diff --git a/src/methods/quelletl/Report_Quellet_Lou-Anne_SICOM.pdf b/src/methods/quelletl/Report_Quellet_Lou-Anne_SICOM.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..fbd9a8ea48120dbca0d6cda7698b6eb0751e57e4
Binary files /dev/null and b/src/methods/quelletl/Report_Quellet_Lou-Anne_SICOM.pdf differ
diff --git a/src/methods/template/reconstruct.py b/src/methods/quelletl/reconstruct.py
similarity index 66%
rename from src/methods/template/reconstruct.py
rename to src/methods/quelletl/reconstruct.py
index a97bd3f6e3c68df763b36c46b2727461af078bd2..63b2b3816da59c272a791c7480be5adde3213e71 100644
--- a/src/methods/template/reconstruct.py
+++ b/src/methods/quelletl/reconstruct.py
@@ -7,6 +7,12 @@ Students can call their functions (declared in others files of src/methods/your_
import numpy as np
from src.forward_model import CFA
+from src.methods.quelletl.some_function import interpolation
+import cv2
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error
+from scipy.signal import convolve2d
def run_reconstruction(y: np.ndarray, cfa: str) -> np.ndarray:
@@ -19,13 +25,30 @@ def run_reconstruction(y: np.ndarray, cfa: str) -> np.ndarray:
Returns:
np.ndarray: Demosaicked image.
"""
- # Performing the reconstruction.
- # TODO
+
input_shape = (y.shape[0], y.shape[1], 3)
op = CFA(cfa, input_shape)
+ res = interpolation(y, op)
+
+ return res
- return np.zeros(op.input_shape)
+def quad_to_bayer(y):
+ for i in range(1, y.shape[0], 4):
+ save = np.copy(y[:,i])
+ y[:,i] = y[:,i+1]
+ y[:,i+1] = save
+ for j in range(1, y.shape[0], 4):
+ save = np.copy(y[j,:])
+ y[j,:] = y[j+1,:]
+ y[j+1,:] = save
+ for i in range(1, y.shape[0], 4):
+ for j in range(1, y.shape[0], 4):
+ save = np.copy(y[i,j])
+ y[i,j] = y[i+1,j+1]
+ y[i+1,j+1] = save
+ return y
+ker_bayer_green = np.array([[0, 1, 0], [1, 4, 1], [0, 1, 0]]) / 4
####
####
diff --git a/src/methods/quelletl/some_function.py b/src/methods/quelletl/some_function.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b3cbd7c465dbe0de8d899cb42e135ac900d37bb
--- /dev/null
+++ b/src/methods/quelletl/some_function.py
@@ -0,0 +1,151 @@
+import numpy as np
+
+def bayer_blue_red(res, y, i, j) :
+ """
+ Compute estimated blue/red pixel in red/blue bayer pixel
+
+ Args :
+ res : estimated image
+ y : image to reconstruct
+ i, j : indices
+ Return :
+ value : value of the estimated pixels
+ """
+ K2 = res[i-1,j-1,1] - y[i-1,j-1]
+ K4 = res[i-1,j+1,1] - y[i-1,j+1]
+ K10 = res[i+1,j-1,1] - y[i+1,j-1]
+ K12 = res[i+1,j+1,1] - y[i+1,j+1]
+ value = res[i,j,1]-1/4*(K2+K4+K10+K12)
+ return value
+
+def bayer_green_vert(res, y, i, j) :
+ """
+ Compute estimated blue/red pixel in green bayer pixel in vertical direction
+
+ Args :
+ res : estimated image
+ y : image to reconstruct
+ i, j : indices
+ Return :
+ value : value of the estimated pixels
+ """
+ k1 = res[i-1,j,1] - y[i-1,j]
+ k2 = res[i+1,j,1] - y[i+1,j]
+ value = y[i,j] - 1/2*(k1+k2)
+ return value
+
+def bayer_green_hor(res, y, i, j):
+ """
+ Compute estimated blue/red pixel in green bayer pixel in horizontal direction
+
+ Args :
+ res : estimated image
+ y : image to reconstruct
+ i, j : indices
+ Return :
+ value : value of the estimated pixels
+ """
+ k1 = res[i,j-1,1] - y[i,j-1]
+ k2 = res[i,j+1,1] - y[i,j+1]
+ value = y[i,j] - 1/2*(k1+k2)
+ return value
+
+def interpolate_green(res, y, z):
+ """
+ Directional interpolation of the green channel
+
+ Args :
+ res : estimated image
+ y : image to reconstruct
+ z : bayer pattern
+ Return :
+ res : reconstructed image
+ """
+ for i in range(2,y.shape[0]-1):
+ for j in range(2,y.shape[1]-1):
+ # Vertical and horizontal gradient
+ if z[i,j,1]==0:
+ d_h = np.abs(y[i,j-1]-y[i,j+1])
+ d_v = np.abs(y[i-1,j]-y[i+1,j])
+ if d_h > d_v:
+ green = (y[i-1,j]+y[i+1,j])/2
+ elif d_v > d_h:
+ green = (y[i,j-1]+y[i,j+1])/2
+ else :
+ green = (y[i,j-1]+y[i,j+1]+y[i-1,j]+y[i+1,j])/4
+ else :
+ green = y[i,j]
+ res[i,j,1] = green
+ return res
+
+def quad_to_bayer(y):
+ """
+ Convert Quad Bayer to Bayer
+
+ Args :
+ res : estimated image
+ y : image to reconstruct
+ i, j : indices
+ Return :
+ value : value of the estimated pixels
+ """
+ for i in range(1, y.shape[0], 4):
+ save = np.copy(y[:,i])
+ y[:,i] = y[:,i+1]
+ y[:,i+1] = save
+ for j in range(1, y.shape[0], 4):
+ save = np.copy(y[j,:])
+ y[j,:] = y[j+1,:]
+ y[j+1,:] = save
+ for i in range(1, y.shape[0], 4):
+ for j in range(1, y.shape[0], 4):
+ save = np.copy(y[i,j])
+ y[i,j] = y[i+1,j+1]
+ y[i+1,j+1] = save
+ return y
+
+def interpolation(y, op):
+ """
+ Reconstruct image
+
+ Args :
+ y : image to reconstruct
+ op : CFA operator
+ Return :
+ np.ndarray: Demosaicked image.
+ """
+ if op.cfa == 'quad_bayer':
+ y = quad_to_bayer(y)
+ op.mask = quad_to_bayer(op.mask)
+
+ z = op.adjoint(y)
+ res = np.empty(op.input_shape)
+
+ # Interpolation of green channel
+ res = interpolate_green(res, y, z)
+ # Interpolation of R and B channels using channel correlation
+ for i in range(2,y.shape[0]-2):
+ for j in range(2, y.shape[1]-2):
+ # Bayer is Green
+ if z[i,j,1] != 0 :
+ # Green is between 2 vertical bleu
+ if z[i+1,j,0] == 0:
+ red = bayer_green_hor(res, y, i, j) # Compute Red channel
+ blue = bayer_green_vert(res, y, i, j) # Compute Blue channel
+ # Green is between 2 vertical red
+ else:
+ blue = bayer_green_hor(res, y, i, j) # Compute Blue channel
+ red = bayer_green_vert(res, y, i, j) # Compute Red channel
+ # Bayer is red
+ elif z[i,j,0] != 0 :
+ red = y[i,j] # Red channel
+ blue = bayer_blue_red(res, y, i, j) # Blue channel
+ # Bayer is bleue
+ elif z[i,j,2] != 0 :
+ blue = y[i,j] # Bleu channel
+ red = bayer_blue_red(res, y, i, j) # Res channel
+
+ res[i,j,0] = np.clip(red, 0, 255)
+ res[i,j,2] = np.clip(blue,0,255)
+ return res
+