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| import cv2 | |
| import gradio as gr | |
| import mediapipe as mp | |
| mp_drawing = mp.solutions.drawing_utils | |
| mp_drawing_styles = mp.solutions.drawing_styles | |
| mp_hands = mp.solutions.hands | |
| # For static images: | |
| def fun(img): | |
| print(type(img)) | |
| with mp_hands.Hands(static_image_mode=True,max_num_hands=2,min_detection_confidence=0.5) as hands: | |
| #for idx, file in enumerate(IMAGE_FILES): | |
| # Read an image, flip it around y-axis for correct handedness output (see | |
| # above). | |
| #cv2.imwrite('img.png',img[:,:,::-1]) | |
| #image = cv2.flip(cv2.imread('./img.png'), 1) | |
| image = cv2.flip(img[:,:,::-1], 1) | |
| # Convert the BGR image to RGB before processing. | |
| results = hands.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) | |
| print(type(results.multi_handedness)) | |
| if results.multi_handedness == None: | |
| return(cv2.imread('nohands.png')) | |
| # Print handedness and draw hand landmarks on the image. | |
| print('Handedness:', results.multi_handedness) | |
| #if not results.multi_hand_landmarks: | |
| # continue | |
| image_height, image_width, _ = image.shape | |
| annotated_image = image.copy() | |
| for hand_landmarks in results.multi_hand_landmarks: | |
| print('hand_landmarks:', hand_landmarks) | |
| print( | |
| f'Index finger tip coordinates: (', | |
| f'{hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].x * image_width}, ' | |
| f'{hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].y * image_height})' | |
| ) | |
| mp_drawing.draw_landmarks( | |
| annotated_image, | |
| hand_landmarks, | |
| mp_hands.HAND_CONNECTIONS, | |
| mp_drawing_styles.get_default_hand_landmarks_style(), | |
| mp_drawing_styles.get_default_hand_connections_style()) | |
| res = cv2.flip(annotated_image, 1) | |
| #cv2.imwrite('res.png',res[:,:,::-1]) | |
| print(res.shape) | |
| return res[:,:,::-1] | |
| image = gr.inputs.Image(source='webcam') | |
| keypoint = gr.outputs.Image() | |
| examples = ['ex1.jpg'] | |
| css = """ | |
| footer {display:none !important} | |
| .output-markdown{display:none !important} | |
| .gr-button-primary { | |
| z-index: 14; | |
| height: 43px; | |
| width: 130px; | |
| left: 0px; | |
| top: 0px; | |
| padding: 0px; | |
| cursor: pointer !important; | |
| background: none rgb(17, 20, 45) !important; | |
| border: none !important; | |
| text-align: center !important; | |
| font-family: Poppins !important; | |
| font-size: 14px !important; | |
| font-weight: 500 !important; | |
| color: rgb(255, 255, 255) !important; | |
| line-height: 1 !important; | |
| border-radius: 12px !important; | |
| transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important; | |
| box-shadow: none !important; | |
| } | |
| .gr-button-primary:hover{ | |
| z-index: 14; | |
| height: 43px; | |
| width: 130px; | |
| left: 0px; | |
| top: 0px; | |
| padding: 0px; | |
| cursor: pointer !important; | |
| background: none rgb(37, 56, 133) !important; | |
| border: none !important; | |
| text-align: center !important; | |
| font-family: Poppins !important; | |
| font-size: 14px !important; | |
| font-weight: 500 !important; | |
| color: rgb(255, 255, 255) !important; | |
| line-height: 1 !important; | |
| border-radius: 12px !important; | |
| transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important; | |
| box-shadow: rgb(0 0 0 / 23%) 0px 1px 7px 0px !important; | |
| } | |
| .hover\:bg-orange-50:hover { | |
| --tw-bg-opacity: 1 !important; | |
| background-color: rgb(229,225,255) !important; | |
| } | |
| """ | |
| intf = gr.Interface(fn=fun, inputs=image, outputs=keypoint, title="Keypoint Detection | Data Science Dojo", examples=examples, css=css) | |
| intf.launch(inline=False,debug=True) |