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demo02-test-00.py
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demo02-test-00.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
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 should_replace(text):
return True
def resize_bbox_to_minimum(x,y,w,h):
min_rect = cv2.minAreaRect(np.array([(x, y), (x + w, y), (x, y + h), (x + w, y + h)]))
min_rect_points = cv2.boxPoints(min_rect).astype(int)
min_x, min_y = np.min(min_rect_points, axis=0)
max_x, max_y = np.max(min_rect_points, axis=0)
return (min_x, min_y, max_x - min_x, max_y - min_y)
def create_boxes(n_boxes, data, path, type):
img = cv2.imread(f"frames/{path}.jpg")
for i in range(n_boxes):
(x, y, w, h) = (data['left'][i], data['top'][i], data['width'][i], data['height'][i])
img = cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
img = cv2.putText(img, data["text"][i].lower(), (x, y - 5),cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0,0,0), 1)
cv2.imwrite(f"text-box/{path}-{type}.jpg", img)
def extract_text_and_erase(image_path, execution_number, frame_number):
global banner_blue_loc_array
global banner_black_loc_array
global banner_blue_dim_array
global banner_black_dim_array
img = cv2.imread(image_path)
path = image_path.split("/")[1].split(".")[0]
all_boxes = find_text_boxes(img, path, "0")
values = []
banner_blue_loc = [0,0,1,1]
banner_black_loc = [0,0,1,1]
most_common_blue_color = ""
most_common_black_color = ""
for value in all_boxes:
xt = value[0]
wwt = value[2]
wt = wwt-xt
yt = value[1]
wht = min(value[3], 800)
ht = wht-yt
if xt > 0 and yt > 0 and wt > 0 and ht > 0:
values.append([xt-10,yt-10,wt+35,ht+35])
for value in values:
if (banner_blue_loc == [0,0,1,1] or banner_blue_loc[1] > value[1]) and value[0] < 800:
banner_blue_loc = value
most_common_blue_color = rgb_to_hsv(find_most_common_color(img[value[1]:value[1]+value[3], value[0]:value[0]+value[2]]))
elif (banner_black_loc == [0,0,1,1] or banner_black_loc[1] > value[1]) and value[0] > 800:
banner_black_loc = value
most_common_black_color = rgb_to_hsv(find_most_common_color(img[value[1]:value[1]+value[3], value[0]:value[0]+value[2]]))
img[value[1]:value[1]+value[3], value[0]:value[0]+value[2]] = text_erasor(value[0], img[value[1]:value[1]+value[3], value[0]:value[0]+value[2]])
if execution_number == 0:
if banner_blue_loc != [0,0,1,1] and banner_blue_dim_array == [1,1]:
banner_blue_dim_array = [banner_blue_loc[2], banner_blue_loc[3]]
if banner_black_loc != [0,0,1,1] and banner_black_dim_array == [1,1]:
banner_black_dim_array = [banner_black_loc[2], banner_black_loc[3]]
banner_blue_loc_array.append([banner_blue_loc, most_common_blue_color])
banner_black_loc_array.append([banner_black_loc, most_common_black_color])
if execution_number == 1:
if banner_blue_loc_array[frame_number][1] != "":
banner_blue = remove_other_colours(cv2.imread("temp/new_banner_man.png"), banner_blue_loc_array[frame_number][1],rgb_to_hsv(blue))
new_banner_blue_resized = cv2.resize(banner_blue, (banner_blue_dim_array[0],banner_blue_dim_array[1]))
img[banner_blue_loc_array[frame_number][0][1]:banner_blue_loc_array[frame_number][0][1]+banner_blue_dim_array[1], banner_blue_loc_array[frame_number][0][0]:banner_blue_loc_array[frame_number][0][0]+banner_blue_dim_array[0]] = new_banner_blue_resized
if banner_black_loc_array[frame_number][1] != "":
banner_black = remove_other_colours(cv2.imread("temp/new_banner_man.png"), banner_black_loc_array[frame_number][1],rgb_to_hsv(black))
new_banner_black_resized = cv2.resize(banner_black, (banner_black_dim_array[0],banner_black_dim_array[1]))
img[banner_black_loc_array[frame_number][0][1]:banner_black_loc_array[frame_number][0][1]+banner_black_dim_array[1], banner_black_loc_array[frame_number][0][0]:banner_black_loc_array[frame_number][0][0]+banner_black_dim_array[0]] = new_banner_black_resized
cv2.imwrite(image_path, img)
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)
for y in range(2):
extract_text_and_erase(x, y, frames.index(x))
# detect_faces_and_swap(x)
video_join(frames, fps)
if executable:
change_video(video)