def transform_im(image, NUM_MVP = 20):
rowid, colid = np.where( image <100)
id2cs = np.random.choice(range(len(rowid)), NUM_MVP, replace=False)
p = np.stack((rowid, colid)).transpose()
p = p[id2cs]
q = p + (20*(np.random.rand(p.shape[0], p.shape[1]) -0.5 ) ).astype(int)
transformed_image = 255*mls_rigid_deformation_inv(image, p, q, alpha=1, density=1)
if np.random.uniform()>0.5:
transformed_image = affine_im(transformed_image, transpix=0.05*image.shape[0]*(np.random.uniform(size=[2])-0.5), angle=5*(np.random.uniform()-0.5))
return binary(transformed_image)
def demo():
p = np.array([[30, 155], [125, 155], [225, 155], [100, 235], [160, 235],
[85, 295], [180, 293]])
q = np.array([[42, 211], [125, 155], [235, 100], [80, 235], [140, 235],
[85, 295], [180, 295]])
image = plt.imread('mr_big_ori.jpg')
plt.subplot(121)
plt.axis('off')
plt.imshow(image)
transformed_image = mls_rigid_deformation_inv(image, p, q)
plt.subplot(122)
plt.axis('off')
plt.imshow(transformed_image)
plt.tight_layout(w_pad=1.0, h_pad=1.0)
plt.show()
#print(count)
#print(p.shape)
#print(q.shape)
#filename = './Users\Sony\Desktop\MSR\face morphing code\video frame extract\output' + "frame%d.jpg";count+=1
#cv2.imwrite(filename, image)
#cv2.imwrite(''+"framer%d.jpg" % countt,image)
#cv2.imshow('frame',image)
#count += 1
#transformed_image = mls_rigid_deformation_inv(image, p, q, alpha=1, density=0.7)
# cv2.imwrite("framesave/frame%d.jpg" % count, image)
lstart=time.time()
print('Preprocessing Time : ',(lstart-start))
astart=time.time()
transformed_image = mls_rigid_deformation_inv(image, p, q, alpha=1, density=1)
end=time.time()
# Display the resulting frame
print('Frame processing Time : ',(end-astart))
cv2.imwrite("frameM%d.jpg" % count, transformed_image)
out.write(transformed_image)
cv2.imshow('frame',transformed_image)
print("success")
if cv2.waitKey(60) & 0xFF == ord('q'):
break
# When everything done, release the capture
noise[:, [0,-1], :] = 0
q = p + noise.reshape(size**2, 2)
return p, q
if __name__ == "__main__":
npzfile = np.load(all_img_lab_path)
train_images = npzfile['train_images']
height = train_images.shape[1]
width = train_images.shape[2]
plt.figure(figsize=(10, 10))
plt.subplot(221)
plt.axis('off')
plt.imshow(train_images[0], cmap='gray')
plt.title("Original image")
for i in range(3):
p, q = generate_ctrl_pts(height, width, size=5, range=20)
rigid = mls_rigid_deformation_inv(train_images[0], p, q)
plt.subplot(2, 2, i+2)
plt.imshow(rigid, cmap='gray')
plt.title("Generate rigid " + str(i + 1))
plt.axis('off')
plt.tight_layout(w_pad=1.0)
plt.show()