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demo02-test-03.py
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demo02-test-03.py
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from face_swap import swap, cv2, DeepFace
from text_detector_test import text_detector, text_erasor, rgb_to_hsv, find_most_common_color
import os,shutil
import face_recognition
import numpy as np
from moviepy.editor import *
from PIL import Image
from error_scope import original_name_errors
from east_text_model import find_text_boxes
from word_to_image import *
from ranges import black, blue
import math
import keras_ocr
pipeline = keras_ocr.pipeline.Pipeline()
executable = True
tolerance = 0.5 # 0.5 -> Very Accurate | >0.5 -> Decent
old_face = "static/orignal.png"
new_face = "static/demo.png"
video = "static/sample.mp4"
new_name = "Arnav"
original_name = "rohit"
banner_blue_loc_array = []
banner_blue_dim_array = [1,1]
banner_black_loc_array = []
banner_black_dim_array = [1,1]
def video_frames(path):
array = []
shutil.rmtree("frames")
os.mkdir("frames")
shutil.rmtree("text")
os.mkdir("text")
shutil.rmtree("text-box")
os.mkdir("text-box")
shutil.rmtree("detected-text")
os.mkdir("detected-text")
capture = cv2.VideoCapture(path)
video = VideoFileClip(path)
audio = video.audio
audio.write_audiofile("temp/output-audio.mp3")
fps = capture.get(cv2.CAP_PROP_FPS)
frameNr = 0
while (True):
success, frame = capture.read()
if success:
cv2.imwrite(f'frames/frame_{frameNr}.jpg', frame)
array.append(f'frames/frame_{frameNr}.jpg')
else:
break
frameNr = frameNr+1
capture.release()
return [array, fps]
def video_play(path):
cap = cv2.VideoCapture(path)
if (cap.isOpened()== False):
print("Error opening video file")
while(cap.isOpened()):
ret, frame = cap.read()
if ret == True:
cv2.imshow('Frame', frame)
if cv2.waitKey(25) & 0xFF == ord('q'):
break
else:
break
cap.release()
cv2.destroyAllWindows()
def video_join(frames, fps):
height, width, layers = cv2.imread(frames[0]).shape
video = cv2.VideoWriter("temp/output-video.mp4", cv2.VideoWriter_fourcc(*'DIVX'), fps, (width,height))
for image in frames:
video.write(cv2.imread(image))
cv2.destroyAllWindows()
video.release()
video_clip = VideoFileClip("temp/output-video.mp4")
audio_clip = AudioFileClip("temp/output-audio.mp3")
final_clip = video_clip.set_audio(audio_clip)
final_clip.write_videofile("output/output.mp4")
print("Video Saved")
def match_face(face_array, face):
result = face_recognition.compare_faces(face_array, face, tolerance=tolerance) #tolerance=0.5 removed
if True in result:
return [True, result.index(True)]
else:
return [False, len(face_array)]
def action(encodings, dictionary, face, frame, img,x,y,w,h):
file = f'middleware/{len(encodings)}.jpg'
cv2.imwrite(file, face)
loaded = cv2.imread(file)
if face_recognition.face_encodings(loaded, model="large") != []:
encoding = face_recognition.face_encodings(loaded, model="large")[0]
result = match_face(encodings, encoding)
matched = result[1]
print(matched, result)
if matched in dictionary.keys():
new_image = swap(dictionary[matched],face)
cv2.imwrite("temp/temp.jpg", new_image)
img[y:y+h, x:x+w] = cv2.imread("temp/temp.jpg")
cv2.imwrite(frame, img)
encodings = [ face_recognition.face_encodings(cv2.imread(old_face),model="large")[0]]
dictionary = {0:cv2.imread(new_face)}
def detect_faces_and_swap(frame):
img = cv2.imread(frame)
cv2.imwrite(frame, img)
i = 0
obj = []
try:
obj = DeepFace.analyze(img, actions = ["gender"])
except:
print("No face detected")
for temp_img in obj:
i = i + 1
x = max(temp_img["region"]["x"] - int(temp_img["region"]["w"]/2), 0)
y = max(temp_img["region"]["y"] - int(temp_img["region"]["h"]/2), 0)
w = temp_img["region"]["w"] + int(temp_img["region"]["w"]/1.5)
h = temp_img["region"]["h"] + int(temp_img["region"]["h"]/1.5)
face = img[y:y+h, x:x+w]
action(encodings, dictionary, face, frame, img, x,y,w,h)
def overlay_images(background_img_path, data, x, y):
w = int(math.sqrt(math.pow(data[1][2][0] - data[1][3][0], 2) + math.pow(data[1][2][1] - data[1][3][1], 2)))
h = int(math.sqrt(math.pow(data[1][1][0] - data[1][2][0], 2) + math.pow(data[1][1][1] - data[1][2][1], 2)))
angle = math.degrees(math.atan((data[1][2][1] - data[1][3][1])/(data[1][3][0] - data[1][2][0])))
background = Image.open(background_img_path)
overlay = Image.open("temp/banner.png")
if overlay.mode != 'RGBA':
overlay = overlay.convert('RGBA')
overlay = overlay.resize((w, h))
overlay = overlay.rotate(angle, expand=True, resample=Image.BICUBIC)
mask = overlay.split()[3] if len(overlay.split()) == 4 else None
background.paste(overlay, (x, y), mask=mask)
background.save(background_img_path)
def extract_text_and_erase(image_path):
global banner_blue_loc_array
global banner_black_loc_array
image = keras_ocr.tools.read(image_path)
prediction_groups = pipeline.recognize([image])
for data in prediction_groups[0]:
coordinates = data[1]
coordinates = coordinates.astype(np.int32)
coordinates = coordinates.reshape((-1, 1, 2))
coordinates = coordinates.reshape((4, 2))
coordinates = coordinates.astype(int)
if data[0] == original_name:
image = cv2.imread(image_path)
x, y, w, h = cv2.boundingRect(coordinates)
x = max(0, x)
y = max(0, y)
w = min(w, image.shape[1] - x)
h = min(h, image.shape[0] - y)
color = find_most_common_color(image[y:y+h, x:x+w])
if x < 800:
banner = remove_other_colours(cv2.imread("temp/new_banner_man.png"), rgb_to_hsv(color), rgb_to_hsv(blue))
if x > 800:
banner = remove_other_colours(cv2.imread("temp/new_banner_women.png"), rgb_to_hsv(color), rgb_to_hsv(black))
mask = np.zeros(image.shape[:2], dtype=np.uint8)
cv2.fillPoly(mask, [coordinates], 255)
image[mask == 255] = rgb_to_hsv(color)
cv2.imwrite(image_path, image)
cv2.imwrite("temp/banner.png", banner)
overlay_images(image_path, data, x, y)
def change_video(path):
array = video_frames(path)
frames = array[0]
fps = array[1]
get_image(new_name, "static/alphabets-v2.jpg", "new_banner_man.png")
get_image(new_name, "static/alphabets.png", "new_banner_women.png")
for x in frames:
print(x)
extract_text_and_erase(x)
detect_faces_and_swap(x)
video_join(frames, fps)
if executable:
change_video(video)