updated face detection tutorial to use SSDs as well

Author: x4nth055

machine-learning/face_detection/face_detection_dnn.py

@@ -0,0 +1,41 @@
+import cv2
+import numpy as np
+
+# 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)
+
+# read the desired image
+image = cv2.imread("kids.jpg")
+# get width and height of the image
+h, w = image.shape[:2]
+
+# 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())
+font_scale = 1.0
+for i in range(0, output.shape[0]):
+    # get the confidence
+    confidence = output[i, 2]
+    # if confidence is above 50%, then draw the surrounding box
+    if confidence > 0.5:
+        # 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)
+        # draw the rectangle surrounding the face
+        cv2.rectangle(image, (start_x, start_y), (end_x, end_y), color=(255, 0, 0), thickness=2)
+        # draw text as well
+        cv2.putText(image, f"{confidence*100:.2f}%", (start_x, start_y-5), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (255, 0, 0), 2)
+# show the image
+cv2.imshow("image", image)
+cv2.waitKey(0)
+# save the image with rectangles
+cv2.imwrite("kids_detected_dnn.jpg", image)

machine-learning/face_detection/kids_detected_dnn.jpg

@@ -0,0 +1,47 @@
+import cv2
+import numpy as np
+
+# 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:
+
+    # read the desired image
+    _, image = cap.read()
+    # get width and height of the image
+    h, w = image.shape[:2]
+
+        # 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())
+    font_scale = 1.0
+    for i in range(0, output.shape[0]):
+        # get the confidence
+        confidence = output[i, 2]
+        # if confidence is above 50%, then draw the surrounding box
+        if confidence > 0.5:
+            # 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)
+            # draw the rectangle surrounding the face
+            cv2.rectangle(image, (start_x, start_y), (end_x, end_y), color=(255, 0, 0), thickness=2)
+            # draw text as well
+            cv2.putText(image, f"{confidence*100:.2f}%", (start_x, start_y-5), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (255, 0, 0), 2)
+    # show the image
+    cv2.imshow("image", image)
+    if cv2.waitKey(1) == ord("q"):
+        break
+
+cv2.destroyAllWindows()
+cap.release()