Spaces:
Runtime error
Runtime error
| # USAGE | |
| # python create_gif.py --config config.json --image images/me.jpg --output me.gif | |
| # import the necessary packages | |
| from imutils import face_utils | |
| from imutils import paths | |
| import numpy as np | |
| import argparse | |
| import imutils | |
| import shutil | |
| import json | |
| import dlib | |
| import cv2 | |
| import sys | |
| import os | |
| def overlay_image(bg, fg, fgMask, coords): | |
| (sH, sW) = fg.shape[:2] | |
| (x, y) = coords | |
| overlay = np.zeros(bg.shape, dtype="uint8") #黑色背景 | |
| overlay[y:y + sH, x:x + sW] = fg | |
| alpha = np.zeros(bg.shape[:2], dtype="uint8") | |
| alpha[y:y + sH, x:x + sW] = fgMask | |
| alpha = np.dstack([alpha] * 3) | |
| output = alpha_blend(overlay, bg, alpha) | |
| return output | |
| def alpha_blend(fg, bg, alpha): | |
| fg = fg.astype("float") | |
| bg = bg.astype("float") | |
| alpha = alpha.astype("float") / 255 | |
| fg = cv2.multiply(alpha, fg) | |
| bg = cv2.multiply(1 - alpha, bg) | |
| output = cv2.add(fg, bg) | |
| # return the output image | |
| return output.astype("uint8") | |
| def get_features(img_rd,*args): | |
| # 输入: img_rd: 图像文件 | |
| # 输出: pos_69to81: feature 69 to feature 81, 13 feature points in all, 40 points | |
| detector1 = dlib.get_frontal_face_detector() | |
| predictor1=dlib.shape_predictor("shape_predictor_81_face_landmarks.dat") | |
| # read img file | |
| img = cv2.imread(img_rd) | |
| # 取灰度 | |
| img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) | |
| # 计算 81 点坐标 | |
| pos_81 = [] | |
| rects = detector1(img_gray, 0) | |
| landmarks = np.matrix([[p.x, p.y] for p in predictor1(img, rects[0]).parts()]) | |
| for idx, point in enumerate(landmarks): | |
| # 81点的坐标 | |
| pos = (point[0, 0], point[0, 1]) | |
| pos_81.append(pos) | |
| get_pos=[] | |
| get_pos.append((pos_81[args[0]][0],pos_81[args[0]][1])) | |
| get_pos.append((pos_81[args[1]][0],pos_81[args[1]][1])) | |
| return get_pos | |
| def create_gif(inputPath, outputPath, delay, finalDelay, loop): | |
| # grab all image paths in the input directory | |
| imagePaths = sorted(list(paths.list_images(inputPath))) | |
| # remove the last image path in the list | |
| lastPath = imagePaths[-1] | |
| imagePaths = imagePaths[:-1] | |
| cmd = "convert -delay {} {} -delay {} {} -loop {} {}".format( | |
| delay, " ".join(imagePaths), finalDelay, lastPath, loop, | |
| outputPath) | |
| os.system(cmd) | |
| ap = argparse.ArgumentParser() | |
| ap.add_argument("-c", "--config", required=True, | |
| help="path to configuration file") | |
| ap.add_argument("-i", "--image", required=True, | |
| help="path to input image") | |
| ap.add_argument("-o", "--output", required=True, | |
| help="path to output GIF") | |
| args = vars(ap.parse_args()) | |
| config = json.loads(open(args["config"]).read()) | |
| sg = cv2.imread(config["Crown"]) | |
| sgMask = cv2.imread(config["Crown_mask"]) | |
| shutil.rmtree(config["temp_dir"], ignore_errors=True) | |
| os.makedirs(config["temp_dir"]) | |
| print("[INFO] loading models...") | |
| detector = cv2.dnn.readNetFromCaffe(config["face_detector_prototxt"], | |
| config["face_detector_weights"]) | |
| predictor = dlib.shape_predictor(config["landmark_predictor"]) | |
| image = cv2.imread(args["image"]) | |
| (H, W) = image.shape[:2] | |
| blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0, | |
| (300, 300), (104.0, 177.0, 123.0)) | |
| print("[INFO] computing object detections...") | |
| detector.setInput(blob) | |
| detections = detector.forward() | |
| i = np.argmax(detections[0, 0, :, 2]) | |
| confidence = detections[0, 0, i, 2] | |
| if confidence < config["min_confidence"]: | |
| print("[INFO] no reliable faces found") | |
| sys.exit(0) | |
| # compute the (x, y)-coordinates of the bounding box for the face | |
| box = detections[0, 0, i, 3:7] * np.array([W, H, W, H]) | |
| (startX, startY, endX, endY) = box.astype("int") | |
| rect = dlib.rectangle(int(startX), int(startY), int(endX), int(endY)) | |
| shape = predictor(image, rect) | |
| shape = face_utils.shape_to_np(shape) | |
| forehead=get_features(args["image"],76,73) | |
| left_point=forehead[0] | |
| right_point=forehead[1] | |
| dY=left_point[1]-right_point[1] | |
| dX=left_point[0]-right_point[0] | |
| print("left_point:"+str(left_point[1])) | |
| angle = np.degrees(np.arctan2(dY, dX)) - 180 | |
| # rotate the sunglasses image by our computed angle, ensuring the | |
| # sunglasses will align with how the head is tilted | |
| sg = imutils.rotate_bound(sg, angle) | |
| # sgW = int((shape[16].astype(int)[0] - shape[0].astype(int)[0])*1.40) | |
| sgW=int((endX-startX)*1.2) | |
| sg = imutils.resize(sg, width=sgW) | |
| print("sgw"+str(shape[16])) | |
| sgMask = cv2.cvtColor(sgMask, cv2.COLOR_BGR2GRAY) | |
| sgMask = cv2.threshold(sgMask, 0, 255, cv2.THRESH_BINARY)[1] | |
| sgMask = imutils.rotate_bound(sgMask, angle) | |
| sgMask = imutils.resize(sgMask, width=sgW, inter=cv2.INTER_NEAREST) | |
| steps = np.linspace(0, left_point[1], config["steps"], | |
| dtype="int") | |
| # start looping over the steps | |
| for (i, y) in enumerate(steps): | |
| shiftX = int(sg.shape[1] * 0.18) | |
| shiftY = int(sg.shape[0]*0.80) | |
| y = max(0, y-shiftY) | |
| # add the sunglasses to the image | |
| output = overlay_image(image, sg, sgMask, | |
| (left_point[0]-shiftX, y)) | |
| if i == len(steps) - 1: | |
| dwi = cv2.imread(config["birthday"]) | |
| dwiMask = cv2.imread(config["birthday_mask"]) | |
| dwiMask = cv2.cvtColor(dwiMask, cv2.COLOR_BGR2GRAY) | |
| dwiMask = cv2.threshold(dwiMask, 0, 255, | |
| cv2.THRESH_BINARY)[1] | |
| # resize both the text image and mask to be 80% the width of | |
| # the output image | |
| oW = int(W * 0.8) | |
| dwi = imutils.resize(dwi, width=oW) | |
| dwiMask = imutils.resize(dwiMask, width=oW, | |
| inter=cv2.INTER_NEAREST) | |
| # compute the coordinates of where the text will go on the | |
| # output image and then add the text to the image | |
| oX = int(W * 0.1) | |
| oY = int(H * 0.7) | |
| output = overlay_image(output, dwi, dwiMask, (oX, oY)) | |
| # write the output image to our temporary directory | |
| p = os.path.sep.join([config["temp_dir"], "{}.jpg".format( | |
| str(i).zfill(8))]) | |
| cv2.imwrite(p, output) | |
| print("[INFO] creating GIF...") | |
| create_gif(config["temp_dir"], args["output"], config["delay"], | |
| config["final_delay"], config["loop"]) | |
| # cleanup by deleting our temporary directory | |
| print("[INFO] cleaning up...") | |
| shutil.rmtree(config["temp_dir"], ignore_errors=True) | |