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added blurring faces in images/videos tutorial · wesexcorp/pythoncode-tutorials@c6a81da · GitHub

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added blurring faces in images/videos tutorial
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‎README.md‎

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@@ -42,6 +42,7 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy
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- [How to Recognize Optical Characters in Images in Python](https://www.thepythoncode.com/article/optical-character-recognition-pytesseract-python). ([code](machine-learning/optical-character-recognition))
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- [How to Use K-Means Clustering for Image Segmentation using OpenCV in Python](https://www.thepythoncode.com/article/kmeans-for-image-segmentation-opencv-python). ([code](machine-learning/kmeans-image-segmentation))
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- [How to Perform YOLO Object Detection using OpenCV and PyTorch in Python](https://www.thepythoncode.com/article/yolo-object-detection-with-opencv-and-pytorch-in-python). ([code](machine-learning/object-detection))
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- [How to Blur Faces in Images using OpenCV in Python](https://www.thepythoncode.com/article/blur-faces-in-images-using-opencv-in-python). ([code](machine-learning/blur-faces))
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- [Building a Speech Emotion Recognizer using Scikit-learn](https://www.thepythoncode.com/article/building-a-speech-emotion-recognizer-using-sklearn). ([code](machine-learning/speech-emotion-recognition))
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- [How to Convert Speech to Text in Python](https://www.thepythoncode.com/article/using-speech-recognition-to-convert-speech-to-text-python). ([code](machine-learning/speech-recognition))
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- [Top 8 Python Libraries For Data Scientists and Machine Learning Engineers](https://www.thepythoncode.com/article/top-python-libraries-for-data-scientists).

‎machine-learning/blur-faces/README.md‎

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# [How to Blur Faces in Images using OpenCV in Python](https://www.thepythoncode.com/article/blur-faces-in-images-using-opencv-in-python)
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To run this:
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- `pip3 install -r requirements.txt`
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- To blur faces of the image `father-and-daughter.jpg`:
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```
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python blur_faces.py a-man-with-little-girl.jpg
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```
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This should show the blurred image and save it of the name `image_blurred.jpg` in your current directory.
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- To blur faces using your live camera:
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```
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python blur_faces_live.py
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```
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- To blur faces of a video:
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```
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python blur_faces_video.py video.3gp
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```

‎machine-learning/blur-faces/blur_faces.py‎

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import cv2
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import numpy as np
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import sys
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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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# get the image file name from the command line
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image_file = sys.argv[1]
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# read the desired image
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image = cv2.imread(image_file)
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# get width and height of the image
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h, w = image.shape[:2]
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# gaussian blur kernel size depends on width and height of original image
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kernel_width = (w // 7) | 1
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kernel_height = (h // 7) | 1
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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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for i in range(0, output.shape[0]):
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confidence = output[i, 2]
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# get the confidence
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# if confidence is above 40%, then blur the bounding box (face)
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if confidence > 0.4:
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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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# get the face image
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face = image[start_y: end_y, start_x: end_x]
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# apply gaussian blur to this face
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face = cv2.GaussianBlur(face, (kernel_width, kernel_height), 0)
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# put the blurred face into the original image
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image[start_y: end_y, start_x: end_x] = face
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cv2.imshow("image", image)
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cv2.waitKey(0)
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cv2.imwrite("image_blurred.jpg", image)

‎machine-learning/blur-faces/blur_faces_live.py‎

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import cv2
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import numpy as np
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import time
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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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while True:
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start = time.time()
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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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kernel_width = (w // 7) | 1
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kernel_height = (h // 7) | 1
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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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for i in range(0, output.shape[0]):
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confidence = output[i, 2]
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# get the confidence
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# if confidence is above 40%, then blur the bounding box (face)
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if confidence > 0.4:
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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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# get the face image
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face = image[start_y: end_y, start_x: end_x]
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# apply gaussian blur to this face
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face = cv2.GaussianBlur(face, (kernel_width, kernel_height), 0)
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# put the blurred face into the original image
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image[start_y: end_y, start_x: end_x] = face
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cv2.imshow("image", image)
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if cv2.waitKey(1) == ord("q"):
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break
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time_elapsed = time.time() - start
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fps = 1 / time_elapsed
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print("FPS:", fps)
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cv2.destroyAllWindows()
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cap.release()

‎machine-learning/blur-faces/blur_faces_video.py‎

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import cv2
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import numpy as np
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import time
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import sys
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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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# get video file from command line
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video_file = sys.argv[1]
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# capture frames from video
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cap = cv2.VideoCapture(video_file)
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fourcc = cv2.VideoWriter_fourcc(*"XVID")
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_, image = cap.read()
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print(image.shape)
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out = cv2.VideoWriter("output.avi", fourcc, 20.0, (image.shape[1], image.shape[0]))
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while True:
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start = time.time()
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captured, image = cap.read()
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# get width and height of the image
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if not captured:
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break
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h, w = image.shape[:2]
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kernel_width = (w // 7) | 1
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kernel_height = (h // 7) | 1
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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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for i in range(0, output.shape[0]):
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confidence = output[i, 2]
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# get the confidence
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# if confidence is above 40%, then blur the bounding box (face)
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if confidence > 0.4:
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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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# get the face image
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face = image[start_y: end_y, start_x: end_x]
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# apply gaussian blur to this face
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face = cv2.GaussianBlur(face, (kernel_width, kernel_height), 0)
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# put the blurred face into the original image
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image[start_y: end_y, start_x: end_x] = face
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cv2.imshow("image", image)
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if cv2.waitKey(1) == ord("q"):
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break
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time_elapsed = time.time() - start
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fps = 1 / time_elapsed
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print("FPS:", fps)
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out.write(image)
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cv2.destroyAllWindows()
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cap.release()
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out.release()

‎machine-learning/blur-faces/father-and-daughter.jpg‎

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‎machine-learning/blur-faces/image_blurred.jpg‎

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‎machine-learning/blur-faces/requirements.txt‎

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opencv-python
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numpy

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