Add python tests for dnn module

modules/python/test/test_dnn.py

@@ -0,0 +1,179 @@
+#!/usr/bin/env python
+import os
+import cv2 as cv
+import numpy as np
+
+from tests_common import NewOpenCVTests
+
+def normAssert(test, a, b, lInf=1e-5):
+    test.assertLess(np.max(np.abs(a - b)), lInf)
+
+def inter_area(box1, box2):
+    x_min, x_max = max(box1[0], box2[0]), min(box1[2], box2[2])
+    y_min, y_max = max(box1[1], box2[1]), min(box1[3], box2[3])
+    return (x_max - x_min) * (y_max - y_min)
+
+def area(box):
+    return (box[2] - box[0]) * (box[3] - box[1])
+
+def box2str(box):
+    left, top = box[0], box[1]
+    width, height = box[2] - left, box[3] - top
+    return '[%f x %f from (%f, %f)]' % (width, height, left, top)
+
+def normAssertDetections(test, ref, out, confThreshold=0.0, scores_diff=1e-5, boxes_iou_diff=1e-4):
+    ref = np.array(ref, np.float32)
+    refClassIds, testClassIds = ref[:, 1], out[:, 1]
+    refScores, testScores = ref[:, 2], out[:, 2]
+    refBoxes, testBoxes = ref[:, 3:], out[:, 3:]
+
+    matchedRefBoxes = [False] * len(refBoxes)
+    errMsg = ''
+    for i in range(len(refBoxes)):
+        testScore = testScores[i]
+        if testScore < confThreshold:
+            continue
+
+        testClassId, testBox = testClassIds[i], testBoxes[i]
+        matched = False
+        for j in range(len(refBoxes)):
+            if (not matchedRefBoxes[j]) and testClassId == refClassIds[j] and \
+               abs(testScore - refScores[j]) < scores_diff:
+                interArea = inter_area(testBox, refBoxes[j])
+                iou = interArea / (area(testBox) + area(refBoxes[j]) - interArea)
+                if abs(iou - 1.0) < boxes_iou_diff:
+                    matched = True
+                    matchedRefBoxes[j] = True
+        if not matched:
+            errMsg += '\nUnmatched prediction: class %d score %f box %s' % (testClassId, testScore, box2str(testBox))
+
+    for i in range(len(refBoxes)):
+        if (not matchedRefBoxes[i]) and refScores[i] > confThreshold:
+            errMsg += '\nUnmatched reference: class %d score %f box %s' % (refClassIds[i], refScores[i], box2str(refBoxes[i]))
+    if errMsg:
+        test.fail(errMsg)
+
+
+# Returns a simple one-layer network created from Caffe's format
+def getSimpleNet():
+    prototxt = """
+        name: "simpleNet"
+        input: "data"
+        layer {
+          type: "Identity"
+          name: "testLayer"
+          top: "testLayer"
+          bottom: "data"
+        }
+    """
+    return cv.dnn.readNetFromCaffe(bytearray(prototxt, 'utf8'))
+
+
+def testBackendAndTarget(backend, target):
+    net = getSimpleNet()
+    net.setPreferableBackend(backend)
+    net.setPreferableTarget(target)
+    inp = np.random.standard_normal([1, 2, 3, 4]).astype(np.float32)
+    try:
+        net.setInput(inp)
+        net.forward()
+    except BaseException as e:
+        return False
+    return True
+
+
+haveInfEngine = testBackendAndTarget(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_CPU)
+dnnBackendsAndTargets = [
+    [cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_CPU],
+]
+
+if haveInfEngine:
+    dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_CPU])
+    if testBackendAndTarget(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_MYRIAD):
+        dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_MYRIAD])
+
+if cv.ocl.haveOpenCL() and cv.ocl.useOpenCL():
+    dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_OPENCL])
+    dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_OPENCL_FP16])
+    if haveInfEngine:
+        dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_OPENCL])
+        dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_OPENCL_FP16])
+
+
+def printParams(backend, target):
+    backendNames = {
+        cv.dnn.DNN_BACKEND_OPENCV: 'OCV',
+        cv.dnn.DNN_BACKEND_INFERENCE_ENGINE: 'DLIE'
+    }
+    targetNames = {
+        cv.dnn.DNN_TARGET_CPU: 'CPU',
+        cv.dnn.DNN_TARGET_OPENCL: 'OCL',
+        cv.dnn.DNN_TARGET_OPENCL_FP16: 'OCL_FP16',
+        cv.dnn.DNN_TARGET_MYRIAD: 'MYRIAD'
+    }
+    print('%s/%s' % (backendNames[backend], targetNames[target]))
+
+
+class dnn_test(NewOpenCVTests):
+
+    def find_dnn_file(self, filename):
+        return self.find_file(filename, [os.environ['OPENCV_DNN_TEST_DATA_PATH']])
+
+    def test_blobFromImage(self):
+        np.random.seed(324)
+
+        width = 6
+        height = 7
+        scale = 1.0/127.5
+        mean = (10, 20, 30)
+
+        # Test arguments names.
+        img = np.random.randint(0, 255, [4, 5, 3]).astype(np.uint8)
+        blob = cv.dnn.blobFromImage(img, scale, (width, height), mean, True, False)
+        blob_args = cv.dnn.blobFromImage(img, scalefactor=scale, size=(width, height),
+                                         mean=mean, swapRB=True, crop=False)
+        normAssert(self, blob, blob_args)
+
+        # Test values.
+        target = cv.resize(img, (width, height), interpolation=cv.INTER_LINEAR)
+        target = target.astype(np.float32)
+        target = target[:,:,[2, 1, 0]]  # BGR2RGB
+        target[:,:,0] -= mean[0]
+        target[:,:,1] -= mean[1]
+        target[:,:,2] -= mean[2]
+        target *= scale
+        target = target.transpose(2, 0, 1).reshape(1, 3, height, width)  # to NCHW
+        normAssert(self, blob, target)
+
+
+    def test_face_detection(self):
+        proto = self.find_dnn_file('dnn/opencv_face_detector.prototxt')
+        model = self.find_dnn_file('dnn/opencv_face_detector.caffemodel')
+
+        img = self.get_sample('gpu/lbpcascade/er.png')
+        blob = cv.dnn.blobFromImage(img, mean=(104, 177, 123), swapRB=False, crop=False)
+
+        ref = [[0, 1, 0.99520785, 0.80997437, 0.16379407, 0.87996572, 0.26685631],
+               [0, 1, 0.9934696,  0.2831718,  0.50738752, 0.345781,   0.5985168],
+               [0, 1, 0.99096733, 0.13629119, 0.24892329, 0.19756334, 0.3310290],
+               [0, 1, 0.98977017, 0.23901358, 0.09084064, 0.29902688, 0.1769477],
+               [0, 1, 0.97203469, 0.67965847, 0.06876482, 0.73999709, 0.1513494],
+               [0, 1, 0.95097077, 0.51901293, 0.45863652, 0.5777427,  0.5347801]]
+
+        print('\n')
+        for backend, target in dnnBackendsAndTargets:
+            printParams(backend, target)
+
+            net = cv.dnn.readNet(proto, model)
+            net.setPreferableBackend(backend)
+            net.setPreferableTarget(target)
+            net.setInput(blob)
+            out = net.forward().reshape(-1, 7)
+
+            scoresDiff = 4e-3 if target in [cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD] else 1e-5
+            iouDiff = 2e-2 if target in [cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD] else 1e-4
+
+            normAssertDetections(self, ref, out, 0.5, scoresDiff, iouDiff)
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

modules/python/test/tests_common.py

@@ -26,23 +26,24 @@ class NewOpenCVTests(unittest.TestCase):
     # github repository url
     repoUrl = 'https://raw.github.com/opencv/opencv/master'
 
+    def find_file(self, filename, searchPaths=[]):
+        searchPaths = searchPaths if searchPaths else [self.repoPath, self.extraTestDataPath]
+        for path in searchPaths:
+            if path is not None:
+                candidate = path + '/' + filename
+                if os.path.isfile(candidate):
+                    return candidate
+        self.fail('File ' + filename + ' not found')
+        return None
+
+
     def get_sample(self, filename, iscolor = None):
         if iscolor is None:
             iscolor = cv.IMREAD_COLOR
         if not filename in self.image_cache:
-            filedata = None
-            if NewOpenCVTests.repoPath is not None:
-                candidate = NewOpenCVTests.repoPath + '/' + filename
-                if os.path.isfile(candidate):
-                    with open(candidate, 'rb') as f:
-                        filedata = f.read()
-            if NewOpenCVTests.extraTestDataPath is not None:
-                candidate = NewOpenCVTests.extraTestDataPath + '/' + filename
-                if os.path.isfile(candidate):
-                    with open(candidate, 'rb') as f:
-                        filedata = f.read()
-            if filedata is None:
-                return None#filedata = urlopen(NewOpenCVTests.repoUrl + '/' + filename).read()
+            filepath = self.find_file(filename)
+            with open(filepath, 'rb') as f:
+                filedata = f.read()
             self.image_cache[filename] = cv.imdecode(np.fromstring(filedata, dtype=np.uint8), iscolor)
         return self.image_cache[filename]
 
@@ -102,4 +103,4 @@ def isPointInRect(p, rect):
     if rect[0] <= p[0] and rect[1] <=p[1] and p[0] <= rect[2] and p[1] <= rect[3]:
         return True
     else:
-        return False
+        return False