Tonemapping

README.md

@@ -29,10 +29,15 @@ usage: main.py [-h] [--test_path]
 ```
 
 ```
-optional arguments: -h, --help  show this help message and exit
-                    --test_path path to test images
-                    --tonemap   choose tonemap method
-                    --cmap      save radiance map
+usage: gui.py
+```
+
+```
+optional arguments: -h, --help  			show this help message and exit
+                    --test_path 			path to test images
+                    --tonemap   			choose tonemap method
+                    --cmap      			save radiance map
+                    --hdr_radiance_path		in case of non-empty parameter hdr radiance map will be loaded from a file, if file does not exists then it will be calculate and stored 
 ```
 
 #### Result

gui.py

@@ -0,0 +1,107 @@
+import os
+import glob
+import cv2
+import argparse
+import numpy as np
+from utils import *
+
+gamma = 130
+tonemapType = 0
+
+def proc_hdr():
+    if tonemapType == 0:
+        output1 = np.uint8(globalTonemap(hdr_img, gamma / 100.) * 255.)
+    elif tonemapType == 1:
+        tm = cv2.createTonemapMantiuk(gamma / 100.)
+        output1 = np.uint8(255. * tm.process((hdr_img/255.).astype(np.float32)))
+    elif tonemapType == 2:
+        tm = cv2.createTonemapReinhard(gamma / 100.)
+        output1 = np.uint8(255. * tm.process((hdr_img/255.).astype(np.float32)))
+    elif tonemapType == 3:
+        tm = cv2.createTonemapDrago(gamma / 100.)
+        output1 = np.uint8(255. * tm.process((hdr_img/255.).astype(np.float32)))
+    #elif tonemapType == 4:
+        #merge_mertens = cv2.createMergeMertens()
+        #output1 = merge_mertens.process(stack)
+
+    #template = stack[len(stack)//2]
+    #image_tuned = intensityAdjustment(output, template)
+
+    output = cv2.normalize(output1, None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX)
+    cv2.imshow('image', output.astype(np.uint8))
+    return output
+
+def change_gamma(value):
+    global gamma
+    gamma = max(value, 1)
+    proc_hdr()
+
+def change_tonemap(value):
+    global tonemapType
+    tonemapType = value
+    proc_hdr()
+
+def dump():
+    from matplotlib.pylab import cm
+    colorize = cm.jet
+    cmap = np.float32(cv2.cvtColor(np.uint8(hdr_img), cv2.COLOR_BGR2GRAY)/255.)
+    cmap = colorize(cmap)
+    cv2.imwrite(os.path.join(config.test_path, 'cmap.jpg'), np.uint8(cmap*255.))
+    cv2.imwrite(os.path.join(config.test_path, 'hdr_radiance.tiff'), hdr_img)
+    file_name = "hdr" + "_" + str(gamma) + "_" + str(tonemapType) + ".jpg"
+    cv2.imwrite(os.path.join(config.test_path, file_name), proc_hdr())
+
+def main(config):
+    list_file, exps = load(config.test_path)
+    global stack
+    stack = read(list_file)
+
+    num_channels = stack.shape[-1]
+    global hdr_img
+    hdr_img = np.zeros(stack[0].shape, dtype=np.float64)
+
+    if config.hdr_radiance_path == '':
+        for c in range(num_channels):
+            layer_stack = [img[:,:,c] for img in stack]
+
+            sample = sample_intensity(layer_stack)
+
+            curve = estimate_curve(sample, exps, 100.)
+
+            img_rad = computeRadiance(np.array(layer_stack), exps, curve)
+
+            hdr_img[:,:, c] = cv2.normalize(img_rad, None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX)
+    else:
+        hdr_img = cv2.imread(config.hdr_radiance_path, -1)
+
+    proc_hdr()
+
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument('--test_path', type=str, default="imgs2/")
+    parser.add_argument('--hdr_radiance_path', type=str, default="imgs2/hdr_radiance_map.tiff")
+
+    config = parser.parse_args()
+    main(config)
+
+    cv2.createTrackbar('gamma', 'image', 130, 256, change_gamma)
+    cv2.createTrackbar('tonemap', 'image', 0, 3, change_tonemap)
+
+    while(1):
+        k = cv2.waitKey(10)
+        if k==27:    # Esc key to stop
+            break
+        elif k==32:  # Space key to dump the image
+            dump()
+        elif k==-1:  # normally -1 returned,so don't print it
+            continue
+        else:
+            #print k # else print its value
+            continue
+
+
+    #cv2.waitKey(0)
+    cv2.destroyAllWindows()

main.py

@@ -12,16 +12,19 @@ def main(config):
     num_channels = stack.shape[-1]
     hdr_img = np.zeros(stack[0].shape, dtype=np.float64)
 
-    for c in range(num_channels):
-        layer_stack = [img[:,:,c] for img in stack]
-
-        sample = sample_intensity(layer_stack)
+    if config.hdr_radiance_path == '':
+        for c in range(num_channels):
+            layer_stack = [img[:,:,c] for img in stack]
 
-        curve = estimate_curve(sample, exps, 100.)
+            sample = sample_intensity(layer_stack)
 
-        img_rad = computeRadiance(np.array(layer_stack), exps, curve)
+            curve = estimate_curve(sample, exps, 100.)
 
-        hdr_img[:,:, c] = cv2.normalize(img_rad, None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX)
+            img_rad = computeRadiance(np.array(layer_stack), exps, curve)
+
+            hdr_img[:,:, c] = cv2.normalize(img_rad, None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX)
+    else:
+        hdr_img = cv2.imread(config.hdr_radiance_path, -1)
 
     if config.tonemap =='gamma':
         output = np.uint8(globalTonemap(hdr_img, 1.3) * 255.)
@@ -34,7 +37,9 @@ def main(config):
         colorize = cm.jet
         cmap = np.float32(cv2.cvtColor(np.uint8(hdr_img), cv2.COLOR_BGR2GRAY)/255.)
         cmap = colorize(cmap)
+        cv2.imwrite(os.path.join(config.test_path, 'cmap.hdr'), cmap[:,:,0:3])
         cv2.imwrite(os.path.join(config.test_path, 'cmap.jpg'), np.uint8(cmap*255.))
+        cv2.imwrite(os.path.join(config.test_path, 'hdr_radiance.tiff'), hdr_img)
 
     template = stack[len(stack)//2]
     image_tuned = intensityAdjustment(output, template)
@@ -47,6 +52,7 @@ def main(config):
     parser = argparse.ArgumentParser()
 
     parser.add_argument('--test_path', type=str, default="imgs/night01/")
+    parser.add_argument('--hdr_radiance_path', type=str, default='')
     parser.add_argument('--tonemap', type=str, default=' ')
     parser.add_argument('--cmap', type=bool, default=False)
 

utils.py

@@ -4,18 +4,21 @@
 import random
 import numpy as np
 
+#I_min, I_max = 0., 65535.
+I_min, I_max = 0., 255.
+#I_min, I_max = 0., 1023.
+#I_min, I_max = 0., 4095.
+n = int(I_max)
 
 def weight(I):
-    I_min, I_max = 0., 255.
     if I <= (I_min + I_max) /2:
         return I - I_min
     return I_max - I
 
 def sample_intensity(stack):
-    I_min, I_max = 0., 255.
     num_intensities = int(I_max - I_min + 1)
     num_images = len(stack)
-    sample = np.zeros((num_intensities, num_images), dtype=np.uint8)
+    sample = np.zeros((num_intensities, num_images), dtype=np.float16)
 
     mid_img = stack[num_images // 2]
 
@@ -30,8 +33,6 @@ def sample_intensity(stack):
 
 
 def estimate_curve(sample, exps, l):
-    I_min, I_max = 0., 255.
-    n = 255
     A = np.zeros((sample.shape[0] * sample.shape[1] + n, n + sample.shape[0] + 1), dtype=np.float64)
     b = np.zeros((A.shape[0], 1), dtype=np.float64)
 
@@ -40,7 +41,7 @@ def estimate_curve(sample, exps, l):
     #1. data fitting
     for i in range(sample.shape[0]):
         for j in range(sample.shape[1]):
-            I_ij = sample[i,j]
+            I_ij = int(sample[i,j])
             w_ij = weight(I_ij)
             A[k, I_ij] = w_ij
             A[k, n + 1 + i] = -w_ij
@@ -115,6 +116,6 @@ def read(path_list):
 
     stack = np.zeros((len(path_list), shape[0], shape[1], shape[2]))
     for i in path_list:
-        im = cv2.imread(i)
+        im = cv2.imread(i, -1)[:,:,0:3]
         stack[path_list.index(i), :, :, :] = im
     return stack