Merge pull request #10 from matlab-deep-learning/feature/5

Perform image type conversion upfront

Author: justinpinkney

code/mtcnn/+mtcnn/Detector.m

@@ -70,9 +70,7 @@
             %
             %   See also: mtcnn.detectFaces
 
-            if obj.UseGPU()
-                im = gpuArray(single(im));
-            end
+            im = obj.prepImage(im);
 
             bboxes = [];
             scores = [];
@@ -103,7 +101,7 @@
             end
 
             %% Stage 2 - Refinement
-            [cropped, bboxes] = obj.prepImages(im, bboxes, obj.RnetSize);
+            [cropped, bboxes] = obj.prepBbox(im, bboxes, obj.RnetSize);
             [probs, correction] = mtcnn.rnet(cropped, obj.RnetWeights);
             [scores, bboxes] = obj.processOutputs(probs, correction, bboxes, 2);
 
@@ -112,7 +110,7 @@
             end
 
             %% Stage 3 - Output
-            [cropped, bboxes] = obj.prepImages(im, bboxes, obj.OnetSize);
+            [cropped, bboxes] = obj.prepBbox(im, bboxes, obj.OnetSize);
 
             % Adjust bboxes for the behaviour of imcrop
             bboxes(:, 1:2) = bboxes(:, 1:2) - 0.5;
@@ -144,12 +142,12 @@ function loadWeights(obj)
             obj.OnetWeights = load(fullfile(mtcnnRoot(), "weights", "onet.mat"));
         end
 
-        function [cropped, bboxes] = prepImages(obj, im, bboxes, outputSize)
+        function [cropped, bboxes] = prepBbox(obj, im, bboxes, outputSize)
             % prepImages    Pre-process the images and bounding boxes.
             bboxes = mtcnn.util.makeSquare(bboxes);
             bboxes = round(bboxes);
             cropped = mtcnn.util.cropImage(im, bboxes, outputSize);
-            cropped = dlarray(single(cropped)./255*2 - 1, "SSCB");
+            cropped = dlarray(cropped, "SSCB");
 
         end
 
@@ -167,5 +165,29 @@ function loadWeights(obj)
                                 "OverlapThreshold", obj.NmsThresholds(netIdx));
             end
         end
+        
+        function outIm = prepImage(obj, im)
+            % convert the image to the correct scaling and type
+            % All images should be scaled to -1 to 1 and of single type
+            % also place on the GPU if required
+            
+            switch class(im)
+                case "uint8"
+                    outIm = single(im)/255*2 - 1;
+                case "single"
+                    % expect floats to be 0-1 scaled
+                    outIm = im*2 - 1;
+                case "double"
+                    outIm = single(im)*2 - 1;
+                otherwise
+                    error("mtcnn:Detector:UnsupportedType", ...
+                        "Input image is of unsupported type '%s'", class(im));
+            end
+            
+            if obj.UseGPU()
+                outIm = gpuArray(outIm);
+            end
+            
+        end
     end
 end

code/mtcnn/+mtcnn/proposeRegions.m

@@ -2,7 +2,7 @@
 % proposeRegions    Generate region proposals at a given scale.
 %
 % Args:
-%   im          - Input image 0-255 range
+%   im          - Input image -1 to 1 range, type single
 %   scale       - Scale to run proposal at
 %   threshold   - Confidence threshold to accept proposal
 %   weights     - P-Net weights struct
@@ -19,7 +19,7 @@
     pnetSize = 12;
 
     im = imresize(im, 1/scale);
-    im = dlarray(single(im)./255*2 - 1, "SSCB");
+    im = dlarray(im, "SSCB");
 
     [probability, correction] = mtcnn.pnet(im, weights);
 

test/+tests/DetectorTest.m

@@ -8,6 +8,12 @@
         Reference
     end
 
+    properties (TestParameter)
+        imageTypeConversion = struct("uint8", @(x) x, ...
+            "single", @(x) single(x)/255, ...
+            "double", @(x) double(x)/255)
+    end
+    
     methods (TestClassSetup)
         function setupTestImage(test)
             test.Image = imread("visionteam.jpg");
@@ -26,10 +32,14 @@ function testCreate(test)
             detector = mtcnn.Detector();
         end
 
-        function testDetectwithDefaults(test)
+        function testDetectwithDefaults(test, imageTypeConversion)
+            % Test expected inputs with images of type uint8, single,
+            % double (float images are scaled 0-1);
             detector = mtcnn.Detector();
 
-            [bboxes, scores, landmarks] = detector.detect(test.Image);
+            inputImage = imageTypeConversion(test.Image);
+            
+            [bboxes, scores, landmarks] = detector.detect(inputImage);
 
             test.verifyEqual(size(bboxes), [6, 4]);
             test.verifyEqual(size(scores), [6, 1]);
@@ -118,4 +128,4 @@ function testGpuDetect(test)
             test.verifyEqual(landmarks, test.Reference.landmarks, "RelTol", 1e-1);
         end
     end
-end
+    end

test/makeDetectionReference.m

@@ -0,0 +1,10 @@
+function makeDetectionReference()
+    % Run the detector in known good config to create reference boxes,
+    % scores and landmarks for regression tests.
+    im = imread("visionteam.jpg");
+    [bboxes, scores, landmarks] = mtcnn.detectFaces(im);
+    
+    filename = fullfile(mtcnnTestRoot(), "resources", "ref.mat");
+    save(filename, "bboxes", "scores", "landmarks");
+    
+end