def predict(self, imagePaths):
image_size = self.image_sizes[self.model]
LABELS = open(self.classesFile).read().strip().split("\n")
classes = [label.split(',')[0] for label in LABELS]
num_classes = len(classes)
weighted_bifpn = False
colors = [
np.random.randint(0, 256, 3).tolist() for i in range(num_classes)
]
model, prediction_model = efficientdet(phi=self.model,
weighted_bifpn=weighted_bifpn,
num_classes=num_classes,
score_threshold=self.CONFIDENCE)
prediction_model.load_weights(self.modelWeights, by_name=True)
imagePaths = list(paths.list_images(imagePaths))
for (i, image_path) in enumerate(imagePaths):
print("[INFO] predicting on image {} of {}".format(
i + 1, len(imagePaths)))
image = cv2.imread(image_path)
src_image = image.copy()
image = image[:, :, ::-1]
h, w = image.shape[:2]
image, scale, offset_h, offset_w = preprocess_image(
image, image_size=image_size)
inputs = np.expand_dims(image, axis=0)
anchors = anchors_for_shape((image_size, image_size))
# run network
start = time.time()
boxes, scores, labels = prediction_model.predict_on_batch([
np.expand_dims(image, axis=0),
np.expand_dims(anchors, axis=0)
])
boxes[0, :, [0, 2]] = boxes[0, :, [0, 2]] - offset_w
boxes[0, :, [1, 3]] = boxes[0, :, [1, 3]] - offset_h
boxes /= scale
boxes[0, :, 0] = np.clip(boxes[0, :, 0], 0, w - 1)
boxes[0, :, 1] = np.clip(boxes[0, :, 1], 0, h - 1)
boxes[0, :, 2] = np.clip(boxes[0, :, 2], 0, w - 1)
boxes[0, :, 3] = np.clip(boxes[0, :, 3], 0, h - 1)
# select indices which have a score above the threshold
indices = np.where(scores[0, :] > self.CONFIDENCE)[0]
# select those detections
boxes = boxes[0, indices]
scores = scores[0, indices]
labels = labels[0, indices]
file = open(image_path[0:image_path.rfind(".")] + ".xml", "w")
file.write(
self.generateXML(image_path[0:image_path.rfind(".")],
image_path, h, w, 3, boxes, scores, labels,
classes))
file.close()
def mainDataset(dataset, output, confidence, weights, fichClass, phi=0):
f = open(fichClass)
LABELS = f.read().strip().split("\n")
LABELS = {int(L.split(",")[1]): L.split(",")[0] for L in LABELS}
f.close()
weighted_bifpn = False
model_path = weights
image_sizes = (512, 640, 768, 896, 1024, 1280, 1408)
image_size = image_sizes[phi]
num_classes = len(LABELS)
score_threshold = confidence
model, prediction_model = efficientdet(phi=phi,
weighted_bifpn=weighted_bifpn,
num_classes=num_classes,
score_threshold=score_threshold)
prediction_model.load_weights(model_path, by_name=True)
imagePaths = list(paths.list_images(dataset))
# loop over the input image paths
for (i, imagePath) in enumerate(imagePaths):
image = cv2.imread(imagePath)
image = image[:, :, ::-1]
h, w = image.shape[:2]
image, scale, offset_h, offset_w = preprocess_image(
image, image_size=image_size)
inputs = np.expand_dims(image, axis=0)
anchors = anchors_for_shape((image_size, image_size))
boxes, scores, labels = prediction_model.predict_on_batch(
[np.expand_dims(image, axis=0),
np.expand_dims(anchors, axis=0)])
boxes[0, :, [0, 2]] = boxes[0, :, [0, 2]] - offset_w
boxes[0, :, [1, 3]] = boxes[0, :, [1, 3]] - offset_h
boxes /= scale
boxes[0, :, 0] = np.clip(boxes[0, :, 0], 0, w - 1)
boxes[0, :, 1] = np.clip(boxes[0, :, 1], 0, h - 1)
boxes[0, :, 2] = np.clip(boxes[0, :, 2], 0, w - 1)
boxes[0, :, 3] = np.clip(boxes[0, :, 3], 0, h - 1)
indices = np.where(scores[0, :] > score_threshold)[0]
boxes = boxes[0, indices]
scores = scores[0, indices]
labels = labels[0, indices]
# parse the filename from the input image path, construct the
# path to the output image, and write the image to disk
filename = imagePath.split(os.path.sep)[-1]
file = open(imagePath[0:imagePath.rfind(".")] + ".xml", "w")
file.write(
generateXML(imagePath[0:imagePath.rfind(".")], imagePath, h, w, 3,
boxes, scores, labels, LABELS))
file.close()
def preprocess(self, frames):
inputs, anchors, meta = [], [], []
for frame in frames:
image = frame[:, :, ::-1]
h, w = image.shape[:2]
image, scale, offset_h, offset_w = preprocess_image(
image, image_size=self.size)
inputs.append(image)
anchors.append(anchors_for_shape((self.size, self.size)))
meta.append((scale, h, w, offset_h, offset_w))
return [np.array(inputs), np.array(anchors)], meta
num_classes = len(classes)
score_threshold = 0.5
colors = [np.random.randint(0, 256, 3).tolist() for i in range(num_classes)]
model, prediction_model = efficientdet(phi=phi,
weighted_bifpn=weighted_bifpn,
num_classes=num_classes,
score_threshold=score_threshold)
prediction_model.load_weights(model_path, by_name=True)
image_path = 'sample.jpg'
image = cv2.imread(image_path)
src_image = image.copy()
image = image[:, :, ::-1]
h, w = image.shape[:2]
image, scale, offset_h, offset_w = preprocess_image(image, image_size=image_size)
inputs = np.expand_dims(image, axis=0)
anchors = anchors_for_shape((image_size, image_size))
# run network
start = time.time()
boxes, scores, labels = prediction_model.predict_on_batch([np.expand_dims(image, axis=0),
np.expand_dims(anchors, axis=0)])
print(time.time() - start)
boxes[0, :, [0, 2]] = boxes[0, :, [0, 2]] - offset_w
boxes[0, :, [1, 3]] = boxes[0, :, [1, 3]] - offset_h
boxes /= scale
boxes[0, :, 0] = np.clip(boxes[0, :, 0], 0, w - 1)
boxes[0, :, 1] = np.clip(boxes[0, :, 1], 0, h - 1)
boxes[0, :, 2] = np.clip(boxes[0, :, 2], 0, w - 1)
boxes[0, :, 3] = np.clip(boxes[0, :, 3], 0, h - 1)