added blurring faces in images/videos tutorial

Author: x4nth055

Diff for: README.md

@@ -42,6 +42,7 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy
         - [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))
         - [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))
         - [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))
+        - [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))
     - [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))
     - [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))
     - [Top 8 Python Libraries For Data Scientists and Machine Learning Engineers](https://www.thepythoncode.com/article/top-python-libraries-for-data-scientists).

Diff for: machine-learning/blur-faces/README.md

@@ -0,0 +1,17 @@
+# [How to Blur Faces in Images using OpenCV in Python](https://www.thepythoncode.com/article/blur-faces-in-images-using-opencv-in-python)
+To run this:
+- `pip3 install -r requirements.txt`
+- To blur faces of the image `father-and-daughter.jpg`:
+    ```
+    python blur_faces.py a-man-with-little-girl.jpg
+    ```
+    This should show the blurred image and save it of the name `image_blurred.jpg` in your current directory.
+
+- To blur faces using your live camera:
+    ```
+    python blur_faces_live.py
+    ```
+- To blur faces of a video:
+    ```
+    python blur_faces_video.py video.3gp
+    ```

Diff for: machine-learning/blur-faces/blur_faces.py

@@ -0,0 +1,44 @@
+import cv2
+import numpy as np
+import sys
+
+# https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt
+prototxt_path = "weights/deploy.prototxt.txt"
+# https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel 
+model_path = "weights/res10_300x300_ssd_iter_140000_fp16.caffemodel"
+
+# load Caffe model
+model = cv2.dnn.readNetFromCaffe(prototxt_path, model_path)
+# get the image file name from the command line
+image_file = sys.argv[1]
+# read the desired image
+image = cv2.imread(image_file)
+# get width and height of the image
+h, w = image.shape[:2]
+# gaussian blur kernel size depends on width and height of original image
+kernel_width = (w // 7) | 1
+kernel_height = (h // 7) | 1
+# preprocess the image: resize and performs mean subtraction
+blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300), (104.0, 177.0, 123.0))
+# set the image into the input of the neural network
+model.setInput(blob)
+# perform inference and get the result
+output = np.squeeze(model.forward())
+for i in range(0, output.shape[0]):
+    confidence = output[i, 2]
+    # get the confidence
+    # if confidence is above 40%, then blur the bounding box (face)
+    if confidence > 0.4:
+        # get the surrounding box cordinates and upscale them to original image
+        box = output[i, 3:7] * np.array([w, h, w, h])
+        # convert to integers
+        start_x, start_y, end_x, end_y = box.astype(np.int)
+        # get the face image
+        face = image[start_y: end_y, start_x: end_x]
+        # apply gaussian blur to this face
+        face = cv2.GaussianBlur(face, (kernel_width, kernel_height), 0)
+        # put the blurred face into the original image
+        image[start_y: end_y, start_x: end_x] = face
+cv2.imshow("image", image)
+cv2.waitKey(0)
+cv2.imwrite("image_blurred.jpg", image)

Diff for: machine-learning/blur-faces/blur_faces_live.py

@@ -0,0 +1,50 @@
+import cv2
+import numpy as np
+import time
+
+# https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt
+prototxt_path = "weights/deploy.prototxt.txt"
+# https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel 
+model_path = "weights/res10_300x300_ssd_iter_140000_fp16.caffemodel"
+
+# load Caffe model
+model = cv2.dnn.readNetFromCaffe(prototxt_path, model_path)
+
+cap = cv2.VideoCapture(0)
+while True:
+    start = time.time()
+    _, image = cap.read()
+    # get width and height of the image
+    h, w = image.shape[:2]
+    kernel_width = (w // 7) | 1
+    kernel_height = (h // 7) | 1
+    # preprocess the image: resize and performs mean subtraction
+    blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300), (104.0, 177.0, 123.0))
+    # set the image into the input of the neural network
+    model.setInput(blob)
+    # perform inference and get the result
+    output = np.squeeze(model.forward())
+    for i in range(0, output.shape[0]):
+        confidence = output[i, 2]
+        # get the confidence
+        # if confidence is above 40%, then blur the bounding box (face)
+        if confidence > 0.4:
+            # get the surrounding box cordinates and upscale them to original image
+            box = output[i, 3:7] * np.array([w, h, w, h])
+            # convert to integers
+            start_x, start_y, end_x, end_y = box.astype(np.int)
+            # get the face image
+            face = image[start_y: end_y, start_x: end_x]
+            # apply gaussian blur to this face
+            face = cv2.GaussianBlur(face, (kernel_width, kernel_height), 0)
+            # put the blurred face into the original image
+            image[start_y: end_y, start_x: end_x] = face
+    cv2.imshow("image", image)
+    if cv2.waitKey(1) == ord("q"):
+        break
+    time_elapsed = time.time() - start
+    fps = 1 / time_elapsed
+    print("FPS:", fps)
+
+cv2.destroyAllWindows()
+cap.release()

Diff for: machine-learning/blur-faces/blur_faces_video.py

@@ -0,0 +1,61 @@
+import cv2
+import numpy as np
+import time
+import sys
+
+# https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt
+prototxt_path = "weights/deploy.prototxt.txt"
+# https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel 
+model_path = "weights/res10_300x300_ssd_iter_140000_fp16.caffemodel"
+
+# load Caffe model
+model = cv2.dnn.readNetFromCaffe(prototxt_path, model_path)
+# get video file from command line
+video_file = sys.argv[1]
+# capture frames from video
+cap = cv2.VideoCapture(video_file)
+fourcc = cv2.VideoWriter_fourcc(*"XVID")
+_, image = cap.read()
+print(image.shape)
+out = cv2.VideoWriter("output.avi", fourcc, 20.0, (image.shape[1], image.shape[0]))
+while True:
+    start = time.time()
+    captured, image = cap.read()
+    # get width and height of the image
+    if not captured:
+        break
+    h, w = image.shape[:2]
+    kernel_width = (w // 7) | 1
+    kernel_height = (h // 7) | 1
+    # preprocess the image: resize and performs mean subtraction
+    blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300), (104.0, 177.0, 123.0))
+    # set the image into the input of the neural network
+    model.setInput(blob)
+    # perform inference and get the result
+    output = np.squeeze(model.forward())
+    for i in range(0, output.shape[0]):
+        confidence = output[i, 2]
+        # get the confidence
+        # if confidence is above 40%, then blur the bounding box (face)
+        if confidence > 0.4:
+            # get the surrounding box cordinates and upscale them to original image
+            box = output[i, 3:7] * np.array([w, h, w, h])
+            # convert to integers
+            start_x, start_y, end_x, end_y = box.astype(np.int)
+            # get the face image
+            face = image[start_y: end_y, start_x: end_x]
+            # apply gaussian blur to this face
+            face = cv2.GaussianBlur(face, (kernel_width, kernel_height), 0)
+            # put the blurred face into the original image
+            image[start_y: end_y, start_x: end_x] = face
+    cv2.imshow("image", image)
+    if cv2.waitKey(1) == ord("q"):
+        break
+    time_elapsed = time.time() - start
+    fps = 1 / time_elapsed
+    print("FPS:", fps)
+    out.write(image)
+
+cv2.destroyAllWindows()
+cap.release()
+out.release()

Diff for: machine-learning/blur-faces/father-and-daughter.jpg

@@ -0,0 +1,2 @@
+opencv-python
+numpy