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2021-129
Commit 4e368534 authored by dilshan-98's avatar dilshan-98
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Backend/Blur.py
View file @ 4e368534
import cv2 import cv2
import numpy as np
import os import os
import sys
from mrcnn import utils
from mrcnn import visualize
from mrcnn.visualize import display_images
from mrcnn.visualize import display_instances
import mrcnn.model as modellib import mrcnn.model as modellib
from mrcnn.model import log
from mrcnn.config import Config from mrcnn.config import Config
from mrcnn import model as modellib, utils import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import PIL def Blur():
from PIL import Image input = 'static/theimage.jpg'
image = cv2.imread(input)
# Load the pre-trained model data intent = 15
MODEL_DIR = os.path.join("logs")
COCO_MODEL_PATH = 'D:/research -app/2021-129/Backend/mask_rcnn_object_0010.h5'
class CustomConfig(Config): dimensions = image.shape
"""Configuration for training on the custom dataset.
Derives from the base Config class and overrides some values.
"""
# Give the configuration a recognizable name
NAME = "object"
IMAGES_PER_GPU = 1 # blurredImage = gaussianBlur(image, intent)
blurredImage = cv2.GaussianBlur(image, (intent, intent), 0)
NUM_CLASSES = 1 + 3 # Background + labels def convolution(inputimage, kernel):
kernelHeight = kernel.shape[0]
kernelWidth = kernel.shape[1]
# Number of training steps per epoch if(len(inputimage.shape) == 3):
STEPS_PER_EPOCH = 10 imagePadding = np.pad(inputimage, paddingWidth=((kernelHeight // 2, kernelHeight // 2),
(kernelWidth // 2, kernelWidth // 2),
(0, 0)),
mode='constant', valueConstant=0).astype(np.float32)
elif(len(inputimage.shape) == 2):
imagePadding = np.pad(inputimage, paddingWidth=((kernelHeight // 2, kernelHeight // 2),
(kernelWidth // 2, kernelWidth // 2)),
mode='constant', valueConstant=0).astype(np.float32)
# Skip detections with < 90% confidence height = kernelHeight // 2
DETECTION_MIN_CONFIDENCE = 0.9 width = kernelWidth // 2
config = CustomConfig() imageConvolution = np.zeros(imagePadding.shape)
# Change the config infermation for i in range(height, imagePadding.shape[0]-height):
class InferenceConfig(config.__class__): for j in range(width, imagePadding.shape[1]-width):
GPU_COUNT = 1 x = imagePadding[i-height:i-height+kernelHeight, j-width:j-width+kernelWidth]
x = x.flatten()*kernel.flatten()
imageConvolution[i][j] = x.sum()
# Number of images to train with on each GPU. A 12GB GPU can typically heightmax = -height
# handle 2 images of 1024x1024px. widthmax = -width
# Adjust based on your GPU memory and image sizes. Use the highest
# number that your GPU can handle for best performance.
IMAGES_PER_GPU = 1
config = InferenceConfig() if(height == 0):
return imageConvolution[height:, width:widthmax]
if(width == 0):
return imageConvolution[height:heightmax, width:]
# COCO dataset object names return imageConvolution[height:heightmax, width:widthmax]
model = modellib.MaskRCNN(
mode="inference", model_dir=MODEL_DIR, config=config
)
model.load_weights(COCO_MODEL_PATH, by_name=True)
class_names = ['BG', 'portrait', 'portrait_body', 'selfie_top']
def gaussianBlur(image, sigma):
image = np.asarray(image)
def Blur(): filter = 2 * int(4 * sigma + 0.5) + 1
# Input the original image name gaussianFilter = np.zeros((filter, filter), np.float32)
original_image = 'static/theimage.jpg'
# Use OpenCV to read the original image f1 = filter//2
image = cv2.imread(original_image) f2 = filter//2
intent= 15
dimensions = image.shape for x in range(-f1, f1+1):
blur = cv2.GaussianBlur(image ,(intent,intent),0) for y in range(-f2, f2+1):
x1 = 2 * np.pi * (sigma ** 2)
x2 = np.exp(-(x ** 2 + y ** 2)/(2 * sigma ** 2))
gaussianFilter[x + f1, y + f2] = (1/x1) * x2
filterImage = np.zeros_like(image, dtype=np.float32)
for c in range(3):
filterImage[:, :, c] = convolution(image[:, :, c], gaussianFilter)
def apply_mask(image, mask): return (filterImage.astype(np.uint8))
blur[:, :, 0] = np.where(
mainFilePath = os.path.join("D:/research -app/2021-129/Backend/")
modelFilePath = os.path.join(mainFilePath, "logs/")
trainedModelPath = 'D:/research -app/2021-129/Backend/mask_rcnn_object_0010.h5'
class InferenceConfig(Config):
NAME = "object"
NUM_CLASSES = 1 + 3
DETECTION_MIN_CONFIDENCE = 0.5
GPU_COUNT = 1
IMAGES_PER_GPU = 1
config = InferenceConfig()
model = modellib.MaskRCNN(mode="inference", model_dir=modelFilePath, config=config)
model.load_weights(trainedModelPath, by_name=True)
classes = ['BG', 'portrait', 'portrait_body', 'selfie_top']
def applyBlur(image, mask):
blurredImage[:, :, 0] = np.where(
mask == 0, mask == 0,
blur[:, :,0], blurredImage[:, :, 0],
image[:, :, 0] image[:, :, 0]
) )
blur[:, :, 1] = np.where( blurredImage[:, :, 1] = np.where(
mask == 0, mask == 0,
blur[:, :,1], blurredImage[:, :, 1],
image[:, :, 1] image[:, :, 1]
) )
blur[:, :, 2] = np.where( blurredImage[:, :, 2] = np.where(
mask == 0, mask == 0,
blur[:, :,2], blurredImage[:, :, 2],
image[:, :, 2] image[:, :, 2]
) )
return blur return blurredImage
# This function is used to show the object detection result in original image. def applyBlur(image, boxes, masks, class_ids, classes, scores, viewMask=True):
def display_instances(image, boxes, masks, ids, names, scores): instances = boxes.shape[0]
# max_area will save the largest object for all the detection results
max_area = 0 if not instances:
print("No instances available!!!")
# n_instances saves the amount of all objects maskedImage = image.astype(np.uint32).copy()
n_instances = boxes.shape[0]
if not n_instances: for i in range(instances):
print('NO INSTANCES TO DISPLAY')
else:
assert boxes.shape[0] == masks.shape[-1] == ids.shape[0]
for i in range(n_instances):
if not np.any(boxes[i]):
continue
# compute the square of each object
y1, x1, y2, x2 = boxes[i]
square = (y2 - y1) * (x2 - x1)
# use label to select person object from all the 80 classes in COCO dataset
label = names[ids[i]]
if label == 'selfie_top' or label == 'portrait_body' or label == 'portrait':
# save the largest object in the image as main character
# other people will be regarded as background
if square > max_area:
max_area = square
mask = masks[:, :, i] mask = masks[:, :, i]
else: if viewMask:
continue maskedImage = applyBlur(maskedImage, mask)
else:
continue
# apply mask for the image return maskedImage
save1 = apply_mask(image, mask)
return save1 detectionResults = model.detect([image], verbose=0)
r = detectionResults[0]
# run mask rcnn model output = applyBlur(image, r['rois'], r['masks'], r['class_ids'], classes, r['scores'])
results = model.detect([image], verbose=0)
r = results[0]
# applying our effect # output = cv2.resize(np.float32(output), (466, 700))
frame1 = display_instances( cv2.imwrite('static/blur.jpg', output)
image, r['rois'], r['masks'], r['class_ids'], class_names, r['scores']
)
frame1 = cv2.resize(frame1, (466, 700))
cv2.imwrite('static/blur.jpg', frame1)
return "blurdone" return "blurdone"
Backend/Cut.py
View file @ 4e368534
...@@ -17,52 +17,36 @@ import PIL ...@@ -17,52 +17,36 @@ import PIL
from PIL import Image from PIL import Image
def cutImage(): def cutImage():
# Input the original image name # Input the original image name
# original_image = 'samples/ex7.jpg'
original_image = 'static/theimage.jpg' original_image = 'static/theimage.jpg'
# Use OpenCV to read the original image # Use OpenCV to read the original image
image = cv2.imread(original_image) image = cv2.imread(original_image)
image = cv2.resize(image, (466, 700)) height, width, channels = image.shape
print("Read original image successfully! The original image shape is:") print("Read original image successfully! The original image shape is:")
print(image.shape) print(image.shape)
dimensions = (width, height)
# blur = cv2.GaussianBlur(image ,(intent,intent),0)
bg = cv2.imread('static/bg.jpg') bg = cv2.imread('static/bg.jpg')
bg = cv2.resize(bg, (466, 700)) bg = cv2.resize(bg, dimensions, interpolation = cv2.INTER_AREA)
# blur1 = cv2.GaussianBlur(image ,(intent,intent),0)
# cv2.imwrite('original_image.jpg', bg)
# Load the pre-trained model data # Load the pre-trained model data
ROOT_DIR = "" ROOT_DIR = os.path.join("D:/research -app/2021-129/Backend/")
MODEL_DIR = os.path.join(ROOT_DIR, "logs") MODEL_DIR = os.path.join(ROOT_DIR, "logs")
COCO_MODEL_PATH = 'D:/research -app/2021-129/Backend/mask_rcnn_object_0010.h5' customModel = 'D:/research -app/2021-129/Backend/mask_rcnn_object_0010.h5'
class CustomConfig(Config): class InferenceConfig(Config):
"""Configuration for training on the custom dataset.
Derives from the base Config class and overrides some values.
"""
# Give the configuration a recognizable name
NAME = "object" NAME = "object"
GPU_COUNT = 1
IMAGES_PER_GPU = 1 IMAGES_PER_GPU = 1
NUM_CLASSES = 1 + 3 #Background and the total labels of the dataset
NUM_CLASSES = 1 + 3 # Background + labels
# Number of training steps per epoch
STEPS_PER_EPOCH = 10
# Skip detections with < 90% confidence
DETECTION_MIN_CONFIDENCE = 0.9 DETECTION_MIN_CONFIDENCE = 0.9
config = CustomConfig()
# Change the config infermation
class InferenceConfig(config.__class__):
GPU_COUNT = 1
# Number of images to train with on each GPU. A 12GB GPU can typically
# handle 2 images of 1024x1024px.
# Adjust based on your GPU memory and image sizes. Use the highest
# number that your GPU can handle for best performance.
IMAGES_PER_GPU = 1 IMAGES_PER_GPU = 1
config = InferenceConfig() config = InferenceConfig()
...@@ -71,34 +55,46 @@ def cutImage(): ...@@ -71,34 +55,46 @@ def cutImage():
model = modellib.MaskRCNN( model = modellib.MaskRCNN(
mode="inference", model_dir=MODEL_DIR, config=config mode="inference", model_dir=MODEL_DIR, config=config
) )
model.load_weights(COCO_MODEL_PATH, by_name=True) model.load_weights(customModel, by_name=True)
class_names = ['BG', 'portrait', 'portrait_body', 'selfie_top'] class_names = ['BG', 'portrait', 'portrait_body', 'selfie_top']
def apply_mask(image, mask): def apply_mask(image, mask):
bg[:, :, 0] = np.where( bg[:, :, 0] = np.where(
mask == 0, mask == 0,
bg[:, :,0], bg[:, :, 0],
image[:, :, 0] image[:, :, 0]
) )
bg[:, :, 1] = np.where( bg[:, :, 1] = np.where(
mask == 0, mask == 0,
bg[:, :,1], bg[:, :, 1],
image[:, :, 1] image[:, :, 1]
) )
bg[:, :, 2] = np.where( bg[:, :, 2] = np.where(
mask == 0, mask == 0,
bg[:, :,2], bg[:, :, 2],
image[:, :, 2] image[:, :, 2]
) )
return bg return bg
# This function is used to show the object detection result in original image. # This function is used to show the object detection result in original image.
def display_instances(image, boxes, masks, ids, names, scores): def display_instances(image, boxes, masks, ids, names, scores, show_mask=True):
# max_area will save the largest object for all the detection results # max_area will save the largest object for all the detection results
instances = boxes.shape[0]
if not instances:
print('There are no instances available to display!!!')
maskedImage = image.astype(np.uint32).copy()
for i in range(instances):
mask = masks[:, :, i]
if show_mask:
maskedImage = apply_mask(maskedImage, mask)
return maskedImage
max_area = 0 """max_area = 0
# n_instances saves the amount of all objects # n_instances saves the amount of all objects
n_instances = boxes.shape[0] n_instances = boxes.shape[0]
...@@ -131,18 +127,20 @@ def cutImage(): ...@@ -131,18 +127,20 @@ def cutImage():
# apply mask for the image # apply mask for the image
save1 = apply_mask(image, mask) save1 = apply_mask(image, mask)
# save2 = apply_mask2(image, mask)
return save1 return save1 # ,save2"""
# run mask rcnn model
results = model.detect([image], verbose=0) results = model.detect([image], verbose=0)
r = results[0] r = results[0]
# applying our effect ##applying our effect
frame1 = display_instances( frame1 = display_instances(
image, r['rois'], r['masks'], r['class_ids'], class_names, r['scores'] image, r['rois'], r['masks'], r['class_ids'], class_names, r['scores']
) )
cv2.imwrite("static/cut.jpg",frame1) ## save image
return "cutdone"
cv2.imwrite("static/cut.jpg", frame1)
return "done"
Backend/MedBlur.py 0 → 100644
View file @ 4e368534
import cv2
import numpy as np
import os
import sys
import mrcnn.model as modellib
from mrcnn.model import log
from mrcnn.config import Config
import PIL
from PIL import Image
from matplotlib.pyplot import imread
import scipy as sc
from scipy import ndimage
from skimage import filters
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import math
from skimage.color import rgb2gray
rootLocation = os.path.join("D:/research -app/2021-129/Backend/")
modelLocation = os.path.join(rootLocation, "logs/")
customModel = 'D:/research -app/2021-129/Backend/mask_rcnn_object_0010.h5'
class InferenceConfig(Config):
NAME = "object"
GPU_COUNT = 1
IMAGES_PER_GPU = 1
NUM_CLASSES = 1 + 3 #Background and the total labels of the dataset
DETECTION_MIN_CONFIDENCE = 0.9
IMAGES_PER_GPU = 1
config = InferenceConfig()
model = modellib.MaskRCNN(mode = "inference", model_dir = modelLocation, config = config)
model.load_weights(customModel, by_name=True)
class_names = ['BG', 'portrait', 'portrait_body', 'selfie_top']
def medBlur():
def apply_mask(image, mask):
blurredImage[:, :, 0] = np.where(
mask == 0,
blurredImage[:, :, 0],
image[:, :, 0]
)
blurredImage[:, :, 1] = np.where(
mask == 0,
blurredImage[:, :, 1],
image[:, :, 1]
)
blurredImage[:, :, 2] = np.where(
mask == 0,
blurredImage[:, :, 2],
image[:, :, 2]
)
return blurredImage
def applyBlur(image, boxes, masks, show_mask=True):
instances = boxes.shape[0]
if not instances:
print('There are no instances available to display!!!')
maskedImage = image.astype(np.uint32).copy()
for i in range(instances):
mask = masks[:, :, i]
if show_mask:
maskedImage = apply_mask(maskedImage, mask)
return maskedImage
original_image = 'static/theimage.jpg'
image = cv2.imread(original_image)
intent= 15
blurredImage = cv2.medianBlur(image, intent)
results = model.detect([image], verbose=0)
r = results[0]
output = applyBlur(image, r['rois'], r['masks'])
#output = cv2.resize(output, (466, 700))
cv2.imwrite('static/medblur.jpg', output)
return "blurdone"
\ No newline at end of file
Backend/Splash.py
View file @ 4e368534
...@@ -16,50 +16,26 @@ import matplotlib.pyplot as plt ...@@ -16,50 +16,26 @@ import matplotlib.pyplot as plt
import matplotlib.image as mpimg import matplotlib.image as mpimg
def Splash(): def Splash():
# Input the original image name
original_image = 'static/theimage.jpg' original_image = 'static/theimage.jpg'
# Use OpenCV to read and show the original image
image = cv2.imread(original_image) image = cv2.imread(original_image)
# Use cvtColor to accomplish image transformation from RGB image to gray image
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Root directory of the project ROOT_DIR = os.path.join("D:/research -app/2021-129/Backend/")
ROOT_DIR = ""
DEFAULT_LOGS_DIR = os.path.join(ROOT_DIR, "logs")
MODEL_DIR = os.path.join(ROOT_DIR, "logs") MODEL_DIR = os.path.join(ROOT_DIR, "logs")
WEIGHTS_PATH = 'D:/research -app/2021-129/Backend/mask_rcnn_object_0010.h5'
WEIGHTS_PATH = "D:/research -app/2021-129/Backend/mask_rcnn_object_0010.h5" class InferenceConfig(Config):
class CustomConfig(Config):
"""Configuration for training on the custom dataset.
Derives from the base Config class and overrides some values.
"""
# Give the configuration a recognizable name
NAME = "object" NAME = "object"
GPU_COUNT = 1
IMAGES_PER_GPU = 1 IMAGES_PER_GPU = 1
NUM_CLASSES = 1 + 3 #Background and the total labels of the dataset
NUM_CLASSES = 1 + 3 # Background + labels
# Number of training steps per epoch
STEPS_PER_EPOCH = 10
# Skip detections with < 90% confidence
DETECTION_MIN_CONFIDENCE = 0.9 DETECTION_MIN_CONFIDENCE = 0.9
config = CustomConfig()
CUSTOM_DIR = os.path.join(ROOT_DIR, "/Dataset/")
class InferenceConfig(config.__class__):
# Run detection on one image at a time
GPU_COUNT = 1
IMAGES_PER_GPU = 1 IMAGES_PER_GPU = 1
DETECTION_MIN_CONFIDENCE = 0.7
config = InferenceConfig() config = InferenceConfig()
...@@ -89,9 +65,23 @@ def Splash(): ...@@ -89,9 +65,23 @@ def Splash():
) )
return image return image
def display_instances(image, boxes, masks, ids, names, scores): def display_instances(image, boxes, masks, ids, names, scores, show_mask=True):
# max_area will save the largest object for all the detection results
max_area = 0 instances = boxes.shape[0]
if not instances:
print('There are no instances available to display!!!')
maskedImage = image.astype(np.uint32).copy()
for i in range(instances):
mask = masks[:, :, i]
if show_mask:
maskedImage = apply_mask(maskedImage, mask)
return maskedImage
"""max_area = 0
# n_instances saves the amount of all objects # n_instances saves the amount of all objects
n_instances = boxes.shape[0] n_instances = boxes.shape[0]
...@@ -125,7 +115,7 @@ def Splash(): ...@@ -125,7 +115,7 @@ def Splash():
image = apply_mask(image, mask) image = apply_mask(image, mask)
return image return image"""
results = model.detect([image], verbose=0) results = model.detect([image], verbose=0)
r = results[0] r = results[0]
...@@ -133,8 +123,11 @@ def Splash(): ...@@ -133,8 +123,11 @@ def Splash():
image, r['rois'], r['masks'], r['class_ids'], class_names, r['scores'] image, r['rois'], r['masks'], r['class_ids'], class_names, r['scores']
) )
frame = cv2.resize(frame, (466, 700)) frame = cv2.resize(np.float32(frame), (466, 700))
cv2.imwrite('static/splash.jpg', frame) cv2.imwrite('static/splash.jpg', frame)
return "splashdone" return "splashdone"
Backend/Vignette.py 0 → 100644
View file @ 4e368534
import numpy as np
import cv2
def vignette():
input_image = 'static/theimage.jpg'
input_image = cv2.imread(input_image)
rows, cols = input_image.shape[:2]
X_resultant_kernel = cv2.getGaussianKernel(cols,200)
Y_resultant_kernel = cv2.getGaussianKernel(rows,200)
resultant_kernel = Y_resultant_kernel * X_resultant_kernel.T
mask = 255 * resultant_kernel / np.linalg.norm(resultant_kernel)
output = np.copy(input_image)
for i in range(3):
output[:, :, i] = output[:,:,i] * mask
cv2.imwrite('static/vignette.jpg', output)
return "vignettedone"
\ No newline at end of file
Backend/api.py
View file @ 4e368534
...@@ -4,6 +4,9 @@ from Cut import cutImage ...@@ -4,6 +4,9 @@ from Cut import cutImage
from Splash import Splash from Splash import Splash
from Blur import Blur from Blur import Blur
from maskImage import mask from maskImage import mask
from bokeh import Bokeh
from MedBlur import medBlur
from Vignette import vignette
import os import os
import tensorflow as tf import tensorflow as tf
from flask_ngrok import run_with_ngrok from flask_ngrok import run_with_ngrok
...@@ -72,23 +75,32 @@ def blurGet(): ...@@ -72,23 +75,32 @@ def blurGet():
return "done" return "done"
# @app.route('/bokeh', methods = ['GET']) @app.route('/bokeh', methods = ['GET'])
# @cross_origin() @cross_origin()
# def bokehGet(): def bokehGet():
# global graph global graph
# with graph.as_default(): with graph.as_default():
# Bokeh() Bokeh()
# return "done" return "done"
@app.route('/vignette', methods = ['GET'])
@cross_origin()
def vignetteGet():
global graph
with graph.as_default():
vignette()
return "done"
# @app.route('/medBlur', methods = ['GET']) @app.route('/medblur', methods = ['GET'])
# @cross_origin() @cross_origin()
# def medBlurGet(): def medblurGet():
# global graph global graph
# with graph.as_default(): with graph.as_default():
# MedBlur() medBlur()
# return "done" return "done"
if __name__ == '__main__': if __name__ == '__main__':
app.run(debug=True) app.run()
\ No newline at end of file \ No newline at end of file
Backend/bokeh.py 0 → 100644
View file @ 4e368534
import cv2
import os
import mrcnn.model as modellib
from mrcnn.config import Config
import matplotlib.pyplot as plt
import numpy as np
def Bokeh():
input = 'static/theimage.jpg'
image = cv2.imread(input)
plt.rcParams["figure.figsize"]= (10,10)
np.set_printoptions(precision=3)
Array = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],[0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0],[0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0],[0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],[0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],[0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0],[0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0],[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],], dtype='float')
binaryMask = Array
gaussianKernel = cv2.getGaussianKernel(11, 5.)
convoKernel = gaussianKernel*gaussianKernel.transpose()*binaryMask
kernel = convoKernel / np.sum(convoKernel)
print(kernel)
def bokeh(image):
r,g,b = cv2.split(image)
r = r / 255.
r = np.where(r > 0.9, r * 2, r)
filteredR = cv2.filter2D(r, -1, kernel)
filteredR = np.where(filteredR > 1., 1., filteredR)
g = g / 255.
g = np.where(g > 0.9, g * 2, g)
filteredG = cv2.filter2D(g, -1, kernel)
filteredG = np.where(filteredG > 1., 1., filteredG)
b = b / 255.
b = np.where(b > 0.9, b * 2, b)
filteredB = cv2.filter2D(b, -1, kernel)
filteredB = np.where(filteredB > 1., 1., filteredB)
result = cv2.merge((filteredR, filteredG, filteredB))
return result
imageRGB = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
imageRGB = imageRGB / 255.
mainFilePath = os.path.join("D:/research -app/2021-129/Backend/")
modelFilePath = os.path.join(mainFilePath, "logs/")
trainedModelPath = 'D:/research -app/2021-129/Backend/mask_rcnn_object_0010.h5'
class InferenceConfig(Config):
NAME = "object"
NUM_CLASSES = 1 + 3
DETECTION_MIN_CONFIDENCE = 0.5
GPU_COUNT = 1
IMAGES_PER_GPU = 1
config = InferenceConfig()
model = modellib.MaskRCNN(mode="inference", model_dir=modelFilePath, config=config)
model.load_weights(trainedModelPath, by_name=True)
classes = ['BG', 'portrait', 'portrait_body', 'selfie_top']
def applyBokeh(imageInput, boxes, masks, class_ids, classes, scores, show_mask=True):
instances = boxes.shape[0]
if not instances:
print("No instances available!!!")
for i in range(instances):
mask = masks[:, :, i]
invertImage = np.abs(1. - mask)
r,g,b = cv2.split(imageInput)
maskedR = r * mask
maskedG = g * mask
maskedB = b * mask
mergedImage = cv2.merge((maskedR, maskedG, maskedB))
invertR = r * invertImage
invertG = g * invertImage
invertB = b * invertImage
backgroundMerged = cv2.merge((invertR, invertG, invertB))
mergedImage = np.asarray(mergedImage * 255., dtype='uint8')
backgroundMerged = np.asarray(backgroundMerged * 255., dtype='uint8')
backgroundBokeh = bokeh(backgroundMerged)
backgroundBokeh = np.asarray(backgroundBokeh * 255., dtype='uint8')
maskedImage = cv2.add(mergedImage, backgroundBokeh)
maskedImage = cv2.cvtColor(maskedImage, cv2.COLOR_BGR2RGB)
return maskedImage
detectionResults = model.detect([image], verbose=0)
r = detectionResults[0]
output = applyBokeh(imageRGB, r['rois'], r['masks'], r['class_ids'], classes, r['scores'])
cv2.imwrite('static/bokeh.jpg', output)
return "bokehdone"
\ No newline at end of file
Backend/maskImage.py
View file @ 4e368534
...@@ -25,60 +25,41 @@ from mrcnn.model import log ...@@ -25,60 +25,41 @@ from mrcnn.model import log
from mrcnn.config import Config from mrcnn.config import Config
from mrcnn import model as modellib, utils from mrcnn import model as modellib, utils
# Root directory of the project
ROOT_DIR = "samples"
DEFAULT_LOGS_DIR = os.path.join(ROOT_DIR, "logs")
MODEL_DIR = os.path.join(ROOT_DIR, "logs")
WEIGHTS_PATH = "D:/research -app/2021-129/Backend/mask_rcnn_object_0010.h5"
class CustomConfig(Config): def mask():
"""Configuration for training on the custom dataset.
Derives from the base Config class and overrides some values.
"""
# Give the configuration a recognizable name
NAME = "object"
IMAGES_PER_GPU = 1
NUM_CLASSES = 1 + 3 # Background + labels
# Number of training steps per epoch path_to_new_image = 'static/theimage.jpg'
STEPS_PER_EPOCH = 10 #image1 = mpimg.imread(path_to_new_image)
image1 = cv2.imread(path_to_new_image)
# Skip detections with < 90% confidence ROOT_DIR = os.path.join("D:/research -app/2021-129/Backend/")
DETECTION_MIN_CONFIDENCE = 0.9 MODEL_DIR = os.path.join(ROOT_DIR, "logs")
WEIGHTS_PATH = 'D:/research -app/2021-129/Backend/mask_rcnn_object_0010.h5'
config = CustomConfig()
class InferenceConfig(config.__class__): class InferenceConfig(Config):
# Run detection on one image at a time NAME = "object"
GPU_COUNT = 1 GPU_COUNT = 1
IMAGES_PER_GPU = 1 IMAGES_PER_GPU = 1
DETECTION_MIN_CONFIDENCE = 0.7 NUM_CLASSES = 1 + 3 #Background and the total labels of the dataset
DETECTION_MIN_CONFIDENCE = 0.5
IMAGES_PER_GPU = 1
def mask():
config = InferenceConfig() config = InferenceConfig()
config.display() #config.display()
#LOAD MODEL. Create model in inference mode
model = modellib.MaskRCNN(mode="inference", model_dir=MODEL_DIR, config=config) model = modellib.MaskRCNN(mode="inference", model_dir=MODEL_DIR, config=config)
# Load COCO weights Or, load the last model you trained
weights_path = WEIGHTS_PATH weights_path = WEIGHTS_PATH
# Load weights
# print("Loading weights ", weights_path)
model.load_weights(weights_path, by_name=True) model.load_weights(weights_path, by_name=True)
class_names = ['BG', 'portrait','portrait_body', 'selfie_top'] class_names = ['BG', 'portrait', 'portrait_body', 'selfie_top']
# path = sys.argv[1]
path_to_new_image = 'static/theimage.jpg'
# path_to_new_image = path.split("/")[-1]
image1 = mpimg.imread(path_to_new_image)
# plt.imshow(image1)
results1 = model.detect([image1], verbose=1) results1 = model.detect([image1], verbose=1)
...@@ -87,3 +68,4 @@ def mask(): ...@@ -87,3 +68,4 @@ def mask():
class_names, r1['scores'], title="Predictions1") class_names, r1['scores'], title="Predictions1")
return True return True
Backend/maskImage2.py 0 → 100644
View file @ 4e368534
import warnings
warnings.filterwarnings('ignore')
import os
import sys
import json
import datetime
import numpy as np
import skimage.draw
import cv2
import random
import math
import re
import time
import tensorflow as tf
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import matplotlib.image as mpimg
import colorsys
from skimage.measure import find_contours
from matplotlib.patches import Polygon
from mrcnn import utils
from mrcnn import visualize
from mrcnn.visualize import display_images
from mrcnn.visualize import display_instances
import mrcnn.model as modellib
from mrcnn.model import log
from mrcnn.config import Config
from mrcnn import model as modellib, utils
#import custom
# Root directory of the project
ROOT_DIR = ""
DEFAULT_LOGS_DIR = os.path.join(ROOT_DIR, "logs")
MODEL_DIR = os.path.join(ROOT_DIR, "logs")
WEIGHTS_PATH = "mask_rcnn_object_0010.h5"
class CustomConfig(Config):
"""Configuration for training on the custom dataset.
Derives from the base Config class and overrides some values.
"""
# Give the configuration a recognizable name
NAME = "object"
IMAGES_PER_GPU = 1
NUM_CLASSES = 1 + 3 # Background + labels
# Number of training steps per epoch
STEPS_PER_EPOCH = 10
# Skip detections with < 90% confidence
DETECTION_MIN_CONFIDENCE = 0.9
config = CustomConfig()
class InferenceConfig(config.__class__):
# Run detection on one image at a time
GPU_COUNT = 1
IMAGES_PER_GPU = 1
DETECTION_MIN_CONFIDENCE = 0.7
config = InferenceConfig()
config.display()
#LOAD MODEL. Create model in inference mode
model = modellib.MaskRCNN(mode="inference", model_dir=MODEL_DIR, config=config)
# Load COCO weights Or, load the last model you trained
weights_path = WEIGHTS_PATH
# Load weights
# print("Loading weights ", weights_path)
model.load_weights(weights_path, by_name=True)
class_names = ['BG', 'portrait','portrait_body', 'selfie_top']
# path = sys.argv[1]
#path_to_new_image = 'D:/research/maskRcnn/images/portrait demo 1.jpg'
path_to_new_image = path.split("/")[-1]
image1 = mpimg.imread(path_to_new_image)
plt.imshow(image1)
def random_colors(N, bright=True):
"""
Generate random colors.
To get visually distinct colors, generate them in HSV space then
convert to RGB.
"""
brightness = 1.0 if bright else 0.7
hsv = [(i / N, 1, brightness) for i in range(N)]
colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv))
random.shuffle(colors)
return colors
def apply_mask(image, mask, color, alpha=0.5):
"""Apply the given mask to the image.
"""
for c in range(3):
image[:, :, c] = np.where(mask == 1,
image[:, :, c] *
(1 - alpha) + alpha * color[c] * 255,
image[:, :, c])
return image
def display_instances(image, boxes, masks, class_ids, class_names,
scores=None, title="",
figsize=(16, 16), ax=None,
show_mask=True, show_bbox=True,
colors=None, captions=None):
"""
boxes: [num_instance, (y1, x1, y2, x2, class_id)] in image coordinates.
masks: [height, width, num_instances]
class_ids: [num_instances]
class_names: list of class names of the dataset
scores: (optional) confidence scores for each box
title: (optional) Figure title
show_mask, show_bbox: To show masks and bounding boxes or not
figsize: (optional) the size of the image
colors: (optional) An array or colors to use with each object
captions: (optional) A list of strings to use as captions for each object
"""
# Number of instances
N = boxes.shape[0]
if not N:
print("\n*** No instances to display *** \n")
else:
assert boxes.shape[0] == masks.shape[-1] == class_ids.shape[0]
# If no axis is passed, create one and automatically call show()
auto_show = False
if not ax:
_, ax = plt.subplots(1, figsize=figsize)
auto_show = True
# Generate random colors
colors = colors or random_colors(N)
# Show area outside image boundaries.
height, width = image.shape[:2]
ax.set_ylim(height + 10, -10)
ax.set_xlim(-10, width + 10)
ax.axis('off')
ax.set_title(title)
masked_image = image.astype(np.uint32).copy()
for i in range(N):
color = colors[i]
# Bounding box
if not np.any(boxes[i]):
# Skip this instance. Has no bbox. Likely lost in image cropping.
continue
y1, x1, y2, x2 = boxes[i]
if show_bbox:
p = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, linewidth=2,
alpha=0.7, linestyle="dashed",
edgecolor=color, facecolor='none')
ax.add_patch(p)
# Label
if not captions:
class_id = class_ids[i]
score = scores[i] if scores is not None else None
label = class_names[class_id]
caption = "{} {:.3f}".format(label, score) if score else label
else:
caption = captions[i]
ax.text(x1, y1 + 8, caption,
color='w', size=11, backgroundcolor="none")
# Mask
mask = masks[:, :, i]
if show_mask:
masked_image = apply_mask(masked_image, mask, color)
# Mask Polygon
# Pad to ensure proper polygons for masks that touch image edges.
padded_mask = np.zeros(
(mask.shape[0] + 2, mask.shape[1] + 2), dtype=np.uint8)
padded_mask[1:-1, 1:-1] = mask
contours = find_contours(padded_mask, 0.5)
for verts in contours:
# Subtract the padding and flip (y, x) to (x, y)
verts = np.fliplr(verts) - 1
p = Polygon(verts, facecolor="none", edgecolor=color)
ax.add_patch(p)
ax.imshow(masked_image.astype(np.uint8))
if auto_show:
plt.show()
# print(len([image1]))
results1 = model.detect([image1], verbose=1)
r1 = results1[0]
display_instances(image1, r1['rois'], r1['masks'], r1['class_ids'],
class_names, r1['scores'], title="Predictions1")
\ No newline at end of file
Backend/mrcnn/visualize.py
View file @ 4e368534
...@@ -164,7 +164,7 @@ def display_instances(image, boxes, masks, class_ids, class_names, ...@@ -164,7 +164,7 @@ def display_instances(image, boxes, masks, class_ids, class_names,
p = Polygon(verts, facecolor="none", edgecolor=color) p = Polygon(verts, facecolor="none", edgecolor=color)
ax.add_patch(p) ax.add_patch(p)
# ax.imshow(masked_image.astype(np.uint8)) # ax.imshow(masked_image.astype(np.uint8))
cv2.imwrite('masked2.jpg', masked_image.astype(np.uint8)) cv2.imwrite('static/masked.jpg', masked_image.astype(np.uint8))
# if auto_show: # if auto_show:
# plt.show() # plt.show()
......
Backend/static/Blur.py 0 → 100644
View file @ 4e368534
import cv2
import numpy as np
import os
import sys
from mrcnn import utils
from mrcnn import visualize
from mrcnn.visualize import display_images
from mrcnn.visualize import display_instances
import mrcnn.model as modellib
from mrcnn.model import log
from mrcnn.config import Config
from mrcnn import model as modellib, utils
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import PIL
from PIL import Image
MODEL_DIR = os.path.join("logs")
COCO_MODEL_PATH = 'D:/chrome/Y4S2/research/New folder (3)/Dinusha-IT18118346/Backend/mask_rcnn_object_0010.h5'
class CustomConfig(Config):
"""Configuration for training on the custom dataset.
Derives from the base Config class and overrides some values.
"""
# Give the configuration a recognizable name
NAME = "object"
IMAGES_PER_GPU = 1
NUM_CLASSES = 1 + 3 # Background + labels
# Number of training steps per epoch
STEPS_PER_EPOCH = 10
# Skip detections with < 90% confidence
DETECTION_MIN_CONFIDENCE = 0.9
config = CustomConfig()
# Change the config infermation
class InferenceConfig(config.__class__):
GPU_COUNT = 1
# Number of images to train with on each GPU. A 12GB GPU can typically
# handle 2 images of 1024x1024px.
# Adjust based on your GPU memory and image sizes. Use the highest
# number that your GPU can handle for best performance.
IMAGES_PER_GPU = 1
config = InferenceConfig()
# COCO dataset object names
model = modellib.MaskRCNN(
mode="inference", model_dir=MODEL_DIR, config=config
)
model.load_weights(COCO_MODEL_PATH, by_name=True)
class_names = ['BG', 'portrait', 'portrait_body', 'selfie_top']
"""def apply_mask2(image, mask):
blur1[:, :, 0] = np.where(
mask == 0,
image[:, :, 0],
blur1[:, :,0]
)
blur1[:, :, 1] = np.where(
mask == 0,
image[:, :, 1],
blur1[:, :,1]
)
blur1[:, :, 2] = np.where(
mask == 0,
image[:, :, 2],
blur1[:, :,2]
)
return blur1 """
def Blur():
# Input the original image name
original_image = 'static/theimage.jpg'
# Use OpenCV to read the original image
image = cv2.imread(original_image)
intent= 15
dimensions = image.shape
blur = cv2.GaussianBlur(image ,(intent,intent),0)
def apply_mask(image, mask):
blur[:, :, 0] = np.where(
mask == 0,
blur[:, :, 0],
image[:, :, 0]
)
blur[:, :, 1] = np.where(
mask == 0,
blur[:, :, 1],
image[:, :, 1]
)
blur[:, :, 2] = np.where(
mask == 0,
blur[:, :, 2],
image[:, :, 2]
)
return blur
# This function is used to show the object detection result in original image.
def display_instances(image, boxes, masks, ids, names, scores):
# max_area will save the largest object for all the detection results
max_area = 0
# n_instances saves the amount of all objects
n_instances = boxes.shape[0]
if not n_instances:
print('NO INSTANCES TO DISPLAY')
else:
assert boxes.shape[0] == masks.shape[-1] == ids.shape[0]
for i in range(n_instances):
if not np.any(boxes[i]):
continue
# compute the square of each object
y1, x1, y2, x2 = boxes[i]
square = (y2 - y1) * (x2 - x1)
# use label to select person object from all the 80 classes in COCO dataset
label = names[ids[i]]
if label == 'selfie_top' or label == 'portrait_body' or label == 'portrait':
# save the largest object in the image as main character
# other people will be regarded as background
if square > max_area:
max_area = square
mask = masks[:, :, i]
else:
continue
else:
continue
# apply mask for the image
save1 = apply_mask(image, mask)
# save2 = apply_mask2(image, mask)
return save1 # ,save2
## run fast rcnn model
results = model.detect([image], verbose=0)
r = results[0]
##applying our effect
frame1 = display_instances(
image, r['rois'], r['masks'], r['class_ids'], class_names, r['scores']
)
frame1 = cv2.resize(frame1, (466, 700))
cv2.imwrite('static/blur.jpg', frame1)
return "blurdone"
app/build.gradle
View file @ 4e368534
...@@ -47,7 +47,6 @@ dependencies { ...@@ -47,7 +47,6 @@ dependencies {
implementation 'com.github.bumptech.glide:glide:4.11.0' implementation 'com.github.bumptech.glide:glide:4.11.0'
implementation 'org.tensorflow:tensorflow-lite-metadata:0.1.0-rc1' implementation 'org.tensorflow:tensorflow-lite-metadata:0.1.0-rc1'
implementation 'org.tensorflow:tensorflow-lite-gpu:2.2.0' implementation 'org.tensorflow:tensorflow-lite-gpu:2.2.0'
implementation project(path: ':openCVLibrary343')
annotationProcessor 'com.github.bumptech.glide:compiler:4.11.0' annotationProcessor 'com.github.bumptech.glide:compiler:4.11.0'
implementation 'de.hdodenhof:circleimageview:3.1.0' implementation 'de.hdodenhof:circleimageview:3.1.0'
implementation 'com.github.zjywill:roundedcornerimageview:1.1.0' implementation 'com.github.zjywill:roundedcornerimageview:1.1.0'
......
app/src/main/AndroidManifest.xml
View file @ 4e368534
...@@ -19,7 +19,7 @@ ...@@ -19,7 +19,7 @@
<activity android:name=".ImageProcessing.Cut" /> <activity android:name=".ImageProcessing.Cut" />
<activity android:name=".ImageProcessing.Blur" /> <activity android:name=".ImageProcessing.Blur" />
<activity android:name=".ImageProcessing.Selectedit" /> <activity android:name=".ImageProcessing.Selectedit" />
<activity android:name=".ImageProcessing.BackgroundCustomization" /> <!-- <activity android:name=".ImageProcessing.BackgroundCustomization" />-->
<activity android:name=".activities.DenoisingActivity" /> <activity android:name=".activities.DenoisingActivity" />
<activity android:name=".activities.MyGallery" /> <activity android:name=".activities.MyGallery" />
<activity android:name=".activities.CropActivity" /> <activity android:name=".activities.CropActivity" />
...@@ -27,7 +27,7 @@ ...@@ -27,7 +27,7 @@
<activity android:name=".activities.CameraView" /> <activity android:name=".activities.CameraView" />
<activity android:name=".activities.EnvironmentChecker" /> <activity android:name=".activities.EnvironmentChecker" />
<activity <activity
android:name=".activities.SplashScreen" android:name=".ImageProcessing.BackgroundCustomization"
android:screenOrientation="portrait"> android:screenOrientation="portrait">
<intent-filter> <intent-filter>
<action android:name="android.intent.action.MAIN" /> <action android:name="android.intent.action.MAIN" />
......
app/src/main/java/com/app/smartphotoeditor/ImageProcessing/Api.java
View file @ 4e368534
...@@ -29,4 +29,12 @@ public interface Api { ...@@ -29,4 +29,12 @@ public interface Api {
@GET("/cut") @GET("/cut")
Call<ResponseBody> cut(); Call<ResponseBody> cut();
@GET("/bokeh")
Call<ResponseBody> bokeh();
@GET("/vignette")
Call<ResponseBody> vignette();
@GET("/medblur")
Call<ResponseBody> medblur();
} }
app/src/main/java/com/app/smartphotoeditor/ImageProcessing/Bokeh.java 0 → 100644
View file @ 4e368534
package com.app.smartphotoeditor.ImageProcessing;
import android.content.Intent;
import android.os.Bundle;
import android.view.View;
import android.widget.Button;
import android.widget.ImageView;
import androidx.appcompat.app.AppCompatActivity;
import com.app.smartphotoeditor.R;
import com.squareup.picasso.MemoryPolicy;
import com.squareup.picasso.NetworkPolicy;
import com.squareup.picasso.Picasso;
public class Bokeh extends AppCompatActivity {
Button btnBack;
ImageView imgView;
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_bokeh);
btnBack=(Button) findViewById(R.id.back);
imgView=(ImageView) findViewById(R.id.imgView);
Picasso
.get()
.load("https://"+Constants.IMGLINK+".ngrok.io/static/bokeh.jpg")
.placeholder(R.drawable.progress_bar_material)
.networkPolicy(NetworkPolicy.NO_CACHE)
.memoryPolicy(MemoryPolicy.NO_CACHE)
.resize(300,300)
.into(imgView);
btnBack.setOnClickListener(new View.OnClickListener() {
@Override
public void onClick(View view) {
openSelecteditAcitivity();
}
});
}
public void openSelecteditAcitivity() {
Intent intent = new Intent(this, Selectedit.class);
startActivity(intent);
}
}
\ No newline at end of file
app/src/main/java/com/app/smartphotoeditor/ImageProcessing/Constants.java
View file @ 4e368534
...@@ -3,6 +3,6 @@ package com.app.smartphotoeditor.ImageProcessing; ...@@ -3,6 +3,6 @@ package com.app.smartphotoeditor.ImageProcessing;
public class Constants { public class Constants {
// This should be replaced with the server link when server starts running // This should be replaced with the server link when server starts running
public static final String IMGLINK = ""; public static final String IMGLINK = "4e11-112-134-169-32";
} }
app/src/main/java/com/app/smartphotoeditor/ImageProcessing/Cut.java
View file @ 4e368534
package com.app.smartphotoeditor.ImageProcessing; package com.app.smartphotoeditor.ImageProcessing;
import androidx.appcompat.app.AppCompatActivity;
import android.content.Intent; import android.content.Intent;
import android.os.Bundle; import android.os.Bundle;
import android.view.View; import android.view.View;
import android.widget.Button; import android.widget.Button;
import android.widget.ImageView; import android.widget.ImageView;
import androidx.appcompat.app.AppCompatActivity;
import com.app.smartphotoeditor.R; import com.app.smartphotoeditor.R;
import com.squareup.picasso.MemoryPolicy; import com.squareup.picasso.MemoryPolicy;
import com.squareup.picasso.NetworkPolicy; import com.squareup.picasso.NetworkPolicy;
......
app/src/main/java/com/app/smartphotoeditor/ImageProcessing/MedBlur.java 0 → 100644
View file @ 4e368534
package com.app.smartphotoeditor.ImageProcessing;
import android.content.Intent;
import android.os.Bundle;
import android.view.View;
import android.widget.Button;
import android.widget.ImageView;
import androidx.appcompat.app.AppCompatActivity;
import com.app.smartphotoeditor.R;
import com.squareup.picasso.MemoryPolicy;
import com.squareup.picasso.NetworkPolicy;
import com.squareup.picasso.Picasso;
public class MedBlur extends AppCompatActivity {
Button btnBack;
ImageView imgView;
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_med_blur);
btnBack=(Button) findViewById(R.id.back);
imgView=(ImageView) findViewById(R.id.imgView);
Picasso
.get()
.load("https://"+Constants.IMGLINK+".ngrok.io/static/medblur.jpg")
.placeholder(R.drawable.progress_bar_material)
.networkPolicy(NetworkPolicy.NO_CACHE)
.memoryPolicy(MemoryPolicy.NO_CACHE)
.resize(300,300)
.into(imgView);
btnBack.setOnClickListener(new View.OnClickListener() {
@Override
public void onClick(View view) {
openSelecteditAcitivity();
}
});
}
public void openSelecteditAcitivity() {
Intent intent = new Intent(this, Selectedit.class);
startActivity(intent);
}
}
\ No newline at end of file
app/src/main/java/com/app/smartphotoeditor/ImageProcessing/RetrofitClient.java
View file @ 4e368534
...@@ -8,7 +8,8 @@ import retrofit2.converter.gson.GsonConverterFactory; ...@@ -8,7 +8,8 @@ import retrofit2.converter.gson.GsonConverterFactory;
public class RetrofitClient { public class RetrofitClient {
private static final String baseurl="http://10.0.2.15:5000"; // private static final String baseurl="http://10.0.2.15:5000";
private static final String baseurl="http://4e11-112-134-169-32.ngrok.io";
private static RetrofitClient mInstance; private static RetrofitClient mInstance;
private Retrofit retrofit; private Retrofit retrofit;
......
app/src/main/java/com/app/smartphotoeditor/ImageProcessing/Vignette.java 0 → 100644
View file @ 4e368534
package com.app.smartphotoeditor.ImageProcessing;
import android.content.Intent;
import android.os.Bundle;
import android.view.View;
import android.widget.Button;
import android.widget.ImageView;
import androidx.appcompat.app.AppCompatActivity;
import com.app.smartphotoeditor.R;
import com.squareup.picasso.MemoryPolicy;
import com.squareup.picasso.NetworkPolicy;
import com.squareup.picasso.Picasso;
public class Vignette extends AppCompatActivity {
Button btnBack;
ImageView imgView;
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_vignette);
btnBack=(Button) findViewById(R.id.back);
imgView=(ImageView) findViewById(R.id.imgView);
Picasso
.get()
.load("https://"+Constants.IMGLINK+".ngrok.io/static/vignette.jpg")
.placeholder(R.drawable.progress_bar_material)
.networkPolicy(NetworkPolicy.NO_CACHE)
.memoryPolicy(MemoryPolicy.NO_CACHE)
.resize(300,300)
.into(imgView);
btnBack.setOnClickListener(new View.OnClickListener() {
@Override
public void onClick(View view) {
openSelecteditAcitivity();
}
});
}
public void openSelecteditAcitivity() {
Intent intent = new Intent(this, Selectedit.class);
startActivity(intent);
}
}
\ No newline at end of file
app/src/main/res/layout/activity_bokeh.xml 0 → 100644
View file @ 4e368534
<?xml version="1.0" encoding="utf-8"?>
<androidx.constraintlayout.widget.ConstraintLayout xmlns:android="http://schemas.android.com/apk/res/android"
xmlns:app="http://schemas.android.com/apk/res-auto"
xmlns:tools="http://schemas.android.com/tools"
android:layout_width="match_parent"
android:layout_height="match_parent"
tools:context=".ImageProcessing.Bokeh">
<ScrollView
android:layout_width="match_parent"
android:layout_height="match_parent">
<LinearLayout
android:layout_width="match_parent"
android:layout_height="wrap_content"
android:orientation="vertical" >
<Button
android:id="@+id/back"
android:layout_width="match_parent"
android:layout_height="wrap_content"
android:text="Back" />
<ImageView
android:id="@+id/imgView"
android:layout_width="match_parent"
android:layout_height="383dp"
tools:srcCompat="@tools:sample/avatars" />
</LinearLayout>
</ScrollView>
</androidx.constraintlayout.widget.ConstraintLayout>
\ No newline at end of file
app/src/main/res/layout/activity_med_blur.xml 0 → 100644
View file @ 4e368534
<?xml version="1.0" encoding="utf-8"?>
<androidx.constraintlayout.widget.ConstraintLayout xmlns:android="http://schemas.android.com/apk/res/android"
xmlns:app="http://schemas.android.com/apk/res-auto"
xmlns:tools="http://schemas.android.com/tools"
android:layout_width="match_parent"
android:layout_height="match_parent"
tools:context=".ImageProcessing.MedBlur">
<ScrollView
android:layout_width="match_parent"
android:layout_height="match_parent">
<LinearLayout
android:layout_width="match_parent"
android:layout_height="wrap_content"
android:orientation="vertical" >
<Button
android:id="@+id/back"
android:layout_width="match_parent"
android:layout_height="wrap_content"
android:text="Back" />
<ImageView
android:id="@+id/imgView"
android:layout_width="match_parent"
android:layout_height="383dp"
tools:srcCompat="@tools:sample/avatars" />
</LinearLayout>
</ScrollView>
</androidx.constraintlayout.widget.ConstraintLayout>
\ No newline at end of file
app/src/main/res/layout/activity_selectedit.xml
View file @ 4e368534
...@@ -11,63 +11,122 @@ ...@@ -11,63 +11,122 @@
android:layout_height="match_parent" android:layout_height="match_parent"
> >
<LinearLayout
android:layout_width="match_parent"
android:layout_height="match_parent"
android:orientation="vertical">
<Button
android:id="@+id/newimage"
android:layout_width="match_parent"
android:layout_height="wrap_content"
android:layout_marginLeft="20dp"
android:layout_marginRight="20dp"
android:text="New Image" />
<LinearLayout <LinearLayout
android:layout_width="match_parent" android:layout_width="match_parent"
android:layout_height="wrap_content" android:layout_height="wrap_content"
android:orientation="horizontal" android:orientation="horizontal"
android:layout_marginTop="80dp" android:layout_marginRight="10dp"
> android:layout_marginLeft="10dp">
<TableLayout
android:layout_width="match_parent"
android:layout_height="wrap_content">
<TableRow
android:id="@+id/tableRow1"
android:layout_width="wrap_content"
android:layout_height="0dp"
android:layout_weight="1" >
<Button <Button
android:id="@+id/blur" android:id="@+id/blur"
android:layout_width="wrap_content" android:layout_width="0dp"
android:layout_height="wrap_content" android:layout_height="wrap_content"
android:layout_weight="1" android:layout_weight="1"
android:layout_marginLeft="20dp" android:layout_marginLeft="20dp"
android:layout_marginRight="20dp"
android:text="Blur" /> android:layout_gravity="center"
android:text="Blur"/>
<Button <Button
android:id="@+id/cut" android:id="@+id/cut"
android:layout_width="wrap_content" android:layout_width="0dp"
android:layout_height="wrap_content" android:layout_height="wrap_content"
android:layout_weight="1" android:layout_weight="1"
android:layout_marginLeft="20dp" android:layout_marginLeft="20dp"
android:text="Cut" /> android:layout_marginRight="20dp"
android:layout_gravity="center"
android:text="Cut"/>
</TableRow>
<TableRow
android:id="@+id/tableRow2"
android:layout_width="wrap_content"
android:layout_height="0dp"
android:layout_weight="1" >
<Button <Button
android:id="@+id/splash" android:id="@+id/splash"
android:layout_width="wrap_content" android:layout_width="0dp"
android:layout_height="wrap_content" android:layout_height="wrap_content"
android:layout_gravity="center"
android:layout_weight="1" android:layout_weight="1"
android:layout_marginLeft="20dp" android:layout_marginLeft="20dp"
android:layout_marginRight="20dp" android:layout_marginRight="20dp"
android:text="Splash" /> android:text="Splash" />
</LinearLayout> <Button
</ScrollView> android:id="@+id/bokeh"
android:layout_width="0dp"
<LinearLayout android:layout_height="wrap_content"
android:layout_width="match_parent" android:layout_marginLeft="20dp"
android:layout_height="match_parent" android:layout_marginRight="20dp"
android:orientation="vertical"> android:layout_weight="1"
android:layout_gravity="center"
android:text="Bokeh" />
</TableRow>
<TableRow
android:id="@+id/tableRow3"
android:layout_width="wrap_content"
android:layout_height="0dp"
android:layout_weight="1" >
<Button <Button
android:id="@+id/newimage" android:id="@+id/medblur"
android:layout_width="match_parent" android:layout_width="0dp"
android:layout_height="wrap_content" android:layout_height="wrap_content"
android:layout_weight="1"
android:layout_marginLeft="20dp" android:layout_marginLeft="20dp"
android:layout_marginRight="20dp" android:layout_marginRight="20dp"
android:text="New Image" /> android:layout_gravity="center"
android:text="Medium Blur"
/>
<Button
android:id="@+id/vignette"
android:layout_width="0dp"
android:layout_height="wrap_content"
android:layout_weight="1"
android:layout_marginLeft="20dp"
android:layout_marginRight="20dp"
android:layout_gravity="center"
android:text="vignette"/>
</TableRow>
</TableLayout>
</LinearLayout>
<ImageView <ImageView
android:adjustViewBounds="true"
android:layout_marginTop="125dp"
android:id="@+id/imgview" android:id="@+id/imgview"
android:layout_width="match_parent" android:layout_width="match_parent"
android:layout_height="wrap_content" android:layout_height="wrap_content"
android:layout_marginTop="20dp"
android:adjustViewBounds="true"
app:srcCompat="@drawable/ic_launcher_background" /> app:srcCompat="@drawable/ic_launcher_background" />
</LinearLayout> </LinearLayout>
</ScrollView>
</androidx.constraintlayout.widget.ConstraintLayout> </androidx.constraintlayout.widget.ConstraintLayout>
\ No newline at end of file
app/src/main/res/layout/activity_vignette.xml 0 → 100644
View file @ 4e368534
<?xml version="1.0" encoding="utf-8"?>
<androidx.constraintlayout.widget.ConstraintLayout xmlns:android="http://schemas.android.com/apk/res/android"
xmlns:app="http://schemas.android.com/apk/res-auto"
xmlns:tools="http://schemas.android.com/tools"
android:layout_width="match_parent"
android:layout_height="match_parent"
tools:context=".ImageProcessing.Vignette">
<ScrollView
android:layout_width="match_parent"
android:layout_height="match_parent">
<LinearLayout
android:layout_width="match_parent"
android:layout_height="wrap_content"
android:orientation="vertical" >
<Button
android:id="@+id/back"
android:layout_width="match_parent"
android:layout_height="wrap_content"
android:text="Back" />
<ImageView
android:id="@+id/imgView"
android:layout_width="match_parent"
android:layout_height="383dp"
tools:srcCompat="@tools:sample/avatars" />
</LinearLayout>
</ScrollView>
</androidx.constraintlayout.widget.ConstraintLayout>
\ No newline at end of file
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