feat: add histogram, erosion and dilatation

app.py

@@ -30,6 +30,18 @@ def handle_contrast(output_image, contrast: list):
     return output_image
 
 
+# Fonction pour modifier la morphologie
+def handle_morphology(image, morphology):
+    if image is None:
+        return None
+    for trans in morphology:
+        if trans == "Erosion":
+            image = improcess.erosion(image)
+        elif trans == "Dilatation":
+            image = improcess.dilatation(image)
+    return image
+
+
 # Fonction pour egaliser l'histogramme
 def equalize_hist(image, equalize):
     if equalize == True:
@@ -67,12 +79,19 @@ with gr.Blocks(theme="Zarkel/IBM_Carbon_Theme") as demo:
         )
         with gr.Group():
             output_img = gr.Image(
-                "./profile.jpeg", label="Transformed image", scale=2, interactive=False
+                "./profile.jpeg",
+                label="Transformed image",
+                scale=2,
+                interactive=False,
+                format="png",
             )
             btn = gr.Button(value="Reset")  # Bouton de réinitialisation
     with gr.Row(variant="panel"):
         with gr.Group():
-            equalize_img = gr.Checkbox(label="Equalize image")
+            morphology = gr.CheckboxGroup(
+                choices=["Dilatation", "Erosion"], label="Morphology"
+            )
+            # equalize_img = gr.Checkbox(label="Equalize image")
             gamma = gr.Slider(
                 minimum=0, maximum=1, label="Luminosity", interactive=True, value=0
             )
@@ -95,6 +114,7 @@ with gr.Blocks(theme="Zarkel/IBM_Carbon_Theme") as demo:
                 interactive=True,
                 label="Extract feature",
             )
+            show_histogram = gr.Button(value="Show Histogram")
 
     # Mise à jour de l'image lorsqu'un filtre est appliqué
     filter.change(fn=choose_filter, inputs=[input_img, filter], outputs=output_img)
@@ -114,12 +134,22 @@ with gr.Blocks(theme="Zarkel/IBM_Carbon_Theme") as demo:
     contrast.change(
         fn=handle_contrast, inputs=[output_img, contrast], outputs=output_img
     )
+    # Mise à jour de l'image avec une transformation morphologique
+    morphology.change(
+        fn=handle_morphology, inputs=[input_img, morphology], outputs=output_img
+    )
     # Egaliser l'histogramme de l'image
-    equalize_img.change(fn=equalize_hist, inputs=[input_img], outputs=[output_img])
+    # equalize_img.change(fn=equalize_hist, inputs=[input_img], outputs=[output_img])
     # Effectuer une rotation de l'image
     angle.change(fn=improcess.rotate, inputs=[input_img, angle], outputs=[output_img])
 
+    # Extraire les caractéristiques
     features.select(fn=detect_feat, inputs=[input_img, features], outputs=[output_img])
+
+    # Afficher l'histogram
+    show_histogram.click(
+        fn=improcess.return_histogram_path, inputs=[input_img], outputs=[output_img]
+    )
 # Lancer l'application
 if __name__ == "__main__":
     demo.launch()

improcess.py

@@ -1,7 +1,72 @@
 import cv2 as cv
 import numpy as np
-from skimage import exposure
+from skimage import exposure, morphology
+from skimage.filters import threshold_mean, threshold_otsu
 from scipy import signal
+import matplotlib.pyplot as plt
+
+
+def show_histogram(image):
+    _, axes = plt.subplots(1, 2)
+    if image.ndim == 2:
+        hist = exposure.histogram(image)
+        axes[0].imshow(image, cmap=plt.get_cmap("gray"))
+        axes[0].set_title("Image")
+        axes[1].plot(hist[0])
+        axes[1].set_title("Histogram")
+    else:
+        axes[0].imshow(image)
+        axes[0].set_title("Image")
+        axes[1].set_title("Histogram")
+        colors = ["red", "green", "blue"]
+        for i, color in enumerate(colors):
+            axes[1].plot(exposure.histogram(image[..., i])[0], color=color)
+    plt.show()
+
+
+def return_histogram_path(image):
+    if image.ndim == 2:
+        hist = exposure.histogram(image)
+        plt.plot(hist[0])
+        plt.xlabel("Pixel Intensity")
+        plt.ylabel("Frequency")
+        plt.title("Histogram")
+    else:
+        plt.title("Histogram")
+        colors = ["red", "green", "blue"]
+        for i, color in enumerate(colors):
+            plt.plot(exposure.histogram(image[..., i])[0], color=color)
+    plt.savefig("histogram.png")
+    plt.close()
+    return "histogram.png"
+
+
+def mean_treshold(image):
+    if image.ndim == 3:
+        image = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
+    thresh = threshold_mean(image)
+    binary = (image > thresh) * 225
+    return binary.astype("uint8")
+
+
+def otsu_treshold(image):
+    if image.ndim == 3:
+        image = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
+    thresh = threshold_otsu(image)
+    binary = (image > thresh) * 255
+    return binary.astype("uint8")
+
+
+def dilatation(image):
+    bin_img = binarize(image) / 255
+    dilation = (morphology.binary_dilation(image=bin_img)) * 255
+    return dilation.astype("uint8")
+
+
+def erosion(image):
+    bin_img = binarize(image) / 255
+    erosion = (morphology.erosion(image=bin_img)) * 255
+    return erosion.astype("uint8")
 
 
 def resize(image: np.ndarray, scale: float = 1.0) -> np.ndarray:
@@ -37,7 +102,9 @@ def negative(image) -> np.ndarray:
 
 
 def gray_scale(image):
-    return cv.cvtColor(image, cv.COLOR_RGB2GRAY).astype("uint8")
+    if image.ndim == 3:
+        return cv.cvtColor(image, cv.COLOR_RGB2GRAY).astype("uint8")
+    return image
 
 
 def binarize(image) -> np.ndarray:
@@ -92,8 +159,7 @@ def rotate(img, angle):
 
 
 def find_contours(image):
-    gray_image = gray_scale(image)
-    _, thresh = cv.threshold(gray_image, 125, 255, cv.THRESH_BINARY)
+    thresh = binarize(image)
     contours, _ = cv.findContours(thresh, cv.RETR_LIST, cv.CHAIN_APPROX_NONE)
     blank = np.zeros(image.shape, dtype="uint8")
     contour_img = cv.drawContours(blank, contours, -1, (255, 255, 255), 2)
@@ -107,7 +173,10 @@ def find_edges(image):
 
 if __name__ == "__main__":
     image = cv.imread("profile.jpeg")
-
+    # show_histogram(image)
     cv.imshow("normal", image)
-    cv.imshow("Gamma", find_contours(image))
+    cv.imshow(
+        "Gamma",
+        erosion(image),
+    )
     cv.waitKey(0)