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changelog.txt

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+## 1.0.0
+
+- Program created and distributed

person_autocrop.py

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+import os
+import cv2
+import numpy as np
+import tensorflow as tf
+import gdown
+
+# Download model if not exist
+def download_model():
+    url = f"https://drive.google.com/uc?id=1Ml260620LIKa-OrWqdzv_z99NJKixt3W"
+    gdown.download(url, "ssd_mobilenetv2_coco/saved_model.pb", quiet=False)
+
+# Explicitly specify the GPU device
+physical_devices = tf.config.list_physical_devices('GPU')
+if len(physical_devices) > 0:
+    tf.config.experimental.set_memory_growth(physical_devices[0], True)
+
+# Initialize model variable
+model = None
+
+def create_folder(output_directory):
+    # Check if the folder exists
+    if not os.path.exists(output_directory):
+        try:
+            # Create the folder if it doesn't exist
+            os.makedirs(output_directory)
+            print(f"LOG: Folder '{output_directory}' created successfully.")
+        except OSError as e:
+            print(f"LOG: Error creating folder '{output_directory}': {e}")
+
+# Function to perform object detection and crop the image
+def object_detection(input_image_path, output_directory):
+
+    # Read the input image
+    image = cv2.imread(input_image_path)
+    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    image_expanded = np.expand_dims(image_rgb, axis=0).astype(np.uint8)
+
+    # Perform inference
+    detections = model.signatures["serving_default"](tf.constant(image_expanded))
+
+    # Extract coordinates of detected persons
+    detection_classes = detections['detection_classes'][0].numpy()
+    detection_boxes = detections['detection_boxes'][0]
+    person_indices = tf.where(tf.equal(detection_classes, 1))[:, 0]
+    person_coords = tf.gather(detection_boxes, person_indices).numpy()
+
+    # Crop the original image based on the detected persons' coordinates
+    for i, coords in enumerate(person_coords):
+        ymin, xmin, ymax, xmax = coords
+
+        # Ensure valid coordinates
+        if 0 <= ymin < ymax <= image.shape[0] and 0 <= xmin < xmax <= image.shape[1]:
+            ymin, xmin, ymax, xmax = (
+                int(ymin * image.shape[0]),
+                int(xmin * image.shape[1]),
+                int(ymax * image.shape[0]),
+                int(xmax * image.shape[1]),
+            )
+            cropped_person = image[ymin:ymax, xmin:xmax, :]
+
+            # Check if the cropped_person array is not empty
+            if not cropped_person.size == 0:
+                # Save the cropped person image with correct color space conversion
+                try:
+                    image_name = os.path.basename(input_image_path)
+                    # Save the image using cv2.imwrite()
+                    cv2.imwrite(f"{output_directory}/{i + 1}_{image_name}", cropped_person)
+                    print(f"LOG: Image saved successfully to '{output_directory}/{i + 1}_{image_name}'.")
+                except Exception as e:
+                    print(f"LOG: Error saving image: {e}")
+            else:
+                print(f"LOG: Warning: Cropped person {i + 1} is empty.")
+
+if __name__ == "__main__":
+    import sys
+
+    if len(sys.argv) != 3:
+        print("Usage: python person_crop.py <input_image_folder> <output_directory>")
+        sys.exit(1)
+
+    input_image_folder = sys.argv[1]
+    output_directory = sys.argv[2]
+
+    if os.path.exists("ssd_mobilenetv2_coco/saved_model.pb"):
+        print("LOG: SSD Mobilenet V2 COCO model found.")
+    else:
+        print("LOG: SSD Mobilenet V2 COCO model not found. Downloading...")
+        download_model()
+        print("LOG: SSD Mobilenet V2 COCO model downloaded.")
+
+    model = tf.saved_model.load("ssd_mobilenetv2_coco")
+    create_folder(output_directory)
+
+    print("LOG: Running...")
+    # Process all images in the input folder
+    for filename in os.listdir(input_image_folder):
+        if filename.endswith(".jpg") or filename.endswith(".png") or filename.endswith(".jpeg") or filename.endswith(".webp") or filename.endswith(".bmp"):
+            input_image_path = os.path.join(input_image_folder, filename)
+            object_detection(input_image_path, output_directory)
+    print("LOG: Done.")

requirements.txt

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+opencv-python
+numpy
+tensorflow
+gdown

run.bat

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+@echo off
+
+set VENV_FOLDER=venv
+set REQUIREMENTS_FILE=requirements.txt
+
+rem Check if virtual environment folder exists
+if not exist %VENV_FOLDER% (
+    echo LOG: Virtual environment not found. Creating...
+    python -m venv %VENV_FOLDER%
+	echo LOG: Virtual environment created.
+)
+
+rem Activate the virtual environment
+call %VENV_FOLDER%\Scripts\activate
+
+echo LOG: Virtual environment is activated.
+echo LOG: Checking if pip is in VENV.
+rem Check if pip is installed in the virtual environment
+pip --version >nul 2>&1
+if %errorlevel% neq 0 (
+    echo LOG: Installing pip in the virtual environment...
+    python -m ensurepip
+	echo LOG: pip successfully installed.
+)
+
+rem Check if requirements are already installed
+set "REQUIREMENTS_MET=true"
+for /f "delims=" %%i in (%REQUIREMENTS_FILE%) do (
+    set "PACKAGE_FOUND=false"
+)
+
+for /d %%p in (%VENV_FOLDER%\Lib\site-packages\*) do (
+    for /f %%i in ('type %REQUIREMENTS_FILE% ^| findstr /i /c:"%%~nxp"') do (
+        set "PACKAGE_FOUND=true"
+        goto :PACKAGE_FOUND
+    )
+)
+:PACKAGE_FOUND
+
+if "%PACKAGE_FOUND%"=="false" (
+    set "REQUIREMENTS_MET=false"
+    echo LOG: Some requirements are missing.
+)
+
+if "%REQUIREMENTS_MET%"=="false" (
+    echo LOG: Installing requirements...
+    pip install -r %REQUIREMENTS_FILE%
+) else (
+    echo LOG: Requirements are already installed.
+)
+
+set "modelfolder=ssd_mobilenetv2_coco"
+
+rem Check if the folder exists
+if not exist "%modelfolder%" (
+    echo LOG: Model folder does not exist. Creating...
+    mkdir "%modelfolder%"
+    if errorlevel 1 (
+        echo LOG: Failed to create model folder. Aborting.
+        exit /b 1
+    ) else (
+        echo LOG: Model folder created successfully.
+    )
+)
+
+echo.
+set /p INPUT_FOLDER="-> Enter input image folder path: "
+set /p OUTPUT_FOLDER="-> Enter output image folder path: "
+
+echo.
+echo LOG: Running the script
+rem Run the Python script
+python person_autocrop.py %INPUT_FOLDER% %OUTPUT_FOLDER%

run.sh

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+#!/bin/bash
+
+VENV_FOLDER="venv"
+REQUIREMENTS_FILE="requirements.txt"
+
+# Check if virtual environment folder exists
+if [ ! -d "$VENV_FOLDER" ]; then
+    echo "LOG: Virtual environment not found. Creating..."
+    python3 -m venv "$VENV_FOLDER"
+    echo "LOG: Virtual environment created."
+fi
+
+# Activate the virtual environment
+source "$VENV_FOLDER/bin/activate"
+
+echo "LOG: Virtual environment is activated."
+echo "LOG: Checking if pip is in VENV."
+
+# Check if pip is installed in the virtual environment
+pip --version > /dev/null 2>&1
+if [ $? -ne 0 ]; then
+    echo "LOG: Installing pip in the virtual environment..."
+    python -m ensurepip
+    echo "LOG: pip successfully installed."
+fi
+
+# Check if requirements are already installed
+REQUIREMENTS_MET=true
+while IFS= read -r req; do
+    PACKAGE_FOUND=false
+done < "$REQUIREMENTS_FILE"
+
+for package in "$VENV_FOLDER/lib/python*/site-packages/"*; do
+    while IFS= read -r req; do
+        if [[ "$package" == *"$req"* ]]; then
+            PACKAGE_FOUND=true
+            break
+        fi
+    done < "$REQUIREMENTS_FILE"
+done
+
+if [ "$PACKAGE_FOUND" == "false" ]; then
+    REQUIREMENTS_MET=false
+    echo "LOG: Some requirements are missing."
+fi
+
+if [ "$REQUIREMENTS_MET" == "false" ]; then
+    echo "LOG: Installing requirements..."
+    pip install -r "$REQUIREMENTS_FILE"
+else
+    echo "LOG: Requirements are already installed."
+fi
+
+MODELFOLDER="ssd_mobilenetv2_coco"
+
+# Check if the folder exists
+if [ ! -d "$MODELFOLDER" ]; then
+    echo "LOG: Model folder does not exist. Creating..."
+    mkdir "$MODELFOLDER"
+    if [ $? -ne 0 ]; then
+        echo "LOG: Failed to create model folder. Aborting."
+        exit 1
+    else
+        echo "LOG: Model folder created successfully."
+    fi
+fi
+
+echo ""
+read -p "-> Enter input image folder path: " INPUT_FOLDER
+read -p "-> Enter output image folder path: " OUTPUT_FOLDER
+
+echo ""
+echo "LOG: Running the script"
+# Run the Python script
+python person_autocrop.py "$INPUT_FOLDER" "$OUTPUT_FOLDER"