【PYTHON OPENCV】Face detection using OpenCV DNN face detector

""" Face detection using OpenCV DNN face detector """ # Import required packages: import cv2 import numpy as np from matplotlib import pyplot as plt def show_img_with_matplotlib(color_img, title, pos): """Shows an image using matplotlib capabilities""" img_RGB = color_img[:, :, ::-1] ax = plt.subplot(1, 1, pos) plt.imshow(img_RGB) plt.title(title) plt.axis('off') # Load pre-trained model: net = cv2.dnn.readNetFromCaffe("deploy.prototxt", "res10_300x300_ssd_iter_140000_fp16.caffemodel") # net = cv2.dnn.readNetFromTensorflow("opencv_face_detector_uint8.pb", "opencv_face_detector.pbtxt") # Load image: image = cv2.imread("test_face_detection.jpg") # Get dimensions of the input image (to be used later): (h, w) = image.shape[:2] # Create 4-dimensional blob from image: blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300), [104., 117., 123.], False, False) # Set the blob as input and obtain the detections: net.setInput(blob) detections = net.forward() # Initialize the number of detected faces counter detected_faces: detected_faces = 0 # Iterate over all detections: for i in range(0, detections.shape[2]): # Get the confidence (probability) of the current detection: confidence = detections[0, 0, i, 2] # Only consider detections if confidence is greater than a fixed minimum confidence: if confidence > 0.7: # Increment the number of detected faces: detected_faces += 1 # Get the coordinates of the current detection: box = detections[0, 0, i, 3:7] * np.array([w, h, w, h]) (startX, startY, endX, endY) = box.astype("int") # Draw the detection and the confidence: text = "{:.3f}%".format(confidence * 100) y = startY - 10 if startY - 10 > 10 else startY + 10 cv2.rectangle(image, (startX, startY), (endX, endY), (255, 0, 0), 3) cv2.putText(image, text, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2) # Create the dimensions of the figure and set title: fig = plt.figure(figsize=(10, 5)) plt.suptitle("Face detection using OpenCV DNN face detector", fontsize=14, fontweight='bold') fig.patch.set_facecolor('silver') # Plot the images: show_img_with_matplotlib(image, "DNN face detector: " + str(detected_faces), 1) # Show the Figure: plt.show()