Upload create_gif.py

create_gif.py

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+# USAGE
+# python create_gif.py --config config.json --image images/vampire.jpg --output out.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(r"D:\PythonProject\birthday_crown\Creating-GIFs-with-OpenCV-master\assets\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)
+
+	# pos_69to81 = []
+	# # 将点 69-81 写入 CSV
+	# # 即 pos_81[68]-pos_81[80]
+	# for i in range(68, 81):
+	# 	pos_69to81.append(pos_81[i][0])
+	# 	pos_69to81.append(pos_81[i][1])
+	# return pos_69to81
+	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["sunglasses"])
+sgMask = cv2.imread(config["sunglasses_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)
+print("额头"+str(forehead))
+
+# print("LANDMARKS_IDXS"+str(face_utils.FACIAL_LANDMARKS_IDXS))
+# (lStart, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left_eye"]
+# (rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"]
+# leftEyePts = shape[lStart:lEnd]
+# rightEyePts = shape[rStart:rEnd]
+#
+# # compute the center of mass for each eye
+# leftEyeCenter = leftEyePts.mean(axis=0).astype("int")
+# rightEyeCenter = rightEyePts.mean(axis=0).astype("int")
+
+# compute the angle between the eye centroids
+# dY = rightEyeCenter[1] - leftEyeCenter[1]
+# dX = rightEyeCenter[0] - leftEyeCenter[0]
+# angle = np.degrees(np.arctan2(dY, dX)) - 180
+left_point=forehead[0]
+right_point=forehead[1]
+
+# left_point=shape[75].astype(int)
+# right_point=shape[79].astype(int)
+
+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["deal_with_it"])
+		dwiMask = cv2.imread(config["deal_with_it_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)
+
+
+