def test_detection_softmax_model(self):
img_path = self.find_dnn_file("dnn/person-detector.png")
weights = self.find_dnn_file("dnn/squeezenet_softmax.caffemodel",
required=False)
config = self.find_dnn_file("dnn/squeezenet_softmax.prototxt")
if weights is None or config is None:
raise unittest.SkipTest(
"Missing DNN test files (dnn/squeezenet_v1.1.{prototxt/caffemodel}). Verify OPENCV_DNN_TEST_DATA_PATH configuration parameter."
)
frame = cv.imread(img_path).astype(np.uint8)
model = cv.dnn_ClassificationModel(config, weights)
model.setInputSize(224, 224)
model.setInputCrop(True)
out = model.predict(frame)
for i in range(10):
frame2 = np.clip(frame + np.random.randint(0, 2, frame.shape), 0,
255).astype(np.uint8)
out2 = model.predict(frame2)
ref = out2
try:
normAssert(self,
out[0],
ref[0],
msg="prediction probility in max. " +
str(np.max(np.abs(out[0] - ref[0]))))
except Exception as e:
print(e.args)
def test_classification_model(self):
img_path = self.find_dnn_file("dnn/googlenet_0.png")
weights = self.find_dnn_file("dnn/squeezenet_v1.1.caffemodel")
config = self.find_dnn_file("dnn/squeezenet_v1.1.prototxt")
ref = np.load(self.find_dnn_file("dnn/squeezenet_v1.1_prob.npy"))
frame = cv.imread(img_path)
model = cv.dnn_ClassificationModel(config, weights)
model.setInputSize(227, 227)
model.setInputCrop(True)
out = model.predict(frame)
normAssert(self, out, ref)
def test_classification_model(self):
img_path = self.find_dnn_file("dnn/googlenet_0.png")
weights = self.find_dnn_file("dnn/squeezenet_v1.1.caffemodel", required=False)
config = self.find_dnn_file("dnn/squeezenet_v1.1.prototxt")
ref = np.load(self.find_dnn_file("dnn/squeezenet_v1.1_prob.npy"))
if weights is None or config is None:
raise unittest.SkipTest("Missing DNN test files (dnn/squeezenet_v1.1.{prototxt/caffemodel}). Verify OPENCV_DNN_TEST_DATA_PATH configuration parameter.")
frame = cv.imread(img_path)
model = cv.dnn_ClassificationModel(config, weights)
model.setInputSize(227, 227)
model.setInputCrop(True)
out = model.predict(frame)
normAssert(self, out, ref)
import os
import cv2
net = cv2.dnn_ClassificationModel('./model/plate_model.xml',
'./model/plate_model.bin')
net.setPreferableTarget(cv2.dnn.DNN_TARGET_MYRIAD)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
path = os.path.dirname(__file__)
HEIGHT = 32
WIDTH = 32
def predict(img):
if type(img) == str: img = cv2.imread(img, 0)
elif img.ndim > 2: img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#if img[0][0] > 1: img = img / 255.
img = cv2.resize(img, (WIDTH, HEIGHT))
prediction = net.predict(img)[0][0]
return prediction
if __name__ == "__main__":
for i in range(9):
img = cv2.imread('./data/' + str(i) + '.png')
