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import tensorflow as tf |
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physical_devices = tf.config.experimental.list_physical_devices('GPU') |
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if len(physical_devices) > 0: |
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tf.config.experimental.set_memory_growth(physical_devices[0], True) |
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from absl import app, flags, logging |
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from absl.flags import FLAGS |
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import core.utils as utils |
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from core.yolov4 import filter_boxes |
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from tensorflow.python.saved_model import tag_constants |
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from PIL import Image |
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import cv2 |
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import numpy as np |
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from tensorflow.compat.v1 import ConfigProto |
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from tensorflow.compat.v1 import InteractiveSession |
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flags.DEFINE_string('framework', 'tf', '(tf, tflite, trt') |
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flags.DEFINE_string('weights', './checkpoints/yolov4-416', |
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'path to weights file') |
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flags.DEFINE_integer('size', 416, 'resize images to') |
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flags.DEFINE_boolean('tiny', False, 'yolo or yolo-tiny') |
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flags.DEFINE_string('model', 'yolov4', 'yolov3 or yolov4') |
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flags.DEFINE_string('image', './data/kite.jpg', 'path to input image') |
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flags.DEFINE_string('output', 'result.png', 'path to output image') |
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flags.DEFINE_float('iou', 0.45, 'iou threshold') |
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flags.DEFINE_float('score', 0.25, 'score threshold') |
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def main(_argv): |
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config = ConfigProto() |
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config.gpu_options.allow_growth = True |
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session = InteractiveSession(config=config) |
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STRIDES, ANCHORS, NUM_CLASS, XYSCALE = utils.load_config(FLAGS) |
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input_size = FLAGS.size |
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image_path = FLAGS.image |
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original_image = cv2.imread(image_path) |
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original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB) |
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image_data = cv2.resize(original_image, (input_size, input_size)) |
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image_data = image_data / 255. |
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images_data = [] |
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for i in range(1): |
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images_data.append(image_data) |
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images_data = np.asarray(images_data).astype(np.float32) |
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if FLAGS.framework == 'tflite': |
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interpreter = tf.lite.Interpreter(model_path=FLAGS.weights) |
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interpreter.allocate_tensors() |
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input_details = interpreter.get_input_details() |
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output_details = interpreter.get_output_details() |
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print(input_details) |
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print(output_details) |
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interpreter.set_tensor(input_details[0]['index'], images_data) |
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interpreter.invoke() |
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pred = [interpreter.get_tensor(output_details[i]['index']) for i in range(len(output_details))] |
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if FLAGS.model == 'yolov3' and FLAGS.tiny == True: |
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boxes, pred_conf = filter_boxes(pred[1], pred[0], score_threshold=0.25, input_shape=tf.constant([input_size, input_size])) |
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else: |
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boxes, pred_conf = filter_boxes(pred[0], pred[1], score_threshold=0.25, input_shape=tf.constant([input_size, input_size])) |
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else: |
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saved_model_loaded = tf.saved_model.load(FLAGS.weights, tags=[tag_constants.SERVING]) |
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infer = saved_model_loaded.signatures['serving_default'] |
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batch_data = tf.constant(images_data) |
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pred_bbox = infer(batch_data) |
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for key, value in pred_bbox.items(): |
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boxes = value[:, :, 0:4] |
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pred_conf = value[:, :, 4:] |
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boxes, scores, classes, valid_detections = tf.image.combined_non_max_suppression( |
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boxes=tf.reshape(boxes, (tf.shape(boxes)[0], -1, 1, 4)), |
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scores=tf.reshape( |
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pred_conf, (tf.shape(pred_conf)[0], -1, tf.shape(pred_conf)[-1])), |
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max_output_size_per_class=50, |
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max_total_size=50, |
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iou_threshold=FLAGS.iou, |
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score_threshold=FLAGS.score |
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) |
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pred_bbox = [boxes.numpy(), scores.numpy(), classes.numpy(), valid_detections.numpy()] |
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image = utils.draw_bbox(original_image, pred_bbox) |
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image = Image.fromarray(image.astype(np.uint8)) |
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image.show() |
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image = cv2.cvtColor(np.array(image), cv2.COLOR_BGR2RGB) |
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cv2.imwrite(FLAGS.output, image) |
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if __name__ == '__main__': |
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try: |
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app.run(main) |
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except SystemExit: |
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pass |
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