Switched to more modular structure

file_utility.py

@@ -0,0 +1,27 @@
+from PIL import Image
+
+def openImageList(list, resize: bool = True):
+    stack = []
+    for path in list:
+        img = Image.open(path)
+        if resize == True:
+            img = img.resize((1280, 720))
+        stack.append(img)
+    return stack
+
+def saveStacktoFile(stack, quality: int = 75):
+    '''Saves the arrays in the stack returned by the get exposure stack function to files'''
+
+    print("Saving..")
+    i = 0
+    path = []
+    for array in stack:
+        i += 1
+        image = Image.fromarray(array)
+        image.save(f"image_nr{i}.jpg", quality=quality)
+        path.append(f"image_nr{i}.jpg")
+    return path
+
+def saveResultToFile(hdr_image , output_path: str = '/', quality: int = 75):
+    image = Image.fromarray(hdr_image)
+    image.save(f"{output_path}_hdr.jpg", quality=quality)

main.py

@@ -1,9 +1,16 @@
-import numpyHDR
+import numpyHDR as hdr
+import picamburst as pcb
+import file_utility as file
+
+'''Example of a complete HDR process starting with raspicam '''
+
+#Get sequence from raspicam
+stack = pcb.get_exposure_stack()
+
+#Process HDR with mertens fusion and post effects
+result = hdr.process(stack, 1, 1, 1, True)
+
+#Save Result to File
+file = file.saveResultToFile(result, 'hdr/', 75)
+
 
-#Testfile
-hdr = numpyHDR.NumpyHDR()
-liste = ['test_hdr0.jpg','test_hdr1.jpg', 'test_hdr2.jpg']
-hdr.input_image = liste
-hdr.output_path = 'hdr/fused_merten17'
-hdr.compress_quality = 75
-hdr.sequence(1, 1, 1, True)

numpyHDR.py

@@ -1,10 +1,7 @@
 import numpy as np
-from PIL import Image
 
 
-#import matplotlib.pyplot as plt
 
-class NumpyHDR:
 '''Numpy and PIL implementation of a Mertens Fusion alghoritm
 Usage: Instantiate then set attributes:
     input_image = List containing path strings including .jpg Extension
@@ -20,32 +17,10 @@ class NumpyHDR:
         hdr.compress_quality = 50
         hdr.output_path = photos/result/
         hdr.sequence()
-
     returns: Nothing    
 '''
 
-    def __init__(self):
+def simple_clip(fused,gamma):
-        self.input_image: list = []
-        self.output_path: str = '/'
-        self.compress_quality: int = 75
-
-    def plot_histogram(self, image, title="Histogram", bins=256):
-        """Plot the histogram of an image.
-
-        Args:
-            image: A numpy array representing an image.
-            title: The title of the plot.
-            bins: The number of bins in the histogram.
-        """
-        fig, ax = plt.subplots()
-        ax.hist(image.ravel(), bins=bins, color='gray', alpha=0.7)
-        ax.set_title(title)
-        ax.set_xlabel('Pixel value')
-        ax.set_ylabel('Frequency')
-        plt.show()
-    ### Experimental functions above this line. chatGPT sketches
-
-    def simple_clip(self, fused,gamma):
     # Apply gamma correction
     #fused = np.clip(fused, 0, 1)
     fused = np.power(fused, 1.0 / gamma)
@@ -54,8 +29,7 @@ class NumpyHDR:
     #fused = Image.fromarray(fused)
 
     return fused
-
+def convolve2d(image, kernel):
-    def convolve2d(self, image, kernel):
     """Perform a 2D convolution on the given image with the given kernel.
 
     Args:
@@ -95,7 +69,7 @@ class NumpyHDR:
 
     return convolved_image
 
-    def mask(self, img, center=50, width=20, threshold=0.2):
+def mask(img, center=50, width=20, threshold=0.2):
     '''Mask with sigmoid smooth'''
     mask = 1 / (1 + np.exp((center - img) / width))  # Smooth gradient mask
     mask = np.where(img > threshold, mask, 1)  # Apply threshold to the mask
@@ -103,7 +77,7 @@ class NumpyHDR:
     #plot_histogram(mask, title="mask")
     return mask
 
-    def highlightsdrop(self, img, center=0.7, width=0.2, threshold=0.6, amount=0.08):
+def highlightsdrop(img, center=0.7, width=0.2, threshold=0.6, amount=0.08):
     '''Mask with sigmoid smooth targets bright sections'''
     mask = 1 / (1 + np.exp((center - img) / width))  # Smooth gradient mask
     mask = np.where(img > threshold, mask, 0)  # Apply threshold to the mask
@@ -114,7 +88,7 @@ class NumpyHDR:
 
     return img_adjusted
 
-    def shadowlift(self, img, center=0.2, width=0.1, threshold=0.2, amount= 0.05):
+def shadowlift(img, center=0.2, width=0.1, threshold=0.2, amount= 0.05):
     '''Mask with sigmoid smooth targets bright sections'''
     mask = 1 / (1 + np.exp((center - img) / width))  # Smooth gradient mask
     mask = np.where(img < threshold, mask, 0)  # Apply threshold to the mask
@@ -125,7 +99,7 @@ class NumpyHDR:
 
     return img_adjusted
 
-    def mertens_fusion(self, image_paths, gamma=2.2, contrast_weight=0.2):
+def mertens_fusion(stack, gamma=1, contrast_weight=1):
     """Fuse multiple exposures into a single HDR image using the Mertens algorithm.
 
     Args:
@@ -136,14 +110,11 @@ class NumpyHDR:
     Returns:
         The fused HDR image.
     """
-        # Load the input images and convert them to floating-point format.
-        images = []
-        for path in image_paths:
-            img = Image.open(path)
-            img = img.resize((1280, 720))
-            img = np.array(img).astype(np.float32) / 255.0
-            img = np.power(img, gamma)
 
+    images = []
+    for array in stack:
+        img = np.array(array).astype(np.float32) / 255.0
+        img = np.power(img, gamma)
         images.append(img)
 
     # Compute the weight maps for each input image based on the local contrast.
@@ -152,7 +123,7 @@ class NumpyHDR:
     for img in images:
         gray = np.dot(img, [0.2989, 0.5870, 0.1140])
         kernel = np.array([[-1, -1, -1], [-1, 7, -1], [-1, -1, -1]])
-            laplacian = np.abs(self.convolve2d(gray, kernel))
+        laplacian = np.abs(convolve2d(gray, kernel))
         weight = np.power(laplacian, contrast_weight)
         weight_maps.append(weight)
 
@@ -168,7 +139,8 @@ class NumpyHDR:
 
     return fused
 
-    def compress_dynamic_range(self, image):
+def compress_dynamic_range(image):
+    '''Compress dynamic range based on percentile'''
     # Find the 1st and 99th percentiles of the image
     p1, p99 = np.percentile(image, (0, 99))
 
@@ -183,7 +155,7 @@ class NumpyHDR:
 
     return new_image
 
-    def compress_dynamic_range_histo(self, image, new_min=0.01, new_max=0.99):
+def compress_dynamic_range_histo(image, new_min=0.01, new_max=0.99):
     """Compress the dynamic range of an image using histogram stretching.
 
     Args:
@@ -208,37 +180,39 @@ class NumpyHDR:
 
     return new_image
 
-    def open_image(filename):
+def process(stack, gain: float = 1, weight: float = 1, gamma: float = 1, post: bool = True):
-        # Open the image file in binary mode
+    '''Processes the stack that contains a list of arrays form the camera into a PIL compatible clipped output array
-        with open(filename, 'rb') as f:
+    Args:
-            # Read the binary data from the file
+        stack : input list with arrays
-            binary_data = f.read()
+        gain : low value low contrast, high value high contrast and brightness
+        weight: How much the extracted portions of each image gets allpied to to the result "HDR effect intensity"
+        gamma: Post fusion adjustment of the gamma.
+        post: Enable or disable effects applied after the fusion True or False, default True
+            shadowlift = slightly lifts the shadows
+            Args:
+                center: position of the filter dropoff
+                width: range of the gradient, softness
+                threshold: sets the threshhold form 0 to 1 0.1= lowest blacks....
+                amount: How much the shadows should be lifted. Values under 0.1 seem to be good.
+            returns:
+                Hdr image with lifted blacks clipped to 0,1 range
 
-        # Convert the binary data to a 1D numpy array of uint8 type
+            compress dynamic range:
-        image_array = np.frombuffer(binary_data, dtype=np.uint8)
+                Tries to fit the image better into the available range. Less loggy image.
+    Returns:
+        HDR Image as PIL compatible array.
 
-        # Reshape the 1D array into a 2D array with the correct image shape
+    '''
-        # (Assuming a 3-channel RGB image with shape (height, width))
-        height = int.from_bytes(binary_data[16:20], byteorder='big')
-        width = int.from_bytes(binary_data[20:24], byteorder='big')
-        image_array = image_array[24:].reshape((height, width, 3))
-
-        return image_array
-
-    def sequence(self, gain: float = 0.8, weight: float = 0.5, gamma: float = 1, post: bool = True):
-        '''gain setting : the higher the darker, good range from 0.4- 1.0'''
-        print(self.input_image)
-        hdr_image = self.mertens_fusion(self.input_image ,gain, weight)
 
+    hdr_image = mertens_fusion(stack ,gain, weight)
     if post == True:
-
         #hdr_image = self.highlightsdrop(hdr_image)
-            hdr_image = self.shadowlift(hdr_image)
+        hdr_image = shadowlift(hdr_image)
-            hdr_image = self.compress_dynamic_range(hdr_image)
+        hdr_image = compress_dynamic_range(hdr_image)
         #hdr_image = self.compress_dynamic_range_histo(hdr_image)
 
-        hdr_image = self.simple_clip(hdr_image,gamma)
+    hdr_image = simple_clip(hdr_image,gamma)
-        image = Image.fromarray(hdr_image)
+    return hdr_image
-        image.save(f"{self.output_path}_hdr.jpg", quality=self.compress_quality)
+
 
 

picamburst.py

@@ -13,7 +13,11 @@ picam2.set_controls({"AwbEnable": 1})
 picam2.set_controls({"AeEnable": 1})
 picam2.set_controls({"AfMode": controls.AfModeEnum.Manual })
 picam2.set_controls({"LensPosition": 0.0 })
-#picam2.set_controls({"AnalogueGain": 1.0})
+
+def get_exposure_stack(factor: int = 2):
+    '''Returns a list with arrays that contain different exposures controlled by the factor.'''
+    '''The Autoamtically set exposure of the first frame is saved and multiplied or divided ba the factor to get the above or under epxosures.'''
+
     picam2.start()
     time.sleep(1)
 
@@ -37,7 +41,7 @@ while confirmed != gain_start in range(gain_start -0.1, gain_start +0.1):
     ev1 = picam2.capture_array()
     #print("Picture one is done")
 
-ev_low = int(exposure_start / 2)
+    ev_low = int(exposure_start / factor)
     picam2.set_controls({"ExposureTime": ev_low})
     confirmed = picam2.capture_metadata()["ExposureTime"]
     while confirmed not in range(ev_low -100, ev_low + 100 ):
@@ -48,7 +52,7 @@ while confirmed not in range(ev_low -100, ev_low + 100 ):
     ev2 = picam2.capture_array()
     #print("Picture 2 is captured to array")
 
-ev_high = int(exposure_start * 2)
+    ev_high = int(exposure_start * factor)
     picam2.set_controls({"ExposureTime": ev_high})
     confirmed = picam2.capture_metadata()["ExposureTime"]
     while confirmed not in range(ev_high -100, ev_high + 100 ):
@@ -58,15 +62,17 @@ while confirmed not in range(ev_high -100, ev_high + 100 ):
     #print("3",confirmed)
     ev3 = picam2.capture_array()
     #print("Picture 3 is captured")
-print("Saving..")
-
-image = Image.fromarray(ev1)
-image.save(f"test_hdr0.jpg", quality=50)
-
-image = Image.fromarray(ev2)
-image.save(f"test_hdr1.jpg", quality=50)
-
-image = Image.fromarray(ev3)
-image.save(f"test_hdr2.jpg", quality=50)
 
     picam2.stop()
+    stack = [ev1,ev2,ev3]
+
+    return stack
+
+
+
+
+
+
+
+
+