big commit.jpeg

package/app.py

@@ -1,26 +1,110 @@
-import io, sys, os
-
-# Imports the Google Cloud client library
+import io, sys, os, time, rawpy, imageio, progressbar
 from google.cloud import vision
 from google.cloud.vision import types
+from PIL import Image
 
-# Instantiates a client
-client = vision.ImageAnnotatorClient()
+# The name of the image file to annotate
+input_path = os.path.join(sys.path[0], 'package', 'processing', 'input')
+temp_path = os.path.join(sys.path[0], 'package', 'processing', 'temp')
+output_path = os.path.join(sys.path[0], 'package', 'processing', 'output')
 
+# Process a single file in these steps:
+# 1) Create a temporary file
+# 2) Send it to GoogleAPI
+# 3) Read XMP, then write new tags to it
+# 4) Delete temporary file, move NEF/JPEG and XMP
+
+def process_file(file_name):
+    global client
+
+    # Remove the temporary file
+    def _cleanup():
+        if os.path.exists(temp_file_path):
+            # Deletes the temporary file
+            os.remove(temp_file_path)
+
+    # Get the size of the file. Is concerned with filesize type. 1024KiB -> 1MiB
+    def _size(file_path):
+        size, type = os.path.getsize(file_path) / 1024, 'KiB'
+        if size >= 1024: size /= 1024; type = 'MiB'
+        return round(size, 2), type
+
+    # Optimizes a file using JPEG thumbnailing and compression.
+    def _optimize(file_path, size=(512, 512), quality=85):
+        image = Image.open(file_path)
+        image.thumbnail(size, resample=Image.ANTIALIAS)
+        image.save(file_path, format='jpeg', optimize=True, quality=quality)
+
+    base, ext = os.path.splitext(file_name)
+    temp_file_path = os.path.join(temp_path, base + '.jpeg')
+
+    try:
+        # Process the file into a JPEG
+        rgb = rawpy.imread(os.path.join(input_path, file_name)).postprocess()
+        imageio.imsave(os.path.join(temp_file_path), rgb)
+
+        # Information on file sizes
+        print("Raw Size: {} {}".format(*_size(os.path.join(input_path, file_name))), end=' | ')
+        print("Resave Size: {} {}".format(*_size(temp_file_path)), end=' | ')
+        pre = os.path.getsize(temp_file_path)
+        _optimize(temp_file_path)
+        post = os.path.getsize(temp_file_path)
+        print("Optimized Size: {} {} ({}% savings)".format(*_size(temp_file_path), round((1.0 - (post / pre)) * 100), 2) )
+
+        # Open the image, read as bytes, convert to types Image
+        image = Image.open(temp_file_path)
+        bytesIO = io.BytesIO()
+        image.save(bytesIO, format='jpeg')
+        image = types.Image(content=bytesIO.getvalue())
+        
+        # Performs label detection on the image file
+        response = client.label_detection(image=image)
+        labels = response.label_annotations
+        print('\tLabels: {}'.format(', '.join([label.description for label in labels])))    
+    except:
+        _cleanup()
+        raise
+    _cleanup()
+
+# Driver code for the package
 def run():
+    global client
 
-    # The name of the image file to annotate
-    path = os.path.join(sys.path[0], 'DSC_6315.jpg')
-    print(path)
+    # Clients
+    client = vision.ImageAnnotatorClient()
 
-    # Loads the image into memory
-    with io.open(path, 'rb') as image_file:
-        content = image_file.read()
+    # Find files we want to process based on if they have a corresponding .XMP
+    files = os.listdir(input_path)
+    select = [file for file in files if os.path.splitext(file)[1] == '.xmp']
 
-    image = types.Image(content=content)
+    # Create the 'temp' directory
+    print(f'Initializing file processing for {len(select)} files...')
+    os.makedirs(temp_path)
+    
+    try:
+        # Process files
+        for index, file in progressbar.progressbar(list(enumerate(select)), redirect_stdout=True, term_width=110):
+            # Get all possible files
+            possibles = [possible for possible in files if
+            possible.startswith(os.path.splitext(file)[0])
+            and not possible.endswith(os.path.splitext(file)[1])]
+            
+            # Skip and warn if more than 1 possible files, user error
+            if len(possibles) > 1:
+                print('More than 1 possible binding file for \'{}\'...'.format(file))
+                print('\n'.join(['>>> {}'.format(possible) for possible in possibles]))
+            # Zero possible files, user error, likely
+            elif len(possibles) <= 0:
+                print('Zero possible files for \'{}\'. skipping...'.format(file))
+            # Process individual file
+            else:
+                print('Processing file {}, \'{}\''.format(index + 1, possibles[0]), end=' | ')
+                process_file(possibles[0])
+                time.sleep(0.3)
+    except:
+        os.rmdir(temp_path)
+        raise
 
-    # Performs label detection on the image file
-    response = client.label_detection(image=image)
-    labels = response.label_annotations
-
-    print([label.description for label in labels])
+    # Remove the directory, we are done here
+    print('Cleaning up temporary directory...')
+    os.rmdir(temp_path)