# USAGE # python detect_mask_image.py --image images/pic1.jpeg import argparse import os import cv2 import numpy as np # import the necessary packages from tensorflow.keras.applications.mobilenet_v2 import preprocess_input from tensorflow.keras.models import load_model from tensorflow.keras.preprocessing.image import img_to_array def mask_image(): # construct the argument parser and parse the arguments ap = argparse.ArgumentParser() ap.add_argument( "-i", "--image", required=True, help="path to input image", ) ap.add_argument( "-f", "--face", type=str, default="face_detector", help="path to face detector model directory", ) ap.add_argument( "-m", "--model", type=str, default="mask_detector.model", help="path to trained face mask detector model", ) ap.add_argument( "-c", "--confidence", type=float, default=0.5, help="minimum probability to filter weak detections", ) args = vars(ap.parse_args()) # load our serialized face detector model from disk print("[INFO] loading face detector model...") prototxtPath = os.path.sep.join([args["face"], "deploy.prototxt"]) weightsPath = os.path.sep.join( [ args["face"], "res10_300x300_ssd_iter_140000.caffemodel", ], ) net = cv2.dnn.readNet(prototxtPath, weightsPath) # load the face mask detector model from disk print("[INFO] loading face mask detector model...") model = load_model(args["model"]) # load the input image from disk, clone it, and grab the image spatial # dimensions image = cv2.imread(args["image"]) (h, w) = image.shape[:2] # construct a blob from the image blob = cv2.dnn.blobFromImage( image, 1.0, (300, 300), (104.0, 177.0, 123.0), ) # pass the blob through the network and obtain the face detections print("[INFO] computing face detections...") net.setInput(blob) detections = net.forward() # loop over the detections for i in range(0, detections.shape[2]): # extract the confidence (i.e., probability) associated with # the detection confidence = detections[0, 0, i, 2] # filter out weak detections by ensuring the confidence is # greater than the minimum confidence if confidence > args["confidence"]: # compute the (x, y)-coordinates of the bounding box for # the object box = detections[0, 0, i, 3:7] * np.array([w, h, w, h]) (startX, startY, endX, endY) = box.astype("int") # ensure the bounding boxes fall within the dimensions of # the frame (startX, startY) = (max(0, startX), max(0, startY)) (endX, endY) = (min(w - 1, endX), min(h - 1, endY)) # extract the face ROI, convert it from BGR to RGB channel # ordering, resize it to 224x224, and preprocess it face = image[startY:endY, startX:endX] face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB) face = cv2.resize(face, (224, 224)) face = img_to_array(face) face = preprocess_input(face) face = np.expand_dims(face, axis=0) # pass the face through the model to determine if the face # has a mask or not (mask, withoutMask) = model.predict(face)[0] # determine the class label and color we'll use to draw # the bounding box and text label = "Mask" if mask > withoutMask else "No Mask" color = (0, 255, 0) if label == "Mask" else (0, 0, 255) # include the probability in the label label = f"{label}: {max(mask, withoutMask) * 100:.2f}%" # display the label and bounding box rectangle on the output # frame cv2.putText( image, label, (startX, startY - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2, ) cv2.rectangle(image, (startX, startY), (endX, endY), color, 2) # show the output image cv2.imshow("Output", image) cv2.waitKey(0) def detect_mask_in_image(image, faceNet, maskNet): # dimensions (h, w) = image.shape[:2] # construct a blob from the image blob = cv2.dnn.blobFromImage( image, 1.0, (300, 300), (104.0, 177.0, 123.0), ) # TODO: add to config # pass the blob through the network and obtain the face detections print("[INFO] computing face detections...") faceNet.setInput(blob) detections = faceNet.forward() face_count = 0 # image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # loop over the detections for i in range(0, detections.shape[2]): # extract the confidence associated with the detection confidence = detections[0, 0, i, 2] # print(f"[INFO] face {i}: {confidence}") # filter out weak detections by ensuring the confidence is # greater than the minimum confidence if confidence > 0.5: face_count += 1 # compute the (x, y)-coordinates of the object's bbox box = detections[0, 0, i, 3:7] * np.array([w, h, w, h]) (startX, startY, endX, endY) = box.astype("int") # ensure the bounding boxes fall within the dimensions of # the frame (startX, startY) = (max(0, startX), max(0, startY)) (endX, endY) = (min(w - 1, endX), min(h - 1, endY)) # extract the face ROI # ordering, resize it to 224x224, and preprocess it face = image[startY:endY, startX:endX] face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB) face = cv2.resize(face, (224, 224)) face = img_to_array(face) face = preprocess_input(face) face = np.expand_dims(face, axis=0) # pass the face through the model to determine if the face # has a mask or not (mask, withoutMask) = maskNet.predict(face)[0] # determine the class label and color we'll use to draw # the bounding box and text label = "Mask" if mask > withoutMask else "No Mask" color = (0, 255, 0) if label == "Mask" else (255, 0, 0) # include the probability in the label label = f"{label}: {max(mask, withoutMask) * 100:.2f}%" # display the label & bbox rectangle on the output frame cv2.putText( image, label, (startX, startY - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2, ) cv2.rectangle(image, (startX, startY), (endX, endY), color, 2) else: break text = f"[INFO] Detect {face_count} face(s)." print(text) cv2.putText( image, text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.70, (0, 255, 0), 2, ) return image if __name__ == "__main__": mask_image()