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Commit4d9d332

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updated face detection tutorial to use SSDs as well
1 parent15e18cb commit4d9d332

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importcv2
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importnumpyasnp
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# https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt
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prototxt_path="weights/deploy.prototxt.txt"
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# https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel
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model_path="weights/res10_300x300_ssd_iter_140000_fp16.caffemodel"
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# load Caffe model
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model=cv2.dnn.readNetFromCaffe(prototxt_path,model_path)
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# read the desired image
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image=cv2.imread("kids.jpg")
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# get width and height of the image
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h,w=image.shape[:2]
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# preprocess the image: resize and performs mean subtraction
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blob=cv2.dnn.blobFromImage(image,1.0, (300,300), (104.0,177.0,123.0))
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# set the image into the input of the neural network
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model.setInput(blob)
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# perform inference and get the result
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output=np.squeeze(model.forward())
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font_scale=1.0
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foriinrange(0,output.shape[0]):
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# get the confidence
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confidence=output[i,2]
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# if confidence is above 50%, then draw the surrounding box
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ifconfidence>0.5:
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# get the surrounding box cordinates and upscale them to original image
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box=output[i,3:7]*np.array([w,h,w,h])
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# convert to integers
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start_x,start_y,end_x,end_y=box.astype(np.int)
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# draw the rectangle surrounding the face
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cv2.rectangle(image, (start_x,start_y), (end_x,end_y),color=(255,0,0),thickness=2)
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# draw text as well
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cv2.putText(image,f"{confidence*100:.2f}%", (start_x,start_y-5),cv2.FONT_HERSHEY_SIMPLEX,font_scale, (255,0,0),2)
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# show the image
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cv2.imshow("image",image)
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cv2.waitKey(0)
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# save the image with rectangles
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cv2.imwrite("kids_detected_dnn.jpg",image)
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importcv2
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importnumpyasnp
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# https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt
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prototxt_path="weights/deploy.prototxt.txt"
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# https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel
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model_path="weights/res10_300x300_ssd_iter_140000_fp16.caffemodel"
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# load Caffe model
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model=cv2.dnn.readNetFromCaffe(prototxt_path,model_path)
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cap=cv2.VideoCapture(0)
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whileTrue:
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# read the desired image
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_,image=cap.read()
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# get width and height of the image
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h,w=image.shape[:2]
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# preprocess the image: resize and performs mean subtraction
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blob=cv2.dnn.blobFromImage(image,1.0, (300,300), (104.0,177.0,123.0))
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# set the image into the input of the neural network
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model.setInput(blob)
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# perform inference and get the result
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output=np.squeeze(model.forward())
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font_scale=1.0
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foriinrange(0,output.shape[0]):
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# get the confidence
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confidence=output[i,2]
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# if confidence is above 50%, then draw the surrounding box
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ifconfidence>0.5:
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# get the surrounding box cordinates and upscale them to original image
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box=output[i,3:7]*np.array([w,h,w,h])
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# convert to integers
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start_x,start_y,end_x,end_y=box.astype(np.int)
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# draw the rectangle surrounding the face
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cv2.rectangle(image, (start_x,start_y), (end_x,end_y),color=(255,0,0),thickness=2)
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# draw text as well
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cv2.putText(image,f"{confidence*100:.2f}%", (start_x,start_y-5),cv2.FONT_HERSHEY_SIMPLEX,font_scale, (255,0,0),2)
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# show the image
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cv2.imshow("image",image)
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ifcv2.waitKey(1)==ord("q"):
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break
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cv2.destroyAllWindows()
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cap.release()

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