Commit 5bd67af5 authored by Kelly Chang's avatar Kelly Chang

kel changed

parent 2aa38181
...@@ -101,18 +101,36 @@ def plot_hist(tiff_list): ...@@ -101,18 +101,36 @@ def plot_hist(tiff_list):
image = Image.open(image) #Open the image and read it as an Image object image = Image.open(image) #Open the image and read it as an Image object
image = np.array(image)[1:,:] #Convert to an array, leaving out the first row because the first row is just housekeeping data image = np.array(image)[1:,:] #Convert to an array, leaving out the first row because the first row is just housekeeping data
row, col = image.shape row, col = image.shape
predict = np.empty(row,col) # create a empty matrix to update prediction predict = np.empty([row,col]) # create a empty matrix to update prediction
predict[0,:] = image[0,:] # keep the first row from the image predict[0,:] = image[0,:] # keep the first row from the image
predict[:,0] = image[:,0] # keep the first columen from the image predict[:,0] = image[:,0] # keep the first columen from the image
diff = np.empty(row,col) predict[-1,:] = image[-1,:] # keep the first row from the image
diff[0,:] = np.zeros(row) # keep the first row from the image predict[:,-1] = image[:,-1] # keep the first columen from the image
diff[:,0] = np.zeros(col) diff = np.empty([row,col])
for r in range(1,row): # loop through the rth row diff[0,:] = np.zeros(col) # keep the first row from the image
for c in range(1,col): # loop through the cth column diff[:,0] = np.zeros(row)
surrounding = anp.array([predict[r-1,c-1], predict[r-1,c], predict[r-1,c+1], predict[r1,c-1]]) diff[-1,:] = np.zeros(col) # keep the first row from the image
diff[:,-1] = np.zeros(row)
for r in range(1,row-1): # loop through the rth row
for c in range(1,col-1): # loop through the cth column
surrounding = np.array([predict[r-1,c-1], predict[r-1,c], predict[r-1,c+1], predict[r,c-1]])
predict[r,c] = np.mean(surrounding) # take the mean of the previous 4 pixels predict[r,c] = np.mean(surrounding) # take the mean of the previous 4 pixels
diff[r,c] = (np.max(surrounding)-np.min(surrounding)) diff[r,c] = (np.max(surrounding)-np.min(surrounding))
predict = np.ravel(predict)
diff = np.ravel(diff)
n = len(predict)
fig = plt.figure()
ax1 = fig.add_subplot(111, projection='3d')
z3 = np.zeros(n)
dx = np.ones(n)
dy = np.ones(n)
dz = np.arange(n)
ax1.bar3d(predict, diff, z3, dx, dy, dz, color="red")
ax1.axis('off')
plt.show()
if __name__ == '__main__': if __name__ == '__main__':
...@@ -124,5 +142,5 @@ if __name__ == '__main__': ...@@ -124,5 +142,5 @@ if __name__ == '__main__':
scenes = file_extractor() scenes = file_extractor()
images = image_extractor(scenes) images = image_extractor(scenes)
plot_hist(images)
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