imgOut, imgB, 1.0) # imgOut = blend_modes.overlay(imgOut, imgB, 1.0) ## # imgOut = blend_modes.overlay(imgOut, imgB, 0.5) # imgOut = blend_modes.multiply(imgOut, imgB, 1.0) # imgOut = blend_modes.dodge(imgOut, imgB, 0.8) # imgOut = blend_modes.addition(imgOut, imgB, 1.0) # imgOut = blend_modes.soft_light(imgOut, imgB, 1.0) # imgOut = blend_modes.hard_light(imgOut, imgB, 1.0) ## # Save images