Merge branch 'master' into main

coco.names

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+person
+bicycle
+car
+motorcycle
+airplane
+bus
+train
+truck
+boat
+traffic light
+fire hydrant
+street sign
+stop sign
+parking meter
+bench
+bird
+cat
+dog
+horse
+sheep
+cow
+elephant
+bear
+zebra
+giraffe
+hat
+backpack
+umbrella
+shoe
+eye glasses
+handbag
+tie
+suitcase
+frisbee
+skis
+snowboard
+sports ball
+kite
+baseball bat
+baseball glove
+skateboard
+surfboard
+tennis racket
+bottle
+plate
+wine glass
+cup
+fork
+knife
+spoon
+bowl
+banana
+apple
+sandwich
+orange
+broccoli
+carrot
+hot dog
+pizza
+donut
+cake
+chair
+couch
+potted plant
+bed
+mirror
+dining table
+window
+desk
+toilet
+door
+tv
+laptop
+mouse
+remote
+keyboard
+cell phone
+microwave
+oven
+toaster
+sink
+refrigerator
+blender
+book
+clock
+vase
+scissors
+teddy bear
+hair drier
+toothbrush
+hair brush

main.py

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+import cv2
+
+thres = 0.45  # Threshold to detect object
+
+cap = cv2.VideoCapture(1)
+cap.set(3, 1280)
+cap.set(4, 720)
+cap.set(10, 70)
+
+classNames = []
+classFile = 'coco.names'
+with open(classFile,'rt') as f:
+    classNames = f.read().rstrip('\n').split('\n')
+    print(classNames)
+
+    configPath = 'ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt'
+    weightsPath = 'frozen_inference_graph.pb'
+
+    net = cv2.dnn_DetectionModel(weightsPath, configPath)
+    net.setInputSize(320, 320)
+    net.setInputScale(1.0 / 127.5)
+    net.setInputMean((127.5, 127.5, 127.5))
+    net.setInputSwapRB(True)
+
+    while True:
+        success, img = cap.read()
+        img = cv2.imread("box_photo.jpeg")
+
+        classIds, confs, bbox = net.detect(img, confThreshold=thres)
+        print(classIds, bbox)
+
+        if len(classIds) != 0:
+            for classId, confidence, box in zip(classIds.flatten(), confs.flatten(), bbox):
+                cv2.rectangle(img, box, color=(0, 255, 0), thickness=2)
+                cv2.putText(img, classNames[classId - 1].upper(), (box[0] + 10, box[1] + 30),
+                            cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 2)
+                cv2.putText(img, str(round(confidence * 100, 2)), (box[0] + 200, box[1] + 30),
+                            cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 2)
+
+        cv2.imshow("Output", img)
+        cv2.waitKey(1)