v3

ROI_face.py

@@ -21,13 +21,13 @@ def roi(frame):
         color = (255, 255, 255)  # Color in BGR format (white)
         #        thickness = 2
         mask = np.zeros((height, width), dtype=np.uint8)
-        cv2.ellipse(mask, center_ellipse, axes, angle, 0, 360, 255, -1)  # 255 for a white oval
+        cv2.ellipse(mask, center_ellipse, axes, angle, 0, 360, 0, -1)  # 255 for a white oval
         # Apply the inverted mask to the face
         result = cv2.bitwise_and(frame, frame, mask=mask)
 
-        #black_mask = (result[:, :, 0] == 0) & (result[:, :, 1] == 0) & (result[:, :, 2] == 0)
+        black_mask = (result[:, :, 3] == 0)# & (result[:, :, 1] == 0) & (result[:, :, 2] == 0)
         #print(result[c_x2, c_y2,1])
         # Replace black pixels with white pixels
-        #result[black_mask] = [result[10, 10, 0], result[10, 10, 1], result[10, 10, 2]]
+        result[black_mask] = [result[0, 0, 0], result[0, 0, 1], result[0, 0, 2]]
 
-        return result
+        return frame

coordinates_change.py

@@ -0,0 +1,67 @@
+import numpy as np
+
+def coord(contours, x_offset, y_offset, x_scale, y_scale):
+    new_contours = []
+    for i in range(len(contours)):
+        for j in range(len(contours[i])):
+            x = contours[i][j][0][0]*x_scale + x_offset
+            y = contours[i][j][0][1]*y_scale + y_offset
+            new_contours.append([x, y])
+    return new_contours
+
+def scale_calculator(contours, size):
+    x_max = contours[0][0][0][0]
+    x_min = contours[0][0][0][0]
+    y_max = contours[0][0][0][1]
+    y_min = contours[0][0][0][1]
+
+    for i in range(len(contours)):
+        for j in range(len(contours[i])):
+            x = contours[i][j][0][0]
+            y = contours[i][j][0][1]
+            if x_min > x:
+                x_min = x
+            if x > x_max:
+                x_max = x
+            if y_min > y:
+                y_min = y
+            if y > y_max:
+                y_max = y
+    # print([[x_min, x_max], [y_min, y_max]])            #display max and min in x and y
+
+    x_scale = 1.0
+    y_scale = 1.0
+    if size[0] != 0:
+        x_scale = size[0]/(x_max - x_min)
+    if size[1] != 0:
+        y_scale = size[1]/(y_max - y_min)
+    if size[0] == 0:
+        x_scale = y_scale
+    if size[1] == 0:
+        y_scale = x_scale
+
+    x_min = x_min*x_scale
+    y_min = y_min*y_scale
+
+    return [x_min, y_min, x_scale, y_scale]
+
+
+def get_size(coords):
+    x_max = coords[0][0]
+    x_min = coords[0][0]
+    y_max = coords[0][1]
+    y_min = coords[0][1]
+
+    for i in range(len(coords)):
+        x = coords[i][0]
+        y = coords[i][1]
+        if x_min > x:
+            x_min = x
+        if x > x_max:
+            x_max = x
+        if y_min > y:
+            y_min = y
+        if y > y_max:
+            y_max = y
+
+    return [[x_min, x_max], [y_min, y_max]]         #display max and min in x and y

get_head.py

@@ -1,37 +1,39 @@
 import cv2
 import time
 
-cap = cv2.VideoCapture(0)
-
 def get_image():
-    # Set the window name
+    cap = cv2.VideoCapture(0)
+
     window_name = 'Camera Capture'
+    countdown_duration = 60
 
-    # Get the default frames per second (fps) of the camera
-    fps = int(cap.get(cv2.CAP_PROP_FPS))
+    # Check if the camera is opened successfully
+    if not cap.isOpened():
+        print("Error: Could not open the camera.")
+        return None
 
-    # Set the countdown duration in seconds
-    countdown_duration = 10
-    # Start the countdown
     for countdown in range(countdown_duration, 10, -1):
         ret, frame = cap.read()
 
-        # Display the camera feed
-        cv2.imshow(window_name, frame)
-        # Display the countdown on the image
+        # Check if the frame is read successfully
+        if not ret:
+            print("Error: Could not read frame.")
+            return None
+
         font = cv2.FONT_HERSHEY_SIMPLEX
         cv2.putText(frame, str(countdown)[0], (10, 30), font, 1, (0, 255, 0), 2, cv2.LINE_AA)
         cv2.imshow(window_name, frame)
+        cv2.waitKey(100)
 
-        # Wait for 1 second and check for key press
-        if cv2.waitKey(100) & 0xFF == 27:  # 27 is the ASCII code for the 'ESC' key
-            break
+    output_path = 'image.png'
+    cv2.imwrite(output_path, frame)
 
-    # Take a picture at the end of the countdown
-    ret, frame = cap.read()
-    #cv2.imwrite("image.png", frame)
-
-    # Release the camera and close the OpenCV window
+    # Release the camera capture object
     cap.release()
-    cv2
-    return frame
+
+    # Destroy all OpenCV windows
+    cv2.destroyAllWindows()
+
+    print(f"Image saved successfully at '{output_path}'.")
+    return output_path
+

main.py

@@ -1,17 +1,42 @@
 import cv2
+
 import show_img_conditions
-import ROI_face
+import remove_bg
 import get_head
+import coordinates_change
 
+import numpy as np
 #show_img_conditions.show()
-gray = get_head.get_image()
-cv2.imshow('Grey Image', gray)
-face = ROI_face.roi(gray)
-cv2.imshow('Face', face)
-edges = cv2.Canny(face, 10, 100)
-cv2.imshow('Image avec Contours de Visage', edges)
 
+image_path = get_head.get_image()
+image = cv2.imread(image_path)
+#cv2.imshow('Image', image)
+
+image_path = 'image.png'
+
+background_path = remove_bg.rmbg(image_path)
+background = cv2.imread(background_path)
+#cv2.imshow('Image without background', background)
+
+gray_image = cv2.cvtColor(background, cv2.COLOR_BGR2GRAY)
+edges = cv2.Canny(gray_image, 20, 80)
+cv2.imshow("contours", edges)
 cv2.waitKey(0)
-cv2.destroyAllWindows()
 
+contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
 
+x_offset = 0            # offset in the x_axis
+y_offset = 0            # offset in the y_axis
+new_size = [150, 0]    # new size of the picture in mm, write 0 if you want to be proportional
+
+[x_min, y_min, x_scale, y_scale] = coordinates_change.scale_calculator(contours, new_size)
+
+x_offset = x_offset - x_min
+y_offset = y_offset - y_min
+
+new_coords = coordinates_change.coord(contours, x_offset, y_offset, x_scale, y_scale)
+
+size = coordinates_change.get_size(new_coords)
+print("x offset : ", int(x_offset+x_min), "mm")
+print("y offset : ", int(y_offset+y_min), "mm")
+print("size : ", int(size[0][1] - size[0][0]), "x", int(size[1][1] - size[1][0]), "mm")

remove_bg.py

@@ -0,0 +1,11 @@
+from rembg import remove
+from PIL import Image
+
+output_path = 'output.png'
+
+def rmbg(input_path):
+    input = Image.open(input_path)
+    output = remove(input)
+    output.save(output_path)
+    return output_path
+

test2.py

@@ -1,35 +1,35 @@
 import cv2
-import numpy as np
 
-# Load an image
-image = cv2.imread('image.jpg')
+# Open a connection to the webcam (default camera index is 0)
+cap = cv2.VideoCapture(0)
 
-# Convert the image to grayscale
-gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+# Check if the camera is opened successfully
+if not cap.isOpened():
+    print("Error: Could not open the camera.")
+    exit()
 
-# Apply thresholding to create a binary image
-_, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
+# Create a window to display the webcam feed
+window_name = 'Webcam Feed'
+cv2.namedWindow(window_name, cv2.WINDOW_NORMAL)
 
-# Find contours in the binary image
-contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+while True:
+    # Read a frame from the webcam
+    ret, frame = cap.read()
 
-# Choose the contour you want to remove (in this example, the first contour is removed)
-contour_to_remove = contours[0]
+    # Check if the frame is read successfully
+    if not ret:
+        print("Error: Could not read frame.")
+        break
 
-# Create a mask with the same shape as the image
-mask = np.zeros_like(image)
+    # Display the frame in the window
+    cv2.imshow(window_name, frame)
 
-# Fill the contour with a background color (white in this case)
-cv2.fillPoly(mask, [contour_to_remove], (255, 255, 255))
+    # Break the loop if 'q' key is pressed
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
 
-# Invert the mask to keep the rest of the image
-mask_inv = cv2.bitwise_not(mask)
+# Release the camera capture object
+cap.release()
 
-# Bitwise AND the original image with the inverted mask
-result = cv2.bitwise_and(image, mask_inv)
-
-# Display the original and processed images
-cv2.imshow('Original Image', image)
-cv2.imshow('Result', result)
-cv2.waitKey(0)
+# Destroy the OpenCV window
 cv2.destroyAllWindows()