Spaces:
Sleeping
Sleeping
Siyun He
commited on
Commit
·
5ed34fb
1
Parent(s):
52be6f8
add lip color
Browse files
app.py
CHANGED
@@ -59,6 +59,108 @@ def change_glasses():
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overlay = cv2.imread(f'glasses/glass{num}.png', cv2.IMREAD_UNCHANGED)
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return overlay
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# Process frame for overlay and face shape detection
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def process_frame(frame):
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global overlay
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@@ -196,12 +298,12 @@ def save_frame(frame):
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return f"Frame saved as '{filename}'"
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-
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-
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# Gradio webcam input
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def webcam_input(frame, transform):
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frame, face_shape, glass_shape = process_frame(frame)
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-
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return frame, face_shape, glass_shape
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# Gradio Interface
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@@ -211,6 +313,8 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="purple", secondary_hue="blue"))
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with gr.Group(elem_classes=["my-group"]):
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transform = gr.Dropdown(choices=["cartoon", "edges", "sepia", "negative", "sketch", "blur", "none"],
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value="none", label="Select Filter")
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gr.Markdown("Click the Webcam icon to start the camera, and then press the record button to start the virtual try-on.")
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input_img = gr.Image(sources=["webcam"], type="numpy", streaming=True)
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gr.Markdown("Face Shape and Recommended Glass Shape")
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@@ -219,7 +323,7 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="purple", secondary_hue="blue"))
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next_button = gr.Button("Next Glasses➡️")
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save_button = gr.Button("Save as a Picture📌")
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input_img.stream(webcam_input, [input_img, transform], [input_img, face_shape_output, glass_shape_output], stream_every=0.1)
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with gr.Row():
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next_button.click(change_glasses, [], [])
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with gr.Row():
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overlay = cv2.imread(f'glasses/glass{num}.png', cv2.IMREAD_UNCHANGED)
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return overlay
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+
def change_lip_color(frame, color_name='none'):
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# Define a mapping from color names to BGR values
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color_map = {
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'classic_red': (255, 0, 0), # Classic red
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'deep_red': (139, 0, 0), # Deep red
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'cherry_red': (205, 0, 0), # Cherry red
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'rose_red': (204, 102, 0), # Rose red
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'wine_red': (128, 0, 0), # Wine red
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'brick_red': (128, 64, 0), # Brick red
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'coral_red': (255, 128, 0), # Coral red
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'berry_red': (153, 0, 0), # Berry red
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'ruby_red': (255, 17, 0), # Ruby red
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'crimson_red': (220, 20, 60), # Crimson red
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}
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# Get the BGR color from the color name
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color = color_map.get(color_name, None)
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# If 'none' is selected, return the original frame
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if color is None:
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return frame
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# Convert to RGB for processing
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frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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results = face_mesh.process(frame_rgb)
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if results.multi_face_landmarks:
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for face_landmarks in results.multi_face_landmarks:
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# Define the region for the upper lip using landmark indices
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upper_lip_region = np.array([
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(face_landmarks.landmark[61].x * frame.shape[1], face_landmarks.landmark[61].y * frame.shape[0]),
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(face_landmarks.landmark[185].x * frame.shape[1], face_landmarks.landmark[185].y * frame.shape[0]),
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(face_landmarks.landmark[40].x * frame.shape[1], face_landmarks.landmark[40].y * frame.shape[0]),
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(face_landmarks.landmark[39].x * frame.shape[1], face_landmarks.landmark[39].y * frame.shape[0]),
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(face_landmarks.landmark[37].x * frame.shape[1], face_landmarks.landmark[37].y * frame.shape[0]),
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(face_landmarks.landmark[0].x * frame.shape[1], face_landmarks.landmark[0].y * frame.shape[0]),
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(face_landmarks.landmark[267].x * frame.shape[1], face_landmarks.landmark[267].y * frame.shape[0]),
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(face_landmarks.landmark[269].x * frame.shape[1], face_landmarks.landmark[269].y * frame.shape[0]),
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(face_landmarks.landmark[270].x * frame.shape[1], face_landmarks.landmark[270].y * frame.shape[0]),
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(face_landmarks.landmark[409].x * frame.shape[1], face_landmarks.landmark[409].y * frame.shape[0]),
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(face_landmarks.landmark[291].x * frame.shape[1], face_landmarks.landmark[291].y * frame.shape[0]),
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(face_landmarks.landmark[61].x * frame.shape[1], face_landmarks.landmark[61].y * frame.shape[0])
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], np.int32)
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# Define the region for the lower lip using landmark indices
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lower_lip_region = np.array([
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(face_landmarks.landmark[61].x * frame.shape[1], face_landmarks.landmark[61].y * frame.shape[0]),
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(face_landmarks.landmark[146].x * frame.shape[1], face_landmarks.landmark[146].y * frame.shape[0]),
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(face_landmarks.landmark[91].x * frame.shape[1], face_landmarks.landmark[91].y * frame.shape[0]),
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(face_landmarks.landmark[181].x * frame.shape[1], face_landmarks.landmark[181].y * frame.shape[0]),
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(face_landmarks.landmark[84].x * frame.shape[1], face_landmarks.landmark[84].y * frame.shape[0]),
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(face_landmarks.landmark[17].x * frame.shape[1], face_landmarks.landmark[17].y * frame.shape[0]),
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(face_landmarks.landmark[314].x * frame.shape[1], face_landmarks.landmark[314].y * frame.shape[0]),
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(face_landmarks.landmark[405].x * frame.shape[1], face_landmarks.landmark[405].y * frame.shape[0]),
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(face_landmarks.landmark[321].x * frame.shape[1], face_landmarks.landmark[321].y * frame.shape[0]),
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(face_landmarks.landmark[375].x * frame.shape[1], face_landmarks.landmark[375].y * frame.shape[0]),
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(face_landmarks.landmark[291].x * frame.shape[1], face_landmarks.landmark[291].y * frame.shape[0]),
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(face_landmarks.landmark[61].x * frame.shape[1], face_landmarks.landmark[61].y * frame.shape[0])
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], np.int32)
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lip_region = np.concatenate((upper_lip_region, lower_lip_region), axis=0)
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# Define the region for the teeth using landmark indices
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teeth_region = np.array([
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(face_landmarks.landmark[78].x * frame.shape[1], face_landmarks.landmark[78].y * frame.shape[0]),
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(face_landmarks.landmark[95].x * frame.shape[1], face_landmarks.landmark[95].y * frame.shape[0]),
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(face_landmarks.landmark[88].x * frame.shape[1], face_landmarks.landmark[88].y * frame.shape[0]),
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(face_landmarks.landmark[178].x * frame.shape[1], face_landmarks.landmark[178].y * frame.shape[0]),
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(face_landmarks.landmark[87].x * frame.shape[1], face_landmarks.landmark[87].y * frame.shape[0]),
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(face_landmarks.landmark[14].x * frame.shape[1], face_landmarks.landmark[14].y * frame.shape[0]),
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(face_landmarks.landmark[317].x * frame.shape[1], face_landmarks.landmark[317].y * frame.shape[0]),
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(face_landmarks.landmark[402].x * frame.shape[1], face_landmarks.landmark[402].y * frame.shape[0]),
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(face_landmarks.landmark[318].x * frame.shape[1], face_landmarks.landmark[318].y * frame.shape[0]),
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(face_landmarks.landmark[324].x * frame.shape[1], face_landmarks.landmark[324].y * frame.shape[0]),
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(face_landmarks.landmark[308].x * frame.shape[1], face_landmarks.landmark[308].y * frame.shape[0]),
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(face_landmarks.landmark[78].x * frame.shape[1], face_landmarks.landmark[78].y * frame.shape[0])
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], np.int32)
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# Create a mask for the lip region
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lip_mask = np.zeros(frame.shape[:2], dtype=np.uint8)
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cv2.fillPoly(lip_mask, [lip_region], 255)
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# Create a mask for the teeth region
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teeth_mask = np.zeros(frame.shape[:2], dtype=np.uint8)
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cv2.fillPoly(teeth_mask, [teeth_region], 255)
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# Subtract the teeth mask from the lip mask
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final_mask = cv2.subtract(lip_mask, teeth_mask)
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# Create a colored lip image
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colored_lips = np.zeros_like(frame)
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colored_lips[:] = color
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# Apply the colored lips only to the lip region
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lips_colored = cv2.bitwise_and(colored_lips, colored_lips, mask=final_mask)
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# Combine the original frame with the colored lips
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frame = cv2.bitwise_and(frame, frame, mask=cv2.bitwise_not(final_mask))
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frame = cv2.add(frame, lips_colored)
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return frame
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# Process frame for overlay and face shape detection
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def process_frame(frame):
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global overlay
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return f"Frame saved as '{filename}'"
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def webcam_input(frame, transform, lip_color):
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frame, face_shape, glass_shape = process_frame(frame)
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if transform != "none" and lip_color == "none":
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frame = transform_cv2(frame, transform)
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elif lip_color != "none" and transform == "none":
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frame = change_lip_color(frame, lip_color)
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return frame, face_shape, glass_shape
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# Gradio Interface
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with gr.Group(elem_classes=["my-group"]):
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transform = gr.Dropdown(choices=["cartoon", "edges", "sepia", "negative", "sketch", "blur", "none"],
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value="none", label="Select Filter")
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lip_color = gr.Dropdown(choices=["classic_red", "deep_red", "cherry_red", "rose_red", "wine_red", "brick_red", "coral_red", "berry_red", "ruby_red", "crimson_red", "none"],
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value="none", label="Select Lip Color")
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gr.Markdown("Click the Webcam icon to start the camera, and then press the record button to start the virtual try-on.")
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input_img = gr.Image(sources=["webcam"], type="numpy", streaming=True)
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gr.Markdown("Face Shape and Recommended Glass Shape")
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next_button = gr.Button("Next Glasses➡️")
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save_button = gr.Button("Save as a Picture📌")
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input_img.stream(webcam_input, [input_img, transform, lip_color], [input_img, face_shape_output, glass_shape_output], stream_every=0.1)
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with gr.Row():
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next_button.click(change_glasses, [], [])
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with gr.Row():
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