Initial commit

README.md

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+TO get this code to work
+
+1. clone models
+
+    git clone https://github.com/tensorflow/models
+
+2. run [`protoc`](http://google.github.io/proto-lens/installing-protoc.html) in `models/research`
+
+    protoc object_detection/protos/*.proto --python_out=.
+
+3. Add models/research into PYTHONPATH
+
+    export PYTHONPATH=/home/sipp11/dev/tf1-test/models/research:$PYTHONPATH
+
+4. Copy `mscoco_label_map.pbtxt` from `/models/research/object_detection/data` to `./data/` However, `.pbtxt` is just an id & label conversion as following:
+
+    item {
+      id: 1
+      display_name: "person"
+    }
+    item {
+      id: 2
+      display_name: "bicycle"
+    }
+
+5. It should work!

data/mscoco_label_map.pbtxt

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+item {
+  id: 1
+  display_name: "person"
+}
+item {
+  id: 2
+  display_name: "bicycle"
+}
+item {
+  id: 3
+  display_name: "car"
+}
+item {
+  id: 4
+  display_name: "motorcycle"
+}
+item {
+  id: 5
+  display_name: "airplane"
+}
+item {
+  id: 6
+  display_name: "bus"
+}
+item {
+  name: "/m/07jdr"
+  id: 7
+  display_name: "train"
+}
+item {
+  name: "/m/07r04"
+  id: 8
+  display_name: "truck"
+}
+item {
+  name: "/m/019jd"
+  id: 9
+  display_name: "boat"
+}
+item {
+  name: "/m/015qff"
+  id: 10
+  display_name: "traffic light"
+}
+item {
+  name: "/m/01pns0"
+  id: 11
+  display_name: "fire hydrant"
+}
+item {
+  name: "/m/02pv19"
+  id: 13
+  display_name: "stop sign"
+}
+item {
+  name: "/m/015qbp"
+  id: 14
+  display_name: "parking meter"
+}
+item {
+  name: "/m/0cvnqh"
+  id: 15
+  display_name: "bench"
+}
+item {
+  name: "/m/015p6"
+  id: 16
+  display_name: "bird"
+}
+item {
+  name: "/m/01yrx"
+  id: 17
+  display_name: "cat"
+}
+item {
+  name: "/m/0bt9lr"
+  id: 18
+  display_name: "dog"
+}
+item {
+  name: "/m/03k3r"
+  id: 19
+  display_name: "horse"
+}
+item {
+  name: "/m/07bgp"
+  id: 20
+  display_name: "sheep"
+}
+item {
+  name: "/m/01xq0k1"
+  id: 21
+  display_name: "cow"
+}
+item {
+  name: "/m/0bwd_0j"
+  id: 22
+  display_name: "elephant"
+}
+item {
+  name: "/m/01dws"
+  id: 23
+  display_name: "bear"
+}
+item {
+  name: "/m/0898b"
+  id: 24
+  display_name: "zebra"
+}
+item {
+  name: "/m/03bk1"
+  id: 25
+  display_name: "giraffe"
+}
+item {
+  name: "/m/01940j"
+  id: 27
+  display_name: "backpack"
+}
+item {
+  name: "/m/0hnnb"
+  id: 28
+  display_name: "umbrella"
+}
+item {
+  name: "/m/080hkjn"
+  id: 31
+  display_name: "handbag"
+}
+item {
+  name: "/m/01rkbr"
+  id: 32
+  display_name: "tie"
+}
+item {
+  name: "/m/01s55n"
+  id: 33
+  display_name: "suitcase"
+}
+item {
+  name: "/m/02wmf"
+  id: 34
+  display_name: "frisbee"
+}
+item {
+  name: "/m/071p9"
+  id: 35
+  display_name: "skis"
+}
+item {
+  name: "/m/06__v"
+  id: 36
+  display_name: "snowboard"
+}
+item {
+  name: "/m/018xm"
+  id: 37
+  display_name: "sports ball"
+}
+item {
+  name: "/m/02zt3"
+  id: 38
+  display_name: "kite"
+}
+item {
+  name: "/m/03g8mr"
+  id: 39
+  display_name: "baseball bat"
+}
+item {
+  name: "/m/03grzl"
+  id: 40
+  display_name: "baseball glove"
+}
+item {
+  name: "/m/06_fw"
+  id: 41
+  display_name: "skateboard"
+}
+item {
+  name: "/m/019w40"
+  id: 42
+  display_name: "surfboard"
+}
+item {
+  name: "/m/0dv9c"
+  id: 43
+  display_name: "tennis racket"
+}
+item {
+  name: "/m/04dr76w"
+  id: 44
+  display_name: "bottle"
+}
+item {
+  name: "/m/09tvcd"
+  id: 46
+  display_name: "wine glass"
+}
+item {
+  name: "/m/08gqpm"
+  id: 47
+  display_name: "cup"
+}
+item {
+  name: "/m/0dt3t"
+  id: 48
+  display_name: "fork"
+}
+item {
+  name: "/m/04ctx"
+  id: 49
+  display_name: "knife"
+}
+item {
+  name: "/m/0cmx8"
+  id: 50
+  display_name: "spoon"
+}
+item {
+  name: "/m/04kkgm"
+  id: 51
+  display_name: "bowl"
+}
+item {
+  name: "/m/09qck"
+  id: 52
+  display_name: "banana"
+}
+item {
+  name: "/m/014j1m"
+  id: 53
+  display_name: "apple"
+}
+item {
+  name: "/m/0l515"
+  id: 54
+  display_name: "sandwich"
+}
+item {
+  name: "/m/0cyhj_"
+  id: 55
+  display_name: "orange"
+}
+item {
+  name: "/m/0hkxq"
+  id: 56
+  display_name: "broccoli"
+}
+item {
+  name: "/m/0fj52s"
+  id: 57
+  display_name: "carrot"
+}
+item {
+  name: "/m/01b9xk"
+  id: 58
+  display_name: "hot dog"
+}
+item {
+  name: "/m/0663v"
+  id: 59
+  display_name: "pizza"
+}
+item {
+  name: "/m/0jy4k"
+  id: 60
+  display_name: "donut"
+}
+item {
+  name: "/m/0fszt"
+  id: 61
+  display_name: "cake"
+}
+item {
+  name: "/m/01mzpv"
+  id: 62
+  display_name: "chair"
+}
+item {
+  name: "/m/02crq1"
+  id: 63
+  display_name: "couch"
+}
+item {
+  name: "/m/03fp41"
+  id: 64
+  display_name: "potted plant"
+}
+item {
+  name: "/m/03ssj5"
+  id: 65
+  display_name: "bed"
+}
+item {
+  name: "/m/04bcr3"
+  id: 67
+  display_name: "dining table"
+}
+item {
+  name: "/m/09g1w"
+  id: 70
+  display_name: "toilet"
+}
+item {
+  name: "/m/07c52"
+  id: 72
+  display_name: "tv"
+}
+item {
+  name: "/m/01c648"
+  id: 73
+  display_name: "laptop"
+}
+item {
+  name: "/m/020lf"
+  id: 74
+  display_name: "mouse"
+}
+item {
+  name: "/m/0qjjc"
+  id: 75
+  display_name: "remote"
+}
+item {
+  name: "/m/01m2v"
+  id: 76
+  display_name: "keyboard"
+}
+item {
+  name: "/m/050k8"
+  id: 77
+  display_name: "cell phone"
+}
+item {
+  name: "/m/0fx9l"
+  id: 78
+  display_name: "microwave"
+}
+item {
+  name: "/m/029bxz"
+  id: 79
+  display_name: "oven"
+}
+item {
+  name: "/m/01k6s3"
+  id: 80
+  display_name: "toaster"
+}
+item {
+  name: "/m/0130jx"
+  id: 81
+  display_name: "sink"
+}
+item {
+  name: "/m/040b_t"
+  id: 82
+  display_name: "refrigerator"
+}
+item {
+  name: "/m/0bt_c3"
+  id: 84
+  display_name: "book"
+}
+item {
+  name: "/m/01x3z"
+  id: 85
+  display_name: "clock"
+}
+item {
+  name: "/m/02s195"
+  id: 86
+  display_name: "vase"
+}
+item {
+  name: "/m/01lsmm"
+  id: 87
+  display_name: "scissors"
+}
+item {
+  name: "/m/0kmg4"
+  id: 88
+  display_name: "teddy bear"
+}
+item {
+  name: "/m/03wvsk"
+  id: 89
+  display_name: "hair drier"
+}
+item {
+  name: "/m/012xff"
+  id: 90
+  display_name: "toothbrush"
+}

pip.txt

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+Cython
+contextlib2
+pillow
+lxml
+jupyter
+matplotlib
+protobuf
+tensorflow==1.14.0

run.py

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+import numpy as np
+import os
+import six.moves.urllib as urllib
+import sys
+import tarfile
+import tensorflow as tf
+import zipfile
+
+from collections import defaultdict
+from io import StringIO
+from matplotlib import pyplot as plt
+from PIL import Image
+
+import cv2
+
+# cap = cv2.VideoCapture(0)
+cap = cv2.VideoCapture("/home/sipp11/Desktop/5min.mp4")
+
+
+sys.path.append("..")
+
+from utils import label_map_util
+
+from utils import visualization_utils as vis_util
+
+MODEL_NAME = "ssd_mobilenet_v1_coco_11_06_2017"
+MODEL_FILE = MODEL_NAME + ".tar.gz"
+DOWNLOAD_BASE = "http://download.tensorflow.org/models/object_detection/"
+
+# Path to frozen detection graph. This is the actual model that is used for the object detection.
+PATH_TO_CKPT = MODEL_NAME + "/frozen_inference_graph.pb"
+
+# List of the strings that is used to add correct label for each box.
+PATH_TO_LABELS = os.path.join("data", "mscoco_label_map.pbtxt")
+
+NUM_CLASSES = 90
+
+opener = urllib.request.URLopener()
+opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)
+tar_file = tarfile.open(MODEL_FILE)
+for file in tar_file.getmembers():
+    file_name = os.path.basename(file.name)
+    if "frozen_inference_graph.pb" in file_name:
+        tar_file.extract(file, os.getcwd())
+
+detection_graph = tf.Graph()
+with detection_graph.as_default():
+    od_graph_def = tf.compat.v1.GraphDef()
+    with tf.io.gfile.GFile(PATH_TO_CKPT, "rb") as fid:
+        serialized_graph = fid.read()
+        od_graph_def.ParseFromString(serialized_graph)
+        tf.import_graph_def(od_graph_def, name="")
+
+label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
+categories = label_map_util.convert_label_map_to_categories(
+    label_map, max_num_classes=NUM_CLASSES, use_display_name=True
+)
+category_index = label_map_util.create_category_index(categories)
+
+with detection_graph.as_default():
+    with tf.compat.v1.Session(graph=detection_graph) as sess:
+        while True:
+            ret, image_np = cap.read()
+            # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
+            image_np_expanded = np.expand_dims(image_np, axis=0)
+            image_tensor = detection_graph.get_tensor_by_name("image_tensor:0")
+            # Each box represents a part of the image where a particular object was detected.
+            boxes = detection_graph.get_tensor_by_name("detection_boxes:0")
+            # Each score represent how level of confidence for each of the objects.
+            # Score is shown on the result image, together with the class label.
+            scores = detection_graph.get_tensor_by_name("detection_scores:0")
+            classes = detection_graph.get_tensor_by_name("detection_classes:0")
+            num_detections = detection_graph.get_tensor_by_name("num_detections:0")
+            # Actual detection.
+            (boxes, scores, classes, num_detections) = sess.run(
+                [boxes, scores, classes, num_detections],
+                feed_dict={image_tensor: image_np_expanded},
+            )
+            # Visualization of the results of a detection.
+            vis_util.visualize_boxes_and_labels_on_image_array(
+                image_np,
+                np.squeeze(boxes),
+                np.squeeze(classes).astype(np.int32),
+                np.squeeze(scores),
+                category_index,
+                use_normalized_coordinates=True,
+                line_thickness=8,
+            )
+
+            cv2.imshow("object detection", cv2.resize(image_np, (800, 600)))
+            if cv2.waitKey(25) & 0xFF == ord("q"):
+                cv2.destroyAllWindows()
+                break