refactor open-loop tests

testOpenLoopSynthesizer.py

@@ -0,0 +1,214 @@
+#!/usr/bin/env python
+
+# Author: Murad Abu-Khalaf, MIT CSAIL.
+
+"""
+    Open-loop testing of the Synthesizer's ability to generate reference views.
+
+"""
+
+import matplotlib.pyplot as plt
+import torch
+from tqdm import tqdm
+import os
+import cv2
+import numpy as np
+
+
+import trainViewSynthesizerNNet
+
+training_data =  trainViewSynthesizerNNet.getNumPyTrainingData()
+net = trainViewSynthesizerNNet.net
+device = trainViewSynthesizerNNet.device
+
+###### Define the Test Methods #######
+
+def showTrainingData():
+    """
+    Plots observed Views to serve as input to the synthesizer
+    along with observed views at desired distances that will
+    serve as ground truth for the output of the Synthesizer
+    during training.
+
+    This simply shows the training data. All views are Camera views, and
+    non are synthesizer views.
+    """
+    print("Training Dataset Size:" + str(len(training_data)))
+    for idx in range(len(training_data)):
+        observed = training_data[idx][0]
+        desired_spacing = training_data[idx][1][0,0,0]
+        desired_view = training_data[idx][2]
+
+        plt.figure(1, figsize=(9,5))
+        plt.subplot(1,2,1)
+        plt.title("Observed View (Input to Synthesizer)")
+        plt.imshow(observed.transpose(1,2,0), cmap="viridis")
+        plt.show(block = False)
+
+        plt.subplot(1,2,2)
+        plt.title("Ground Truth: Observed View for " +  '{:4.2f}'.format(desired_spacing))
+        plt.imshow(desired_view.transpose(1,2,0), cmap="viridis")
+        plt.show(block = False)
+        plt.pause(0.1)
+
+
+def generateReferenceViewFromObservation():
+    """ 
+    Generates a reference view for a FIXED reference distance and FIXED camera view.
+    """
+    idx = 747       # Picking an observation
+    observed = training_data[idx][0]
+    spacing  = training_data[idx][1]
+    groundtruth = training_data[idx][2]
+
+    plt.figure(2, figsize=(9,5))
+    plt.subplot(1,3,1)
+    plt.axis("off")
+    plt.title("Observed View")
+    plt.imshow(observed.transpose(1,2,0)/255.0, cmap="viridis")
+
+    generated = net(torch.Tensor([observed]).to(device), torch.Tensor(spacing).to(device))
+    generated = generated.to('cpu').detach().numpy()[0]
+    plt.subplot(1,3,2)
+    plt.axis("off")
+    plt.title("Generated View for " +  '{:4.2f}'.format(spacing.item()))
+    plt.imshow(generated.transpose(1,2,0)/255.0, cmap="viridis")
+
+    plt.subplot(1,3,3)
+    plt.axis("off")
+    plt.title("Ground Truth View for " +  '{:4.2f}'.format(spacing.item()))
+    plt.imshow(groundtruth.transpose(1,2,0)/255.0, cmap="viridis")
+
+    plt.show(block = True)
+
+    # Save the generated view for publication purposes
+    plt.figure(2,frameon=False)
+    plt.imshow(observed.transpose(1,2,0)/255.0, cmap="viridis")
+    plt.axis("off")
+    plt.savefig('observed.png',bbox_inches='tight', pad_inches=0)
+
+    plt.figure(2,frameon=False)
+    plt.imshow(generated.transpose(1,2,0)/255.0, cmap="viridis")
+    plt.axis("off")
+    plt.savefig('generated.png',bbox_inches='tight', pad_inches=0)
+
+
+def generateReferenceViewsFromObservation():
+    """ 
+    Generates reference views for a VARYING reference distance and FIXED camera view.
+    """
+    idx = 150       # Picking an observation
+    observed = training_data[idx][0]
+    plt.figure(3, figsize=(9,5))
+    plt.ion()
+    plt.subplot(1,2,1)
+    plt.title("Observed View")
+    plt.imshow(observed.transpose(1,2,0)/255.0, cmap="viridis")
+
+    plt.subplot(1,2,2)
+    for s in tqdm(range(10,31,10)):
+        generated = net(torch.Tensor([observed]).to(device), torch.Tensor([[[[s]]]]).to(device))
+        generated = generated.to('cpu').detach().numpy()[0]
+        plt.imshow(generated.transpose(1,2,0)/255.0, cmap="viridis")
+        plt.title("Generated View for " + '{:4.2f}'.format(s))
+        plt.pause(1.00)
+        #plt.draw()
+
+    input("Press [enter] to close.")
+
+def generateReferenceViewFromObservations():
+    """ 
+    Generates a reference view for a FIXED reference distance and VARYING camera views.
+    """
+    s = 10        # Picking a desired spacing 
+
+    fig = plt.figure(4, figsize=(9,5))
+    plt.ion()
+    sub1 = fig.add_subplot(1,2,1)
+    sub1.set_title("Observed View")    
+    sub2 = fig.add_subplot(1,2,2)
+    sub2.set_title("Generated View for " + '{:4.2f}'.format(s))
+
+    for idx in tqdm(range(0,len(training_data),1)):
+        observed = training_data[idx][0]
+        generated = net(torch.Tensor([observed]).to(device), torch.Tensor([[[[s]]]]).to(device))
+        generated = generated.to('cpu').detach().numpy()[0]
+        sub1.imshow(observed.transpose(1,2,0)/255.0, cmap="viridis")
+        sub2.imshow(generated.transpose(1,2,0)/255.0, cmap="viridis")
+        plt.pause(0.25)
+        #plt.draw()
+
+    input("Press [enter] to close.")
+
+
+def testGeneralizationDataSet():
+    """ 
+    Generates reference views for a VARYING reference distance and Fixed camera view.
+    """
+
+    # Create NumPy tensors from images
+    IMG_HEIGHT = 128
+    IMG_WIDTH = 128
+
+    TestingFolder = "CameraViewDistanceDataSet/TestingDataSet"
+    LABELS = [f for f in os.listdir(TestingFolder) if not f.startswith('.')] # Use this to avoid hidden files
+    LABELS.sort()
+
+    test_data = []
+    for label in tqdm(LABELS):
+        try:
+            path = os.path.join(TestingFolder, label)
+            img = cv2.imread(path, cv2.IMREAD_COLOR)   # HxWxC
+            img = cv2.resize(img, (IMG_WIDTH, IMG_HEIGHT))
+            img = img.transpose(2,0,1) # HxWxC ==> CxHxW
+            img = img[::-1,:,:]  # BGR ==> RGB
+            test_data.append(np.array(img))
+        except Exception as e:
+            print(e)
+            pass
+
+    # Generate a reference view from an observation
+    idx = 333       # Picking an observation
+    observed = test_data[idx]
+    plt.figure(6, figsize=(9,5))
+    plt.ion()
+    plt.subplot(1,2,1)
+    plt.title("Observed Camera Feed")
+    plt.imshow(observed.transpose(1,2,0)/255.0, cmap="viridis")
+
+    plt.subplot(1,2,2)
+    for s in tqdm(range(10,31,10)):
+        generated = net(torch.Tensor([observed]).to(device), torch.Tensor([[[[s]]]]).to(device))
+        generated = generated.to('cpu').detach().numpy()[0]
+        plt.imshow(generated.transpose(1,2,0)/255.0, cmap="viridis")
+        plt.title("Generated Scene View for Spacing " + '{:4.2f}'.format(s))
+        plt.pause(1.00)
+
+    input("Press [enter] to close.")
+
+    # Save the generated view for publication purposes
+    plt.figure(6,frameon=False)
+    plt.imshow(observed.transpose(1,2,0)/255.0, cmap="viridis")
+    plt.axis("off")
+    plt.savefig('observed.png',bbox_inches='tight', pad_inches=0)
+
+    plt.figure(6,frameon=False)
+    plt.imshow(generated.transpose(1,2,0)/255.0, cmap="viridis")
+    plt.axis("off")
+    plt.savefig('generated.png',bbox_inches='tight', pad_inches=0)
+
+
+###### Choose the desired test by speciying a number #######
+
+testID = 4
+
+if testID == 0:
+    showTrainingData()
+elif testID == 1:
+    generateReferenceViewFromObservation()
+elif testID == 2:
+    generateReferenceViewsFromObservation()
+elif testID == 3:
+    generateReferenceViewFromObservations()
+elif testID == 4:
+    testGeneralizationDataSet()