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Commit 91f6e16

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model exporter + raccoon detection demo
1 parent 4cde8d3 commit 91f6e16

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14 files changed

+308
-58
lines changed

14 files changed

+308
-58
lines changed

‎README.md

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‎data/samples/clean_raccoon.mp4

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‎detect_objects.py

Lines changed: 2 additions & 2 deletions
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@@ -13,7 +13,7 @@ def DetectFromVideo(detector, Video_path, save_output=False, output_dir='output/
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output_path = os.path.join(output_dir, 'detection_'+ Video_path.split("/")[-1])
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frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), 25, (frame_width, frame_height))
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out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), 30, (frame_width, frame_height))
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while (cap.isOpened()):
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ret, img = cap.read()
@@ -65,7 +65,7 @@ def DetectImagesFromFolder(detector, images_dir, save_output=False, output_dir='
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parser.add_argument('--threshold', help='Detection Threshold', type=float, default=0.4)
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parser.add_argument('--images_dir', help='Directory to input images)', default='data/samples/images/')
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parser.add_argument('--video_path', help='Path to input video)', default='data/samples/pedestrian_test.mp4')
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parser.add_argument('--output_directory', help='Path to output images and video', default='data/output')
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parser.add_argument('--output_directory', help='Path to output images and video', default='data/samples/output')
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parser.add_argument('--video_input', help='Flag for video input, default: False', action='store_true') # default is false
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parser.add_argument('--save_output', help='Flag for save images and video with detections visualized, default: False',
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action='store_true') # default is false

‎detector.py

Lines changed: 5 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -58,13 +58,14 @@ def DisplayDetections(self, image, boxes_list, det_time=None):
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score = str(np.round(boxes_list[idx][-1], 2))
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text = cls + ": " + score
61-
cv2.rectangle(img, (x_min, y_min), (x_max, y_max), (0, 255, 0), 2)
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cv2.rectangle(img, (x_min, y_min), (x_max, y_max), (0, 255, 0), 1)
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cv2.rectangle(img, (x_min, y_min - 20), (x_min, y_min), (255, 255, 255), -1)
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cv2.putText(img, text, (x_min + 5, y_min - 7), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1)
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cv2.putText(img, text, (x_min + 5, y_min - 7), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
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if det_time != None:
66-
elapsed_time = str(det_time) + " ms"
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cv2.putText(img, elapsed_time, (25, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 0), 2)
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fps = round(1000. / det_time, 1)
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fps_txt = str(fps) + " FPS"
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cv2.putText(img, fps_txt, (25, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 0), 2)
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return img
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model_checkpoint_path: "ckpt-0"
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all_model_checkpoint_paths: "ckpt-0"
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all_model_checkpoint_timestamps: 1595859041.5022295
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last_preserved_timestamp: 1595859040.4026093
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model {
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ssd {
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num_classes: 1
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image_resizer {
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fixed_shape_resizer {
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height: 300
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width: 300
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}
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}
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feature_extractor {
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type: "ssd_mobilenet_v2_keras"
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depth_multiplier: 1.0
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min_depth: 16
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conv_hyperparams {
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regularizer {
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l2_regularizer {
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weight: 4e-05
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}
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}
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initializer {
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truncated_normal_initializer {
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mean: 0.0
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stddev: 0.03
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}
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}
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activation: RELU_6
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batch_norm {
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decay: 0.97
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center: true
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scale: true
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epsilon: 0.001
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train: true
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}
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}
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override_base_feature_extractor_hyperparams: true
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}
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box_coder {
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faster_rcnn_box_coder {
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y_scale: 10.0
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x_scale: 10.0
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height_scale: 5.0
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width_scale: 5.0
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}
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}
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matcher {
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argmax_matcher {
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matched_threshold: 0.4
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unmatched_threshold: 0.4
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ignore_thresholds: false
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negatives_lower_than_unmatched: true
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force_match_for_each_row: true
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use_matmul_gather: true
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}
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}
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similarity_calculator {
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iou_similarity {
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}
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}
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box_predictor {
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convolutional_box_predictor {
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conv_hyperparams {
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regularizer {
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l2_regularizer {
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weight: 4e-05
65+
}
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}
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initializer {
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random_normal_initializer {
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mean: 0.0
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stddev: 0.01
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}
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}
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activation: RELU_6
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batch_norm {
75+
decay: 0.97
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center: true
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scale: true
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epsilon: 0.001
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train: true
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}
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}
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min_depth: 0
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max_depth: 0
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num_layers_before_predictor: 0
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use_dropout: false
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dropout_keep_probability: 0.8
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kernel_size: 1
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box_code_size: 4
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apply_sigmoid_to_scores: false
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class_prediction_bias_init: -4.6
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}
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}
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anchor_generator {
94+
ssd_anchor_generator {
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num_layers: 6
96+
min_scale: 0.15
97+
max_scale: 0.95
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aspect_ratios: 1.0
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aspect_ratios: 2.0
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aspect_ratios: 0.5
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}
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}
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post_processing {
104+
batch_non_max_suppression {
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score_threshold: 0.001
106+
iou_threshold: 0.4
107+
max_detections_per_class: 100
108+
max_total_detections: 100
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}
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score_converter: SIGMOID
111+
}
112+
normalize_loss_by_num_matches: true
113+
loss {
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localization_loss {
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weighted_smooth_l1 {
116+
delta: 1.0
117+
}
118+
}
119+
classification_loss {
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weighted_sigmoid_focal {
121+
gamma: 2.0
122+
alpha: 0.75
123+
}
124+
}
125+
classification_weight: 1.0
126+
localization_weight: 1.0
127+
}
128+
encode_background_as_zeros: true
129+
normalize_loc_loss_by_codesize: true
130+
inplace_batchnorm_update: true
131+
freeze_batchnorm: false
132+
}
133+
}
134+
train_config {
135+
batch_size: 16
136+
data_augmentation_options {
137+
random_horizontal_flip {
138+
}
139+
}
140+
data_augmentation_options {
141+
ssd_random_crop {
142+
}
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}
144+
sync_replicas: true
145+
optimizer {
146+
momentum_optimizer {
147+
learning_rate {
148+
cosine_decay_learning_rate {
149+
learning_rate_base: 0.025
150+
total_steps: 3000
151+
warmup_learning_rate: 0.005
152+
warmup_steps: 100
153+
}
154+
}
155+
momentum_optimizer_value: 0.9
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}
157+
use_moving_average: false
158+
}
159+
fine_tune_checkpoint: "../models/ssd_mobilenet_v2_320x320_coco17_tpu-8/checkpoint/ckpt-0"
160+
num_steps: 3000
161+
startup_delay_steps: 0.0
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replicas_to_aggregate: 8
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max_number_of_boxes: 100
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unpad_groundtruth_tensors: false
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fine_tune_checkpoint_type: "detection"
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use_bfloat16: false
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fine_tune_checkpoint_version: V2
168+
}
169+
train_input_reader {
170+
label_map_path: "../models/raccoon_labelmap.pbtxt"
171+
tf_record_input_reader {
172+
input_path: "../data/raccoon_data/train.record"
173+
}
174+
}
175+
eval_config {
176+
metrics_set: "coco_detection_metrics"
177+
use_moving_averages: false
178+
}
179+
eval_input_reader {
180+
label_map_path: "../models/raccoon_labelmap.pbtxt"
181+
shuffle: false
182+
num_epochs: 1
183+
tf_record_input_reader {
184+
input_path: "../data/raccoon_data/val.record"
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}
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}

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