You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


/*M///////////////////////////////////////////////////////////////////////////////////////
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#ifndef __OPENCV_STEREO_HPP__
#define __OPENCV_STEREO_HPP__

#include "opencv2/core.hpp"
#include "opencv2/stereo/descriptor.hpp"
#include <opencv2/stereo/quasi_dense_stereo.hpp>

/**
@defgroup stereo Stereo Correspondance Algorithms

*/

namespace cv
{
    namespace stereo
    {
        //! @addtogroup stereo
        //! @{
        /** @brief Filters off small noise blobs (speckles) in the disparity map
        @param img The input 16-bit signed disparity image
        @param newVal The disparity value used to paint-off the speckles
        @param maxSpeckleSize The maximum speckle size to consider it a speckle. Larger blobs are not
        affected by the algorithm
        @param maxDiff Maximum difference between neighbor disparity pixels to put them into the same
        blob. Note that since StereoBM, StereoSGBM and may be other algorithms return a fixed-point
        disparity map, where disparity values are multiplied by 16, this scale factor should be taken into
        account when specifying this parameter value.
        @param buf The optional temporary buffer to avoid memory allocation within the function.
        */
        /** @brief The base class for stereo correspondence algorithms.
        */
        class StereoMatcher : public Algorithm
        {
        public:
            enum { DISP_SHIFT = 4,
                DISP_SCALE = (1 << DISP_SHIFT)
            };

            /** @brief Computes disparity map for the specified stereo pair

            @param left Left 8-bit single-channel image.
            @param right Right image of the same size and the same type as the left one.
            @param disparity Output disparity map. It has the same size as the input images. Some algorithms,
            like StereoBM or StereoSGBM compute 16-bit fixed-point disparity map (where each disparity value
            has 4 fractional bits), whereas other algorithms output 32-bit floating-point disparity map.
            */
            virtual void compute( InputArray left, InputArray right,
                OutputArray disparity ) = 0;

            virtual int getMinDisparity() const = 0;
            virtual void setMinDisparity(int minDisparity) = 0;

            virtual int getNumDisparities() const = 0;
            virtual void setNumDisparities(int numDisparities) = 0;

            virtual int getBlockSize() const = 0;
            virtual void setBlockSize(int blockSize) = 0;

            virtual int getSpeckleWindowSize() const = 0;
            virtual void setSpeckleWindowSize(int speckleWindowSize) = 0;

            virtual int getSpeckleRange() const = 0;
            virtual void setSpeckleRange(int speckleRange) = 0;

            virtual int getDisp12MaxDiff() const = 0;
            virtual void setDisp12MaxDiff(int disp12MaxDiff) = 0;

        };
        //!speckle removal algorithms. These algorithms have the purpose of removing small regions
        enum {
            CV_SPECKLE_REMOVAL_ALGORITHM, CV_SPECKLE_REMOVAL_AVG_ALGORITHM
        };
        //!subpixel interpolationm methods for disparities.
        enum{
            CV_QUADRATIC_INTERPOLATION, CV_SIMETRICV_INTERPOLATION
        };
        /** @brief Class for computing stereo correspondence using the block matching algorithm, introduced and
        contributed to OpenCV by K. Konolige.
        */
        class StereoBinaryBM : public StereoMatcher
        {
        public:
            enum { PREFILTER_NORMALIZED_RESPONSE = 0,
                PREFILTER_XSOBEL              = 1
            };

            virtual int getPreFilterType() const = 0;
            virtual void setPreFilterType(int preFilterType) = 0;

            virtual int getPreFilterSize() const = 0;
            virtual void setPreFilterSize(int preFilterSize) = 0;

            virtual int getPreFilterCap() const = 0;
            virtual void setPreFilterCap(int preFilterCap) = 0;

            virtual int getTextureThreshold() const = 0;
            virtual void setTextureThreshold(int textureThreshold) = 0;

            virtual int getUniquenessRatio() const = 0;
            virtual void setUniquenessRatio(int uniquenessRatio) = 0;

            virtual int getSmallerBlockSize() const = 0;
            virtual void setSmallerBlockSize(int blockSize) = 0;

            virtual int getScalleFactor() const = 0 ;
            virtual void setScalleFactor(int factor) = 0;

            virtual int getSpekleRemovalTechnique() const = 0 ;
            virtual void setSpekleRemovalTechnique(int factor) = 0;

            virtual bool getUsePrefilter() const = 0 ;
            virtual void setUsePrefilter(bool factor) = 0;

            virtual int getBinaryKernelType() const = 0;
            virtual void setBinaryKernelType(int value) = 0;

            virtual int getAgregationWindowSize() const = 0;
            virtual void setAgregationWindowSize(int value) = 0;
            /** @brief Creates StereoBM object

            @param numDisparities the disparity search range. For each pixel algorithm will find the best
            disparity from 0 (default minimum disparity) to numDisparities. The search range can then be
            shifted by changing the minimum disparity.
            @param blockSize the linear size of the blocks compared by the algorithm. The size should be odd
            (as the block is centered at the current pixel). Larger block size implies smoother, though less
            accurate disparity map. Smaller block size gives more detailed disparity map, but there is higher
            chance for algorithm to find a wrong correspondence.

            The function create StereoBM object. You can then call StereoBM::compute() to compute disparity for
            a specific stereo pair.
            */
            CV_EXPORTS static Ptr< cv::stereo::StereoBinaryBM > create(int numDisparities = 0, int blockSize = 9);
        };

        /** @brief The class implements the modified H. Hirschmuller algorithm @cite HH08 that differs from the original
        one as follows:

        -   By default, the algorithm is single-pass, which means that you consider only 5 directions
        instead of 8. Set mode=StereoSGBM::MODE_HH in createStereoSGBM to run the full variant of the
        algorithm but beware that it may consume a lot of memory.
        -   The algorithm matches blocks, not individual pixels. Though, setting blockSize=1 reduces the
        blocks to single pixels.
        -   Mutual information cost function is not implemented. Instead, a simpler Birchfield-Tomasi
        sub-pixel metric from @cite BT98 is used. Though, the color images are supported as well.
        -   Some pre- and post- processing steps from K. Konolige algorithm StereoBM are included, for
        example: pre-filtering (StereoBM::PREFILTER_XSOBEL type) and post-filtering (uniqueness
        check, quadratic interpolation and speckle filtering).

        @note
        -   (Python) An example illustrating the use of the StereoSGBM matching algorithm can be found
        at opencv_source_code/samples/python2/stereo_match.py
        */
        class StereoBinarySGBM : public StereoMatcher
        {
        public:
            enum
            {
                MODE_SGBM = 0,
                MODE_HH   = 1
            };

            virtual int getPreFilterCap() const = 0;
            virtual void setPreFilterCap(int preFilterCap) = 0;

            virtual int getUniquenessRatio() const = 0;
            virtual void setUniquenessRatio(int uniquenessRatio) = 0;

            virtual int getP1() const = 0;
            virtual void setP1(int P1) = 0;

            virtual int getP2() const = 0;
            virtual void setP2(int P2) = 0;

            virtual int getMode() const = 0;
            virtual void setMode(int mode) = 0;

            virtual int getSpekleRemovalTechnique() const = 0 ;
            virtual void setSpekleRemovalTechnique(int factor) = 0;

            virtual int getBinaryKernelType() const = 0;
            virtual void setBinaryKernelType(int value) = 0;

            virtual int getSubPixelInterpolationMethod() const = 0;
            virtual void setSubPixelInterpolationMethod(int value) = 0;

            /** @brief Creates StereoSGBM object

            @param minDisparity Minimum possible disparity value. Normally, it is zero but sometimes
            rectification algorithms can shift images, so this parameter needs to be adjusted accordingly.
            @param numDisparities Maximum disparity minus minimum disparity. The value is always greater than
            zero. In the current implementation, this parameter must be divisible by 16.
            @param blockSize Matched block size. It must be an odd number \>=1 . Normally, it should be
            somewhere in the 3..11 range.
            @param P1 The first parameter controlling the disparity smoothness.This parameter is used for the case of slanted surfaces (not fronto parallel).
            @param P2 The second parameter controlling the disparity smoothness.This parameter is used for "solving" the depth discontinuities problem.
            The larger the values are, the smoother the disparity is. P1 is the penalty on the disparity change by plus or minus 1
            between neighbor pixels. P2 is the penalty on the disparity change by more than 1 between neighbor
            pixels. The algorithm requires P2 \> P1 . See stereo_match.cpp sample where some reasonably good
            P1 and P2 values are shown (like 8\*number_of_image_channels\*SADWindowSize\*SADWindowSize and
            32\*number_of_image_channels\*SADWindowSize\*SADWindowSize , respectively).
            @param disp12MaxDiff Maximum allowed difference (in integer pixel units) in the left-right
            disparity check. Set it to a non-positive value to disable the check.
            @param preFilterCap Truncation value for the prefiltered image pixels. The algorithm first
            computes x-derivative at each pixel and clips its value by [-preFilterCap, preFilterCap] interval.
            The result values are passed to the Birchfield-Tomasi pixel cost function.
            @param uniquenessRatio Margin in percentage by which the best (minimum) computed cost function
            value should "win" the second best value to consider the found match correct. Normally, a value
            within the 5-15 range is good enough.
            @param speckleWindowSize Maximum size of smooth disparity regions to consider their noise speckles
            and invalidate. Set it to 0 to disable speckle filtering. Otherwise, set it somewhere in the
            50-200 range.
            @param speckleRange Maximum disparity variation within each connected component. If you do speckle
            filtering, set the parameter to a positive value, it will be implicitly multiplied by 16.
            Normally, 1 or 2 is good enough.
            @param mode Set it to StereoSGBM::MODE_HH to run the full-scale two-pass dynamic programming
            algorithm. It will consume O(W\*H\*numDisparities) bytes, which is large for 640x480 stereo and
            huge for HD-size pictures. By default, it is set to false .

            The first constructor initializes StereoSGBM with all the default parameters. So, you only have to
            set StereoSGBM::numDisparities at minimum. The second constructor enables you to set each parameter
            to a custom value.
            */
            CV_EXPORTS static Ptr<cv::stereo::StereoBinarySGBM> create(int minDisparity, int numDisparities, int blockSize,
                int P1 = 100, int P2 = 1000, int disp12MaxDiff = 1,
                int preFilterCap = 0, int uniquenessRatio = 5,
                int speckleWindowSize = 400, int speckleRange = 200,
                int mode = StereoBinarySGBM::MODE_SGBM);
        };
        //! @}
    }//stereo
} // cv

#endif