代做M2 Project Progress Report - Virtual Hair Style Fit 2024代写留学生数据结构程序
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October 17, 2024
1 Project Objective
As Figure 1 shows, this project aims to change hair color with hairstyle fitting, including:
• Shape Transform.: Transform. the hair outline in target images to fit with reference im- ages as precisely as possible.
• Color Change: Map hair color into expectation directly.
Figure 1: Shape Transform.
2 Entities and Relationships
So far, we have identified the following components:
• Element: Pixels of images, atomic units processed for desired appearance.
• Entity: All images used in the project are collections of entities. Besides the reference image, a target image is necessary to transform. the hair shape.
• Attribute: Positions and colors of pixels in images are attributes we focus on, especially hair part(it meas that we supposed we have identified the position of hair in images). In an image, a pixel has a value to represent the color, and pixels with specified positions can make up the outline and shape of contents.
Let us draw a conclusion about relationships among attributes, elements and entities.
Figure 2: Entity Relation
In summary, the entities and elements in this problem are followed:
• The reference image R hasn pixels for hair part. The pixel at position i with value Ri , for i = 1,...,n.
• Supposed we have already identified the hair positions. Therefore, the target image T has m pixels constituting hairs. The pixel at position j with value Tj , for j = 1,...,n.
• The resultant image R* has same n pixels with origin one. The pixel at position i with content Ri(*), for i = 1,...,n.
The relationship between the entities and elements has following points:
• Pixels at non-hair positions should keep unchanged as possible, like human face, back- ground. However, it is possible to change these parts little for better fit.
• Give known hair position of images, hair pixels are primary elements we focus on.
Generally, we can abstract this task as a function F to transform. pixels of hair parts in target image to fit with reference image for the least error:
where i is same position of pixels among reference and resultant images
3 Problem Definition
• Objective: Given a target image with known position pixels of hairs, our design F trans- forms hair pixels to fit with reference image with least error.
• Inputs:
– Reference image R: Contains n pixels, where each pixel at position i has value Ri , for j = 1,..., n.
– Target image T: Contains n pixels for hair part, where pixel at position j with value Tj , for j = 1,...,n.
• Outputs:
You seem to assume correspondence is known. If so, you can't have known correspondence between any image pixel, You can only have known correspondence between some identified landmark points. Where are these landmark points?
– Transform. function F : F represents the transformation including shape and color change:
Rj(*) = F(Rj ) = C(T(Rj )) (2)
T is shape transformation function, and C is color change function.
– Resultant target image R* : The transformed image that displays the modified hair
on the target image, it contains n pixels, each is transformed by Rj(*) = F(Rj )
• Optimization Objective: Minimize the difference between the resultant image and the origin image:
Minimize ||F(Ri ) - Ri || 2 (3)
• Constraints:
– Shape Constraint: The constraint using landmark points ensure that hair does not extend below the top of the eyebrows or beyond the sides of the face.
– Color Consistency Constraint: Average color α is used to ensure color transfor- mations maintain consistency with surrounding pixels.
4 Algorithm Design
Algorithm 1 Hair Style Fit
1: Initialize R* = R, i.e., set each pixel Ri(*) = Ri for all i
2: Initialize error E = ∞, threshold τ > 0
3: Identify landmark points in R* :
• Set leyebrow top as the highest pixel point where hair should not extend below (e.g., above the eyebrows).
• Set lface left and lface right as the outermost pixels on the left and right sides of the face, respectively.
• Map corresponding landmark points between T and R.
4: Apply Thin Plate Spline (TPS) Transformation:
• Utilize TPS to perform. a smooth transformation of T based on the mapped landmark points.
• Adjust T to achieve a natural transition towards the reference hair shape in R* .
5: Smooth and refine T:
• Use the TPS transformed positions to further smooth the edges of the hair region.
• Adjust details to correct potential artifacts introduced by transformation.
6: Adjust hair from T to fit the reference image:
7: *Note: This step involves the correction of hair position to avoid unnatural breaks and to ensure it aligns correctly with the facial landmarks, as illustrated in Figure. 3.
8: for each pixel pi in T do
9: if pixel pi is below leyebrow top then
10: Trim T by removing pixels below leyebrow top
11: end if
12: if pixel pi is beyond lface left or lface right then
13: Trim T by adjusting pixels beyond lface left or lface right
14: end if
15: end for
16: Color Adjustment of Hair:
• Compute the average color of hair in R* as CR = AverageColor(R* ).
• Compute the average color of target color(the color which we want to transform to) as CT = AverageColor(T).
• Calculate the scaling factors for color adjustment:
– scaler = CR(*)/CT for each color channel (e.g., RGB).
• Apply pi = pi × scaler on each pixels pi in T:
17: Global Transformation:
18: Initialize transformation parameters: scales = 1, rotation θ = 0, translation t = [0, 0]
19: repeat
20: Set E′ = E
21: for each pixel pi in T do
22: Set d = ∞
23: for each pixel qj in R* do
24: if ||qj − pi || < d then
25: Set c(pi ) = qj , d = ||qj − pi ||
26: end if
27: end for
28: Compute local transformation Ti from pi to c(pi )
29: Update global transformation:
s = s + α × (scale of Ti − s)
θ = θ + α × (rotation of Ti − θ)
t = t + α × (translation of Ti − t)
30: where α is an coefficient to adjust the step (e.g., 0.1)
31: end for
32: Apply global transformation to T: T′ = Transform(T,s,θ, t)
33: Compute new error E = Error(T′ , R)
34: until |E − E′ | < τ
35: Output final transformed hair mask T′
36: Overlay T′ onto R* to generate final image
Figure 3: Landmark Points
5 Justification
5.1 Correctness
The correctness of the algorithm can be justified in the following aspects:
• Landmark Detection: The algorithm identifies facial landmarks, like the top of the eyebrows and the edges of the face, to enforce constraints that hair cannot extend above these points. This prevents the hairstyle. from obscuring the face and maintains a natural appearance. If hair from the target image falls below the eyebrow line, the algorithm trims it for a more visually appealing look.
• Transformation: TPS ensures a seamless transformation and natural appearance after changing hairstyle.
• Color Change: During the color-changing step, the algorithm calculates the average hair color from both images and applies scaling factors, ensuring the final image looks natural and visually appealing.
How does the algorithm satisfies optimization objective and constraints?
5.2 Convergence
The convergence of the algorithm is established through iteration and error minimization:
• Error Reduction: The algorithm initializes an error metric E to quantify the difference between the transformed hairstyle and the reference hairstyle. In each iteration, it updates the hair pixels to minimize the error until it sufficiently converges to the threshold.
• Finite Iterations: The iterations are finite because the algorithm alters a limited number of pixels within defined masks, adhering to constraints set by facial landmarks. This ensures that the algorithm converges to a solution instead of entering an infinite loop.