Task03_Gamaonov

.gitignore

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 __pycache__/
 .ipynb_checkpoints/
 .idea/
+
+assignment_2/

hw3/app.py

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+import numpy as np
+import cv2
+import os
+from flask import Flask, request, redirect, url_for, send_from_directory
+import dlib
+
+# python version 3.6.8
+
+RESULT_IMG_NAME = 'result.png'
+UPLOAD_FOLDER = 'uploads'
+ALLOWED_EXTENSIONS = ['png', 'jpg', 'jpeg']
+
+app = Flask(__name__)
+app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
+
+
+# Apply affine transform calculated using srcTri and dstTri to src and
+# output an image of size.
+def apply_affine_transform(src, src_tri, dst_tri, size):
+    # Given a pair of triangles, find the affine transform.
+    warp_mat = cv2.getAffineTransform(np.float32(src_tri), np.float32(dst_tri))
+
+    # Apply the Affine Transform just found to the src image
+    dst = cv2.warpAffine(src, warp_mat, (size[0], size[1]), None, flags=cv2.INTER_LINEAR,
+                         borderMode=cv2.BORDER_REFLECT_101)
+
+    return dst
+
+
+# Check if a point is inside a rectangle
+def rect_contains(rect, point):
+    if not rect[0] < point[0] < rect[0] + rect[2] or\
+       not rect[1] < point[1] < rect[1] + rect[3]:
+        return False
+    else:
+        return True
+
+
+# calculate delanauy triangle
+def calculate_delaunay_triangles(rect, points):
+    # create subdiv
+    subdiv = cv2.Subdiv2D(rect)
+
+    # Insert points into subdiv
+    for p in points:
+        subdiv.insert(p)
+
+    triangle_list = subdiv.getTriangleList()
+
+    delaunay_tri = []
+
+    pt = []
+
+    for t in triangle_list:
+        pt1 = (t[0], t[1])
+        pt2 = (t[2], t[3])
+        pt3 = (t[4], t[5])
+
+        pt += [pt1, pt2, pt3]
+
+        if rect_contains(rect, pt1) and rect_contains(rect, pt2) and rect_contains(rect, pt3):
+            ind = []
+            # Get face-points (from 68 face detector) by coordinates
+            for j in range(0, 3):
+                for k in range(0, len(points)):
+                    if abs(pt[j][0] - points[k][0]) < 1.0 and abs(pt[j][1] - points[k][1]) < 1.0:
+                        ind.append(k)
+                        # Three points form a triangle. Triangle array corresponds to the file tri.txt in FaceMorph
+            if len(ind) == 3:
+                delaunay_tri.append((ind[0], ind[1], ind[2]))
+
+        pt = []
+
+    return delaunay_tri
+
+
+# Warps and alpha blends triangular regions from img1 and img2 to img
+def warp_triangle(img1, img2, t1, t2):
+    # Find bounding rectangle for each triangle
+    r1 = cv2.boundingRect(np.float32([t1]))
+    r2 = cv2.boundingRect(np.float32([t2]))
+
+    # Offset points by left top corner of the respective rectangles
+    t1_rect = []
+    t2_rect = []
+    t2_rect_int = []
+
+    for i in range(0, 3):
+        t1_rect.append(((t1[i][0] - r1[0]), (t1[i][1] - r1[1])))
+        t2_rect.append(((t2[i][0] - r2[0]), (t2[i][1] - r2[1])))
+        t2_rect_int.append(((t2[i][0] - r2[0]), (t2[i][1] - r2[1])))
+
+    # Get mask by filling triangle
+    mask = np.zeros((r2[3], r2[2], 3), dtype=np.float32)
+    cv2.fillConvexPoly(mask, np.int32(t2_rect_int), (1.0, 1.0, 1.0), 16, 0);
+
+    # Apply warpImage to small rectangular patches
+    img1_rect = img1[r1[1]:r1[1] + r1[3], r1[0]:r1[0] + r1[2]]
+    # img2_rect = np.zeros((r2[3], r2[2]), dtype = img1_rect.dtype)
+
+    size = (r2[2], r2[3])
+
+    img2_rect = apply_affine_transform(img1_rect, t1_rect, t2_rect, size)
+
+    img2_rect = img2_rect * mask
+
+    # Copy triangular region of the rectangular patch to the output image
+    img2[r2[1]:r2[1] + r2[3], r2[0]:r2[0] + r2[2]] = img2[r2[1]:r2[1] + r2[3], r2[0]:r2[0] + r2[2]] * (
+            (1.0, 1.0, 1.0) - mask)
+
+    img2[r2[1]:r2[1] + r2[3], r2[0]:r2[0] + r2[2]] = img2[r2[1]:r2[1] + r2[3], r2[0]:r2[0] + r2[2]] + img2_rect
+
+
+def get_points(img):
+    points = []
+
+    detector = dlib.get_frontal_face_detector()
+    predictor = dlib.shape_predictor('model.dat')
+
+    gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+    # only one face
+    face = detector(gray_img)[0]
+    landmarks = predictor(gray_img, face)
+
+    # predictor yields 68 points
+    for point in range(0, 68):
+        x = landmarks.part(point).x
+        y = landmarks.part(point).y
+        points.append((x, y))
+
+    return points
+
+
+def swap_faces(source1, source2):
+    # Read images
+    img1 = cv2.imread(os.path.join(UPLOAD_FOLDER, source1))
+    img2 = cv2.imread(os.path.join(UPLOAD_FOLDER, source2))
+    img1_warped = np.copy(img2)
+
+    # Read array of corresponding points
+    points1 = get_points(img1)
+    points2 = get_points(img2)
+
+    # Find convex hull
+    hull1 = []
+    hull2 = []
+
+    hull_index = cv2.convexHull(np.array(points2), returnPoints=False)
+
+    for i in range(0, len(hull_index)):
+        hull1.append(points1[int(hull_index[i])])
+        hull2.append(points2[int(hull_index[i])])
+
+    # Find delanauy traingulation for convex hull points
+    size_img2 = img2.shape
+    rect = (0, 0, size_img2[1], size_img2[0])
+
+    dt = calculate_delaunay_triangles(rect, hull2)
+
+    if len(dt) == 0:
+        quit()
+
+    # Apply affine transformation to Delaunay triangles
+    for i in range(0, len(dt)):
+        t1 = []
+        t2 = []
+
+        # get points for img1, img2 corresponding to the triangles
+        for j in range(0, 3):
+            t1.append(hull1[dt[i][j]])
+            t2.append(hull2[dt[i][j]])
+
+        warp_triangle(img1, img1_warped, t1, t2)
+
+    # Calculate Mask
+    hull8U = []
+    for i in range(0, len(hull2)):
+        hull8U.append((hull2[i][0], hull2[i][1]))
+
+    mask = np.zeros(img2.shape, dtype=img2.dtype)
+
+    cv2.fillConvexPoly(mask, np.int32(hull8U), (255, 255, 255))
+
+    r = cv2.boundingRect(np.float32([hull2]))
+
+    center = (r[0] + int(r[2] / 2), r[1] + int(r[3] / 2))
+
+    # Clone seamlessly.
+    output = cv2.seamlessClone(np.uint8(img1_warped), img2, mask, center, cv2.NORMAL_CLONE)
+    cv2.imwrite(os.path.join(app.config['UPLOAD_FOLDER'], RESULT_IMG_NAME), output)
+
+
+# Нагло сдул у Анжелы всё, что ниже
+def allowed_file(filename):
+    return '.' in filename and \
+           filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS
+
+
+@app.route('/', methods=['GET', 'POST'])
+def upload():
+    if request.method == 'POST':
+        img1 = request.files['img1']
+        img2 = request.files['img2']
+        if img1 and allowed_file(img1.filename) and img2 and allowed_file(img2.filename):
+            img1.save(os.path.join(app.config['UPLOAD_FOLDER'], img1.filename))
+            img2.save(os.path.join(app.config['UPLOAD_FOLDER'], img2.filename))
+            swap_faces(img1.filename, img2.filename)
+            return redirect(url_for('uploaded_file', filename=RESULT_IMG_NAME))
+
+    return '''
+    <!DOCTYPE html>
+    <html lang="en">
+    <head>
+        <meta charset="UTF-8">
+        <link rel="stylesheet"
+              href="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css"
+              integrity="sha384-ggOyR0iXCbMQv3Xipma34MD+dH/1fQ784/j6cY/iJTQUOhcWr7x9JvoRxT2MZw1T"
+              crossorigin="anonymous">
+        <title>Upload new images</title>
+    </head>
+    <body>
+    <div class="container mt-5">
+        <form id="form" action="" method=post enctype=multipart/form-data>
+            <p><input type=file name=img1></p>
+            <p><input type=file name=img2></p>
+            <button type="submit" class="btn btn-primary">Upload</button>
+        </form>
+    </div>
+    </body>
+    </html>
+    '''
+
+
+@app.route('/uploads/<filename>')
+def uploaded_file(filename):
+    return send_from_directory(app.config['UPLOAD_FOLDER'], filename)
+
+
+if __name__ == '__main__':
+
+    if not os.path.exists('model.dat'):
+        os.system('download_model.sh')
+
+    if not os.path.exists(UPLOAD_FOLDER):
+        os.makedirs(UPLOAD_FOLDER)
+
+    app.run()

hw3/download_model.sh

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+wget http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2 -O model.dat.bz2
+7z x model.dat.bz2
+rm model.dat.bz2