Initial commit

Divers/tutoriel12/detection.py

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+import numpy as np
+import os
+import tensorflow as tf
+import cv2
+
+labels={
+ 1: "person",
+ 2: "bicycle",
+ 3: "car",
+ 4: "motorcycle",
+ 5: "airplane",
+ 6: "bus",
+ 7: "train",
+ 8: "truck",
+ 9: "boat",
+ 10: "traffic light",
+ 11: "fire hydrant",
+ 13: "stop sign",
+ 14: "parking meter",
+ 15: "bench",
+ 16: "bird",
+ 17: "cat",
+ 18: "dog",
+ 19: "horse",
+ 20: "sheep",
+ 21: "cow",
+ 22: "elephant",
+ 23: "bear",
+ 24: "zebra",
+ 25: "giraffe",
+ 27: "backpack",
+ 28: "umbrella",
+ 31: "handbag",
+ 32: "tie",
+ 33: "suitcase",
+ 34: "frisbee",
+ 35: "skis",
+ 36: "snowboard",
+ 37: "sports ball",
+ 38: "kite",
+ 39: "baseball bat",
+ 40: "baseball glove",
+ 41: "skateboard",
+ 42: "surfboard",
+ 43: "tennis racket",
+ 44: "bottle",
+ 46: "wine glass",
+ 47: "cup",
+ 48: "fork",
+ 49: "knife",
+ 50: "spoon",
+ 51: "bowl",
+ 52: "banana",
+ 53: "apple",
+ 54: "sandwich",
+ 55: "orange",
+ 56: "broccoli",
+ 57: "carrot",
+ 58: "hot dog",
+ 59: "pizza",
+ 60: "donut",
+ 61: "cake",
+ 62: "chair",
+ 63: "couch",
+ 64: "potted plant",
+ 65: "bed",
+ 67: "dining table",
+ 70: "toilet",
+ 72: "tv",
+ 73: "laptop",
+ 74: "mouse",
+ 75: "remote",
+ 76: "keyboard",
+ 77: "cell phone",
+ 78: "microwave",
+ 79: "oven",
+ 80: "toaster",
+ 81: "sink",
+ 82: "refrigerator",
+ 84: "book",
+ 85: "clock",
+ 86: "vase",
+ 87: "scissors",
+ 88: "teddy bear",
+ 89: "hair drier",
+ 90: "toothbrush"
+}
+
+MODEL_NAME='ssd_mobilenet_v2_coco_2018_03_29'
+PATH_TO_FROZEN_GRAPH=MODEL_NAME+'/frozen_inference_graph.pb'
+color_infos=(255, 255, 0)
+
+detection_graph=tf.Graph()
+with detection_graph.as_default():
+  od_graph_def=tf.GraphDef()
+  with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:
+    serialized_graph=fid.read()
+    od_graph_def.ParseFromString(serialized_graph)
+    tf.import_graph_def(od_graph_def, name='')
+
+with detection_graph.as_default():
+    with tf.Session() as sess:
+        cap=cv2.VideoCapture(0)
+        ops=tf.get_default_graph().get_operations()
+        all_tensor_names={output.name for op in ops for output in op.outputs}
+        tensor_dict={}
+        for key in [
+            'num_detections', 'detection_boxes', 'detection_scores',
+            'detection_classes', 'detection_masks']:
+            tensor_name=key+':0'
+            if tensor_name in all_tensor_names:
+                tensor_dict[key]=tf.get_default_graph().get_tensor_by_name(tensor_name)
+        if 'detection_masks' in tensor_dict:
+            quit("Masque non géré")
+        image_tensor=tf.get_default_graph().get_tensor_by_name('image_tensor:0')
+
+        while True:
+            ret, frame=cap.read()
+            tickmark=cv2.getTickCount()
+            output_dict=sess.run(tensor_dict, feed_dict={image_tensor: np.expand_dims(frame, 0)})
+            nbr_object=int(output_dict['num_detections'])
+            classes=output_dict['detection_classes'][0].astype(np.uint8)
+            boxes=output_dict['detection_boxes'][0]
+            scores=output_dict['detection_scores'][0]
+            for objet in range(nbr_object):
+                ymin, xmin, ymax, xmax=boxes[objet]
+                if scores[objet]>0.30:
+                    height, width=frame.shape[:2]
+                    xmin=int(xmin*width)
+                    xmax=int(xmax*width)
+                    ymin=int(ymin*height)
+                    ymax=int(ymax*height)
+                    cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color_infos, 1)
+                    txt="{:s}:{:3.0%}".format(labels[classes[objet]], scores[objet])
+                    cv2.putText(frame, txt, (xmin, ymin-5), cv2.FONT_HERSHEY_PLAIN, 1, color_infos, 2)
+            fps=cv2.getTickFrequency()/(cv2.getTickCount()-tickmark)
+            cv2.putText(frame, "FPS: {:05.2f}".format(fps), (10, 20), cv2.FONT_HERSHEY_PLAIN, 1, color_infos, 2)
+            cv2.imshow('image', frame)
+            key=cv2.waitKey(1)&0xFF
+            if key==ord('a'):
+                for objet in range(500):
+                    ret, frame=cap.read()
+            if key==ord('q'):
+              break
+cap.release()
+cv2.destroyAllWindows()