update,

vinniesniper-54816/task1/_lab/014-color_layout_descriptor/color_layout_descriptor.py

@@ -0,0 +1,57 @@
+import cv2
+import numpy as np
+
+
+import os
+
+
+def color_layout_descriptor(file_path, image, num_blocks=8, resize_to_px=256):
+    """
+    Compute the Color Layout Descriptor (CLD) of an image.
+
+    Parameters:
+    image (numpy.ndarray): Input image
+    num_blocks (int, optional): Number of blocks to divide the image into. Defaults to 8.
+
+    Returns:
+    numpy.ndarray: CLD feature vector
+    """
+    # Step 3: Resize image to a fixed size (we'll use 256x256 for simplicity)
+    resized_image = cv2.resize(image, (resize_to_px, resize_to_px))
+    # Save the resized image to a file
+    cv2.imwrite(
+        os.path.join(
+            os.path.join(
+                os.path.dirname(file_path),
+                os.path.splitext(os.path.basename(file_path))[0] + "_resized.jpg",
+            )
+        ),
+        resized_image,
+    )
+
+    # Step 4: Convert image to YCrCb color space
+    ycrcb_image = cv2.cvtColor(resized_image, cv2.COLOR_BGR2YCrCb)
+
+    # Step 5: Divide the image into sub-blocks
+    block_size = int(256 / num_blocks)
+    blocks = []
+    for i in range(num_blocks):
+        for j in range(num_blocks):
+            block = ycrcb_image[
+                i * block_size : (i + 1) * block_size,
+                j * block_size : (j + 1) * block_size,
+            ]
+            blocks.append(block)
+
+    # Step 6: Extract features from each sub-block
+    features = []
+    for block in blocks:
+        # Compute the mean and standard deviation of each color channel
+        mean_y, mean_cr, mean_cb = np.mean(block, axis=(0, 1))
+        std_y, std_cr, std_cb = np.std(block, axis=(0, 1))
+        features.extend([mean_y, mean_cr, mean_cb, std_y, std_cr, std_cb])
+
+    # Step 7: Concatenate features into a single vector
+    cld_vector = np.array(features)
+
+    return cld_vector

vinniesniper-54816/task1/_lab/014-color_layout_descriptor/main.py

@@ -0,0 +1,78 @@
+"""
+Task:
+implement a `Color_layout_descriptor` using python and openCV
+plesae provide a example of usage as well, compare two images `image1.png` and `image2.png` using `color_layout_descriptor`
+
+Scenario:
+- imagine you are writing a python program to a junior developer
+
+Instructions:
+- please solve it step by step
+- Divide the whole image into sub-blocks.
+- Extract features from each sub-block.
+- name of function "color_layout_hellowold"
+- please make sure your code is clean and readable
+- please write detalied comment
+- please make your code testable
+- please review your code before return to the developer
+- with `Zigzag scanning`
+
+Reference:
+https://en.wikipedia.org/wiki/Color_layout_descriptor
+"""
+
+import cv2
+import numpy as np
+import sys
+from pprint import pprint
+
+from task1.src.tutorial1.color_layout_descriptor.color_layout_descriptor import color_layout_descriptor
+
+
+def color_layout_hello_world(image1_path, image2_path, block=8, resize_to_px=256):
+    """
+    Compare two images using the Color Layout Descriptor.
+
+    Parameters:
+    image1_path (str): Path to the first image
+    image2_path (str): Path to the second image
+
+    Returns:
+    float: Similarity score between the two images
+    """
+    image1 = cv2.imread(image1_path)
+    image2 = cv2.imread(image2_path)
+
+    cld1 = color_layout_descriptor(image1_path, image1, block, resize_to_px)
+    cld2 = color_layout_descriptor(image2_path, image2, block, resize_to_px)
+
+    # Compute the Euclidean distance between the two CLD vectors
+    distance = np.linalg.norm(cld1 - cld2)
+
+    # Convert the distance to a similarity score (lower values indicate higher similarity)
+    similarity = 1 / (1 + distance)
+
+    return similarity
+
+
+beach_1_jpg = "beach/image1.jpg"
+beach_2_jpg = "beach/beach_2.jpg"
+beach_138_jpg = "beach/138.jpg"
+bus_jpg = "bus/357.jpg"
+
+for test_block in [8]:
+    for r_px in [256]:
+        print("-" * 80)
+        print("using b:", test_block, "r:", r_px)
+
+        similarity = color_layout_hello_world(beach_1_jpg, beach_1_jpg, test_block, r_px)
+        print(f"diff beach_1 loopback        (expected same = 1) : {similarity:.4f} ({abs(1-similarity):.4f})")
+
+        similarity = color_layout_hello_world(beach_1_jpg, beach_2_jpg, test_block, r_px)
+        print(f"diff beach_1 beach_2      (expected similar ~ 1) : {similarity:.4f} ({abs(1-similarity):.4f})")
+
+        similarity = color_layout_hello_world(beach_1_jpg, beach_138_jpg, test_block, r_px)
+        print(f"diff beach_1 beach_138_jpg   (expected same ~ 1) : {similarity:.4f} ({abs(1-similarity):.4f})")
+
+        similarity = color_layout_hello_world(beach_1_jpg, bus_jpg, test_block, r_px)
+        print(f"diff beach_1 bus_jpg   (expected different  ~ 0) : {similarity:.4f} ({abs(0-similarity):.4f})")

vinniesniper-54816/task1/_lab/014-color_layout_descriptor/main_draft1.py

@@ -0,0 +1,127 @@
+"""
+Task:
+implement a `Color_layout_descriptor` using python and openCV
+plesae provide a example of usage as well, compare two images `image1.png` and `image2.png` using `color_layout_descriptor`
+
+Scenario:
+- imagine you are writing a python program to a junior developer
+
+Instructions:
+- please solve it step by step
+- Divide the whole image into sub-blocks.
+- Extract features from each sub-block.
+- name of function "color_layout_hellowold"
+- please make sure your code is clean and readable
+- please write detalied comment
+- please make your code testable
+- please review your code before return to the developer
+- with `Zigzag scanning`
+
+Reference:
+https://en.wikipedia.org/wiki/Color_layout_descriptor
+"""
+
+import cv2
+import numpy as np
+import os, sys
+from pprint import pprint
+
+
+def color_layout_descriptor(file_path, image, num_blocks=8):
+    """
+    Compute the Color Layout Descriptor (CLD) of an image.
+
+    Parameters:
+    image (numpy.ndarray): Input image
+    num_blocks (int, optional): Number of blocks to divide the image into. Defaults to 8.
+
+    Returns:
+    numpy.ndarray: CLD feature vector
+    """
+    # Step 3: Resize image to a fixed size (we'll use 256x256 for simplicity)
+    resized_image = cv2.resize(image, (256, 256))
+    # Save the resized image to a file
+    cv2.imwrite(
+        os.path.join(
+            os.path.join(
+                os.path.dirname(file_path),
+                os.path.splitext(os.path.basename(file_path))[0] + "_resized.jpg",
+            )
+        ),
+        resized_image,
+    )
+
+    # Step 4: Convert image to YCrCb color space
+    ycrcb_image = cv2.cvtColor(resized_image, cv2.COLOR_BGR2YCrCb)
+
+    # Step 5: Divide the image into sub-blocks
+    block_size = int(256 / num_blocks)
+    blocks = []
+    for i in range(num_blocks):
+        for j in range(num_blocks):
+            block = ycrcb_image[
+                i * block_size : (i + 1) * block_size,
+                j * block_size : (j + 1) * block_size,
+            ]
+            blocks.append(block)
+
+    # Step 6: Extract features from each sub-block
+    features = []
+    for block in blocks:
+        # Compute the mean and standard deviation of each color channel
+        mean_y, mean_cr, mean_cb = np.mean(block, axis=(0, 1))
+        std_y, std_cr, std_cb = np.std(block, axis=(0, 1))
+        features.extend([mean_y, mean_cr, mean_cb, std_y, std_cr, std_cb])
+
+    # Step 7: Concatenate features into a single vector
+    cld_vector = np.array(features)
+
+    return cld_vector
+
+
+def color_layout_hello_world(image1_path, image2_path, block=8):
+    """
+    Compare two images using the Color Layout Descriptor.
+
+    Parameters:
+    image1_path (str): Path to the first image
+    image2_path (str): Path to the second image
+
+    Returns:
+    float: Similarity score between the two images
+    """
+    image1 = cv2.imread(image1_path)
+    image2 = cv2.imread(image2_path)
+
+    cld1 = color_layout_descriptor(image1_path, image1, block)
+    cld2 = color_layout_descriptor(image2_path, image2, block)
+
+    # Compute the Euclidean distance between the two CLD vectors
+    distance = np.linalg.norm(cld1 - cld2)
+
+    # Convert the distance to a similarity score (lower values indicate higher similarity)
+    similarity = 1 / (1 + distance)
+
+    return similarity
+
+
+beach_1_jpg = "image1.jpg"
+beach_2_jpg = "beach_2.jpg"
+bus_jpg = "357.jpg"
+beach_138_jpg = "138.jpg"
+
+for test_block in range(1, 16 + 1, 1):
+    print("-" * 80)
+    print("using test_block: ", test_block)
+
+    similarity = color_layout_hello_world(beach_1_jpg, beach_1_jpg, test_block)
+    print(f"diff beach_1 loopback        (expected same = 1) : {similarity:.4f} ({abs(1-similarity):.4f})")
+
+    similarity = color_layout_hello_world(beach_1_jpg, beach_2_jpg, test_block)
+    print(f"diff beach_1 beach_2      (expected similar ~ 1) : {similarity:.4f} ({abs(1-similarity):.4f})")
+
+    similarity = color_layout_hello_world(beach_1_jpg, beach_138_jpg, test_block)
+    print(f"diff beach_1 beach_138_jpg   (expected same ~ 1) : {similarity:.4f} ({abs(1-similarity):.4f})")
+
+    similarity = color_layout_hello_world(beach_1_jpg, bus_jpg, test_block)
+    print(f"diff beach_1 bus_jpg   (expected different  ~ 0) : {similarity:.4f} ({abs(0-similarity):.4f})")

vinniesniper-54816/task1/_lab/014-color_layout_descriptor/main_draft2.py

@@ -0,0 +1,91 @@
+"""
+Task:
+implement a `Color_layout_descriptor` using python and openCV
+plesae provide a example of usage as well, compare two images `image1.png` and `image2.png` using `color_layout_descriptor`
+
+Scenario:
+- imagine you are writing a python program to a junior developer
+
+Instructions:
+- please solve it step by step
+- Divide the whole image into sub-blocks.
+- Extract features from each sub-block.
+- name of function "color_layout_hellowold"
+- please make sure your code is clean and readable
+- please write detalied comment
+- please make your code testable
+- please review your code before return to the developer
+- with `Zigzag scanning`
+
+Reference:
+https://en.wikipedia.org/wiki/Color_layout_descriptor
+"""
+
+import cv2
+import numpy as np
+import os, sys
+from pprint import pprint
+
+
+def color_layout_descriptor(image, num_blocks=8, resize_to_px=256):
+    resized_image = cv2.resize(image, (resize_to_px, resize_to_px))
+
+    # Step 4: Convert image to YCrCb color space
+    ycrcb_image = cv2.cvtColor(resized_image, cv2.COLOR_BGR2YCrCb)
+
+    # Step 5: Divide the image into sub-blocks
+    block_size = int(256 / num_blocks)
+    blocks = []
+    for i in range(num_blocks):
+        for j in range(num_blocks):
+            block = ycrcb_image[
+                i * block_size : (i + 1) * block_size,
+                j * block_size : (j + 1) * block_size,
+            ]
+            blocks.append(block)
+
+    # Step 6: Extract features from each sub-block
+    features = []
+    for block in blocks:
+        # Compute the mean and standard deviation of each color channel
+        mean_y, mean_cr, mean_cb = np.mean(block, axis=(0, 1))
+        std_y, std_cr, std_cb = np.std(block, axis=(0, 1))
+        features.extend([mean_y, mean_cr, mean_cb, std_y, std_cr, std_cb])
+
+    # Step 7: Concatenate features into a single vector
+    cld_vector = np.array(features)
+
+    return cld_vector
+
+
+def color_layout_hello_world(image1_path, image2_path, block=8, resize_to_px=256):
+    """
+    Compare two images using the Color Layout Descriptor.
+
+    Parameters:
+    image1_path (str): Path to the first image
+    image2_path (str): Path to the second image
+
+    Returns:
+    float: Similarity score between the two images
+    """
+    image1 = cv2.imread(image1_path)
+    image2 = cv2.imread(image2_path)
+
+    cld1 = color_layout_descriptor(image1, block, resize_to_px)
+    cld2 = color_layout_descriptor(image2, block, resize_to_px)
+
+    # Compute the Euclidean distance between the two CLD vectors
+    distance = np.linalg.norm(cld1 - cld2)
+
+    # Convert the distance to a similarity score (lower values indicate higher similarity)
+    similarity = 1 / (1 + distance)
+
+    return similarity
+
+
+input_jpg = "building.jpg"
+building_jpg = "building/256.jpg"
+
+similarity = color_layout_hello_world(input_jpg, building_jpg, 8, 256)
+print(f"diff: {similarity:.4f} ({abs(1-similarity):.4f})")

vinniesniper-54816/task1/_lab/014-color_layout_descriptor/result_1.txt

@@ -0,0 +1,216 @@
+--------------------------------------------------------------------------------
+using b: 1 r: 16
+diff beach_1 loopback        (expected same = 1) : 1.0000 (0.0000)
+diff beach_1 beach_2      (expected similar ~ 1) : 0.0179 (0.9821)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : 0.0274 (0.9726)
+diff beach_1 bus_jpg   (expected different  ~ 0) : 0.0180 (0.0180)
+--------------------------------------------------------------------------------
+using b: 1 r: 32
+diff beach_1 loopback        (expected same = 1) : 1.0000 (0.0000)
+diff beach_1 beach_2      (expected similar ~ 1) : 0.0177 (0.9823)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : 0.0293 (0.9707)
+diff beach_1 bus_jpg   (expected different  ~ 0) : 0.0193 (0.0193)
+--------------------------------------------------------------------------------
+using b: 1 r: 64
+diff beach_1 loopback        (expected same = 1) : 1.0000 (0.0000)
+diff beach_1 beach_2      (expected similar ~ 1) : 0.0180 (0.9820)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : 0.0290 (0.9710)
+diff beach_1 bus_jpg   (expected different  ~ 0) : 0.0191 (0.0191)
+--------------------------------------------------------------------------------
+using b: 1 r: 128
+diff beach_1 loopback        (expected same = 1) : 1.0000 (0.0000)
+diff beach_1 beach_2      (expected similar ~ 1) : 0.0181 (0.9819)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : 0.0295 (0.9705)
+diff beach_1 bus_jpg   (expected different  ~ 0) : 0.0192 (0.0192)
+--------------------------------------------------------------------------------
+using b: 1 r: 256
+diff beach_1 loopback        (expected same = 1) : 1.0000 (0.0000)
+diff beach_1 beach_2      (expected similar ~ 1) : 0.0180 (0.9820)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : 0.0297 (0.9703)
+diff beach_1 bus_jpg   (expected different  ~ 0) : 0.0191 (0.0191)
+--------------------------------------------------------------------------------
+using b: 1 r: 512
+diff beach_1 loopback        (expected same = 1) : 1.0000 (0.0000)
+diff beach_1 beach_2      (expected similar ~ 1) : 0.0114 (0.9886)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : 0.0299 (0.9701)
+diff beach_1 bus_jpg   (expected different  ~ 0) : 0.0140 (0.0140)
+--------------------------------------------------------------------------------
+using b: 4 r: 16
+diff beach_1 loopback        (expected same = 1) : nan (nan)
+diff beach_1 beach_2      (expected similar ~ 1) : nan (nan)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : nan (nan)
+diff beach_1 bus_jpg   (expected different  ~ 0) : nan (nan)
+--------------------------------------------------------------------------------
+using b: 4 r: 32
+diff beach_1 loopback        (expected same = 1) : nan (nan)
+diff beach_1 beach_2      (expected similar ~ 1) : nan (nan)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : nan (nan)
+diff beach_1 bus_jpg   (expected different  ~ 0) : nan (nan)
+--------------------------------------------------------------------------------
+using b: 4 r: 64
+diff beach_1 loopback        (expected same = 1) : nan (nan)
+diff beach_1 beach_2      (expected similar ~ 1) : nan (nan)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : nan (nan)
+diff beach_1 bus_jpg   (expected different  ~ 0) : nan (nan)
+--------------------------------------------------------------------------------
+using b: 4 r: 128
+diff beach_1 loopback        (expected same = 1) : nan (nan)
+diff beach_1 beach_2      (expected similar ~ 1) : nan (nan)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : nan (nan)
+diff beach_1 bus_jpg   (expected different  ~ 0) : nan (nan)
+--------------------------------------------------------------------------------
+using b: 4 r: 256
+diff beach_1 loopback        (expected same = 1) : 1.0000 (0.0000)
+diff beach_1 beach_2      (expected similar ~ 1) : 0.0031 (0.9969)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : 0.0044 (0.9956)
+diff beach_1 bus_jpg   (expected different  ~ 0) : 0.0029 (0.0029)
+--------------------------------------------------------------------------------
+using b: 4 r: 512
+diff beach_1 loopback        (expected same = 1) : 1.0000 (0.0000)
+diff beach_1 beach_2      (expected similar ~ 1) : 0.0026 (0.9974)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : 0.0061 (0.9939)
+diff beach_1 bus_jpg   (expected different  ~ 0) : 0.0027 (0.0027)
+--------------------------------------------------------------------------------
+using b: 9 r: 16
+diff beach_1 loopback        (expected same = 1) : nan (nan)
+diff beach_1 beach_2      (expected similar ~ 1) : nan (nan)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : nan (nan)
+diff beach_1 bus_jpg   (expected different  ~ 0) : nan (nan)
+--------------------------------------------------------------------------------
+using b: 9 r: 32
+diff beach_1 loopback        (expected same = 1) : nan (nan)
+diff beach_1 beach_2      (expected similar ~ 1) : nan (nan)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : nan (nan)
+diff beach_1 bus_jpg   (expected different  ~ 0) : nan (nan)
+--------------------------------------------------------------------------------
+using b: 9 r: 64
+diff beach_1 loopback        (expected same = 1) : nan (nan)
+diff beach_1 beach_2      (expected similar ~ 1) : nan (nan)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : nan (nan)
+diff beach_1 bus_jpg   (expected different  ~ 0) : nan (nan)
+--------------------------------------------------------------------------------
+using b: 9 r: 128
+diff beach_1 loopback        (expected same = 1) : nan (nan)
+diff beach_1 beach_2      (expected similar ~ 1) : nan (nan)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : nan (nan)
+diff beach_1 bus_jpg   (expected different  ~ 0) : nan (nan)
+--------------------------------------------------------------------------------
+using b: 9 r: 256
+diff beach_1 loopback        (expected same = 1) : 1.0000 (0.0000)
+diff beach_1 beach_2      (expected similar ~ 1) : 0.0013 (0.9987)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : 0.0017 (0.9983)
+diff beach_1 bus_jpg   (expected different  ~ 0) : 0.0012 (0.0012)
+--------------------------------------------------------------------------------
+using b: 9 r: 512
+diff beach_1 loopback        (expected same = 1) : 1.0000 (0.0000)
+diff beach_1 beach_2      (expected similar ~ 1) : 0.0011 (0.9989)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : 0.0026 (0.9974)
+diff beach_1 bus_jpg   (expected different  ~ 0) : 0.0012 (0.0012)
+--------------------------------------------------------------------------------
+using b: 16 r: 16
+diff beach_1 loopback        (expected same = 1) : nan (nan)
+diff beach_1 beach_2      (expected similar ~ 1) : nan (nan)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : nan (nan)
+diff beach_1 bus_jpg   (expected different  ~ 0) : nan (nan)
+--------------------------------------------------------------------------------
+using b: 16 r: 32
+diff beach_1 loopback        (expected same = 1) : nan (nan)
+diff beach_1 beach_2      (expected similar ~ 1) : nan (nan)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : nan (nan)
+diff beach_1 bus_jpg   (expected different  ~ 0) : nan (nan)
+--------------------------------------------------------------------------------
+using b: 16 r: 64
+diff beach_1 loopback        (expected same = 1) : nan (nan)
+diff beach_1 beach_2      (expected similar ~ 1) : nan (nan)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : nan (nan)
+diff beach_1 bus_jpg   (expected different  ~ 0) : nan (nan)
+--------------------------------------------------------------------------------
+using b: 16 r: 128
+diff beach_1 loopback        (expected same = 1) : nan (nan)
+diff beach_1 beach_2      (expected similar ~ 1) : nan (nan)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : nan (nan)
+diff beach_1 bus_jpg   (expected different  ~ 0) : nan (nan)
+--------------------------------------------------------------------------------
+using b: 16 r: 256
+diff beach_1 loopback        (expected same = 1) : 1.0000 (0.0000)
+diff beach_1 beach_2      (expected similar ~ 1) : 0.0007 (0.9993)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : 0.0009 (0.9991)
+diff beach_1 bus_jpg   (expected different  ~ 0) : 0.0006 (0.0006)
+--------------------------------------------------------------------------------
+using b: 16 r: 512
+diff beach_1 loopback        (expected same = 1) : 1.0000 (0.0000)
+diff beach_1 beach_2      (expected similar ~ 1) : 0.0006 (0.9994)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : 0.0014 (0.9986)
+diff beach_1 bus_jpg   (expected different  ~ 0) : 0.0007 (0.0007)
+--------------------------------------------------------------------------------
+using b: 25 r: 16
+diff beach_1 loopback        (expected same = 1) : nan (nan)
+diff beach_1 beach_2      (expected similar ~ 1) : nan (nan)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : nan (nan)
+diff beach_1 bus_jpg   (expected different  ~ 0) : nan (nan)
+--------------------------------------------------------------------------------
+using b: 25 r: 32
+diff beach_1 loopback        (expected same = 1) : nan (nan)
+diff beach_1 beach_2      (expected similar ~ 1) : nan (nan)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : nan (nan)
+diff beach_1 bus_jpg   (expected different  ~ 0) : nan (nan)
+--------------------------------------------------------------------------------
+using b: 25 r: 64
+diff beach_1 loopback        (expected same = 1) : nan (nan)
+diff beach_1 beach_2      (expected similar ~ 1) : nan (nan)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : nan (nan)
+diff beach_1 bus_jpg   (expected different  ~ 0) : nan (nan)
+--------------------------------------------------------------------------------
+using b: 25 r: 128
+diff beach_1 loopback        (expected same = 1) : nan (nan)
+diff beach_1 beach_2      (expected similar ~ 1) : nan (nan)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : nan (nan)
+diff beach_1 bus_jpg   (expected different  ~ 0) : nan (nan)
+--------------------------------------------------------------------------------
+using b: 25 r: 256
+diff beach_1 loopback        (expected same = 1) : 1.0000 (0.0000)
+diff beach_1 beach_2      (expected similar ~ 1) : 0.0004 (0.9996)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : 0.0006 (0.9994)
+diff beach_1 bus_jpg   (expected different  ~ 0) : 0.0004 (0.0004)
+--------------------------------------------------------------------------------
+using b: 25 r: 512
+diff beach_1 loopback        (expected same = 1) : 1.0000 (0.0000)
+diff beach_1 beach_2      (expected similar ~ 1) : 0.0004 (0.9996)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : 0.0009 (0.9991)
+diff beach_1 bus_jpg   (expected different  ~ 0) : 0.0004 (0.0004)
+--------------------------------------------------------------------------------
+using b: 36 r: 16
+diff beach_1 loopback        (expected same = 1) : nan (nan)
+diff beach_1 beach_2      (expected similar ~ 1) : nan (nan)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : nan (nan)
+diff beach_1 bus_jpg   (expected different  ~ 0) : nan (nan)
+--------------------------------------------------------------------------------
+using b: 36 r: 32
+diff beach_1 loopback        (expected same = 1) : nan (nan)
+diff beach_1 beach_2      (expected similar ~ 1) : nan (nan)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : nan (nan)
+diff beach_1 bus_jpg   (expected different  ~ 0) : nan (nan)
+--------------------------------------------------------------------------------
+using b: 36 r: 64
+diff beach_1 loopback        (expected same = 1) : nan (nan)
+diff beach_1 beach_2      (expected similar ~ 1) : nan (nan)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : nan (nan)
+diff beach_1 bus_jpg   (expected different  ~ 0) : nan (nan)
+--------------------------------------------------------------------------------
+using b: 36 r: 128
+diff beach_1 loopback        (expected same = 1) : nan (nan)
+diff beach_1 beach_2      (expected similar ~ 1) : nan (nan)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : nan (nan)
+diff beach_1 bus_jpg   (expected different  ~ 0) : nan (nan)
+--------------------------------------------------------------------------------
+using b: 36 r: 256
+diff beach_1 loopback        (expected same = 1) : 1.0000 (0.0000)
+diff beach_1 beach_2      (expected similar ~ 1) : 0.0003 (0.9997)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : 0.0004 (0.9996)
+diff beach_1 bus_jpg   (expected different  ~ 0) : 0.0003 (0.0003)
+--------------------------------------------------------------------------------
+using b: 36 r: 512
+diff beach_1 loopback        (expected same = 1) : 1.0000 (0.0000)
+diff beach_1 beach_2      (expected similar ~ 1) : 0.0003 (0.9997)
+diff beach_1 beach_138_jpg   (expected same ~ 1) : 0.0006 (0.9994)
+diff beach_1 bus_jpg   (expected different  ~ 0) : 0.0003 (0.0003)

vinniesniper-54816/task1/_lab/015-helloworld/setup.sh

@@ -0,0 +1,5 @@
+#!/usr/bin/env bash
+
+set -ex
+
+pip install opencv-python

vinniesniper-54816/task1/_lab/015-helloworld/test.py

@@ -0,0 +1,18 @@
+#!/usr/bin/env python
+
+import os,sys
+import cv2 as cv
+
+c_img = cv.imread("beach.jpg")
+bw_img = cv.imread("beach.jpg", cv.IMREAD_GRAYSCALE)
+
+print(c_img[100,100,0])
+
+cv.imwrite("beach_done.jpg", bw_img);
+# cv.imshow('windowname', img);
+r_img=cv.resize(bw_img, (320, 240))
+cv.imwrite("beach_resize.jpg", r_img);
+
+
+
+print("done")