【PYTHON OPENCV】Face detection using OpenCV DNN face detector when feeding several images to the network and crop=True in function cv2.dnn.blobFromImages()

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Face detection using OpenCV DNN face detector when feeding several images to the network and crop=True in function cv2.dnn.blobFromImages() """ # 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(2, 2, pos) plt.imshow(img_RGB) plt.title(title) plt.axis('off') def get_cropped_imgs(imgs): """Returns the cropped images""" imgs_cropped = [] for img in imgs: # Create a copy of the image: img_copy = img.copy() # calculate size of resulting image: size = min(img_copy.shape[1], img_copy.shape[0]) # calculate x1, and y1 x1 = int(0.5 * (img_copy.shape[1] - size)) y1 = int(0.5 * (img_copy.shape[0] - size)) # crop and return the image imgs_cropped.append(img_copy[y1:(y1 + size), x1:(x1 + size)]) return imgs_cropped # Load pre-trained model: net = cv2.dnn.readNetFromCaffe("deploy.prototxt", "res10_300x300_ssd_iter_140000_fp16.caffemodel") # Load images and get the list of images: image = cv2.imread("face_test.png") image2 = cv2.imread("face_test2.jpg") images = [image, image2] # Get the cropped (original) images: images_cropped = get_cropped_imgs(images) # Call cv2.dnn.blobFromImages(): blob_cropped = cv2.dnn.blobFromImages(images, 1.0, (300, 300), [104., 117., 123.], False, True) # Set the blob as input and obtain the detections: net.setInput(blob_cropped) detections = net.forward() # Iterate over all detections: # We have to check the first element of each detection to know which image it belongs to: for i in range(0, detections.shape[2]): # First, we have to get the image the detection belongs to: img_id = int(detections[0, 0, i, 0]) # Get the confidence of this prediction: confidence = detections[0, 0, i, 2] # Filter out weak predictions: if confidence > 0.25: # Get the size of the current image: (h, w) = images_cropped[img_id].shape[:2] # Get the (x,y) coordinates of the detection: box = detections[0, 0, i, 3:7] * np.array([w, h, w, h]) (startX, startY, endX, endY) = box.astype("int") # Draw bounding box and probability: text = "{:.2f}%".format(confidence * 100) y = startY - 10 if startY - 10 > 10 else startY + 10 # Note here that we are showing the results in the cropped images!: cv2.rectangle(images_cropped[img_id], (startX, startY), (endX, endY), (0, 0, 255), 2) cv2.putText(images_cropped[img_id], text, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2) # Create the dimensions of the figure and set title: fig = plt.figure(figsize=(16, 8)) plt.suptitle("OpenCV DNN face detector when feeding several images and cropping", fontsize=14, fontweight='bold') fig.patch.set_facecolor('silver') # Show the input and the output images with the detections: show_img_with_matplotlib(image, "input img 1", 1) show_img_with_matplotlib(image2, "input img 2", 2) show_img_with_matplotlib(images_cropped[0], "output cropped img 1", 3) show_img_with_matplotlib(images_cropped[1], "output cropped img 2", 4) # Show the Figure: plt.show()