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132 lines
4.2 KiB
132 lines
4.2 KiB
# USAGE
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# python recognize_faces_video_file.py --encodings encodings.pickle --input videos/lunch_scene.mp4
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# python recognize_faces_video_file.py --encodings encodings.pickle --input videos/lunch_scene.mp4 --output output/lunch_scene_output.avi --display 0
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# import the necessary packages
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import face_recognition
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import argparse
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import imutils
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import pickle
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import time
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import cv2
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# construct the argument parser and parse the arguments
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ap = argparse.ArgumentParser()
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ap.add_argument("-e", "--encodings", required=True,
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help="path to serialized db of facial encodings")
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ap.add_argument("-i", "--input", required=True,
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help="path to input video")
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ap.add_argument("-o", "--output", type=str,
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help="path to output video")
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ap.add_argument("-y", "--display", type=int, default=1,
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help="whether or not to display output frame to screen")
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ap.add_argument("-d", "--detection-method", type=str, default="cnn",
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help="face detection model to use: either `hog` or `cnn`")
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args = vars(ap.parse_args())
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# load the known faces and embeddings
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print("[INFO] loading encodings...")
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data = pickle.loads(open(args["encodings"], "rb").read())
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# initialize the pointer to the video file and the video writer
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print("[INFO] processing video...")
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stream = cv2.VideoCapture(args["input"])
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writer = None
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# loop over frames from the video file stream
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while True:
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# grab the next frame
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(grabbed, frame) = stream.read()
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# if the frame was not grabbed, then we have reached the
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# end of the stream
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if not grabbed:
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break
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# convert the input frame from BGR to RGB then resize it to have
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# a width of 750px (to speedup processing)
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rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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rgb = imutils.resize(frame, width=750)
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r = frame.shape[1] / float(rgb.shape[1])
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# detect the (x, y)-coordinates of the bounding boxes
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# corresponding to each face in the input frame, then compute
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# the facial embeddings for each face
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boxes = face_recognition.face_locations(rgb,
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model=args["detection_method"])
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encodings = face_recognition.face_encodings(rgb, boxes)
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names = []
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# loop over the facial embeddings
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for encoding in encodings:
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# attempt to match each face in the input image to our known
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# encodings
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matches = face_recognition.compare_faces(data["encodings"],
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encoding)
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name = "Unknown"
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# check to see if we have found a match
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if True in matches:
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# find the indexes of all matched faces then initialize a
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# dictionary to count the total number of times each face
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# was matched
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matchedIdxs = [i for (i, b) in enumerate(matches) if b]
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counts = {}
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# loop over the matched indexes and maintain a count for
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# each recognized face face
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for i in matchedIdxs:
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name = data["names"][i]
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counts[name] = counts.get(name, 0) + 1
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# determine the recognized face with the largest number
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# of votes (note: in the event of an unlikely tie Python
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# will select first entry in the dictionary)
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name = max(counts, key=counts.get)
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# update the list of names
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names.append(name)
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# loop over the recognized faces
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for ((top, right, bottom, left), name) in zip(boxes, names):
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# rescale the face coordinates
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top = int(top * r)
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right = int(right * r)
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bottom = int(bottom * r)
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left = int(left * r)
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# draw the predicted face name on the image
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cv2.rectangle(frame, (left, top), (right, bottom),
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(0, 255, 0), 2)
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y = top - 15 if top - 15 > 15 else top + 15
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cv2.putText(frame, name, (left, y), cv2.FONT_HERSHEY_SIMPLEX,
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0.75, (0, 255, 0), 2)
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# if the video writer is None *AND* we are supposed to write
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# the output video to disk initialize the writer
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if writer is None and args["output"] is not None:
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fourcc = cv2.VideoWriter_fourcc(*"MJPG")
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writer = cv2.VideoWriter(args["output"], fourcc, 24,
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(frame.shape[1], frame.shape[0]), True)
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# if the writer is not None, write the frame with recognized
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# faces t odisk
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if writer is not None:
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writer.write(frame)
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# check to see if we are supposed to display the output frame to
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# the screen
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if args["display"] > 0:
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cv2.imshow("Frame", frame)
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key = cv2.waitKey(1) & 0xFF
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# if the `q` key was pressed, break from the loop
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if key == ord("q"):
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break
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# close the video file pointers
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stream.release()
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# check to see if the video writer point needs to be released
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if writer is not None:
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writer.release()
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