Machine-Learning/Video_Classification.py at main · PICT-Chunked-Backup-Project/Machine-Learning · GitHub

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import cv2
import matplotlib.pyplot as plt

# config_file =r"C:\Users\dell\Desktop\machine learning\Object Detection\ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt"
# frozen_model = r"C:\Users\dell\Desktop\machine learning\Object Detection\frozen_inference_graph.pb"
# model = cv2.dnn_DetectionModel(frozen_model,config_file)

# classLabels = []
# file_name = r"C:\Users\dell\Desktop\machine learning\Object Detection\Labels.txt"
# with open(file_name,'rt') as fpt:
#     classLabels = fpt.read().rstrip('\n').split('\n')
# model.setInputSize(320,320)
# model.setInputScale(1.0/127.5)
# model.setInputMean(127.5)
# model.setInputSwapRB(True)

class image_classifier:
    config_file =""
    frozen_model = ""
    label_path=""
    def __init__(self,p,c,f,l):
        self.path = p
        self.label_path=l
        self.config_file=c
        self.frozen_model=f


    def classify(self):
        model=cv2.dnn_DetectionModel(self.frozen_model,self.config_file)
        tags=""
        cap = cv2.VideoCapture(self.path)

        if not cap.isOpened():
            cap = cv2.VideoCapture(0)
        if not cap.isOpened():
            raise IOError("Cannot open video")
        classLabels = []
        with open(self.label_path,'rt') as fpt:
            classLabels = fpt.read().rstrip('\n').split('\n')
        model.setInputSize(320,320)
        model.setInputScale(1.0/127.5)
        model.setInputMean(127.5)
        model.setInputSwapRB(True)
        font_scale = 3
        font = cv2.FONT_HERSHEY_SIMPLEX

        while True:
            ret,frame = cap.read()

            ClassIndex, confidence, bbox = model.detect(frame,confThreshold=0.55)

#             print(ClassIndex)
            if (len(ClassIndex)==0):
                return("None")
            else:
#                 for ClassInd, conf, boxes in zip(ClassIndex.flatten(), confidence.flatten(), bbox):
#                     if(ClassInd<=80):
#                         cv2.rectangle(frame,boxes,(255, 0, 0), 2 )
#                         cv2.putText(frame,classLabels[ClassInd-1],(boxes[0]+10,boxes[1]+40), font, fontScale=font_scale,color=(0,255,0),thickness=3)

#                 cv2.imshow('Object Detection Tutorial', frame)
#                 if cv2.waitKey(2) & 0xFF == ord('q'):
#                     break
#             cap.release()
#             cv2.destroyAllWindows()

                for ClassInd, conf, boxes in zip(set(ClassIndex.flatten()), confidence.flatten(), bbox):
                    if(ClassInd<=80):
                        tags = classLabels[ClassInd-1]
                        print((classLabels[ClassInd-1]))
                return tags

# if __name__ == "__main__":
#     c=image_classifier(r"C:\Users\dell\Desktop\machine learning\Object Detection\icv.mp4",r"C:\Users\dell\Desktop\machine learning\Object Detection\ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt",r"C:\Users\dell\Desktop\machine learning\Object Detection\frozen_inference_graph.pb",r"C:\Users\dell\Desktop\machine learning\Object Detection\Labels.txt")
#     c.classify()